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ISPRS Int. J. Geo-Inf., Volume 7, Issue 3 (March 2018)

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Open AccessArticle Increasing the Accuracy of Crowdsourced Information on Land Cover via a Voting Procedure Weighted by Information Inferred from the Contributed Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 80; doi:10.3390/ijgi7030080
Received: 22 January 2018 / Revised: 16 February 2018 / Accepted: 21 February 2018 / Published: 25 February 2018
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Abstract
Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the
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Simple consensus methods are often used in crowdsourcing studies to label cases when data are provided by multiple contributors. A basic majority vote rule is often used. This approach weights the contributions from each contributor equally but the contributors may vary in the accuracy with which they can label cases. Here, the potential to increase the accuracy of crowdsourced data on land cover identified from satellite remote sensor images through the use of weighted voting strategies is explored. Critically, the information used to weight contributions based on the accuracy with which a contributor labels cases of a class and the relative abundance of class are inferred entirely from the contributed data only via a latent class analysis. The results show that consensus approaches do yield a classification that is more accurate than that achieved by any individual contributor. Here, the most accurate individual could classify the data with an accuracy of 73.91% while a basic consensus label derived from the data provided by all seven volunteers contributing data was 76.58%. More importantly, the results show that weighting contributions can lead to a statistically significant increase in the overall accuracy to 80.60% by ignoring the contributions from the volunteer adjudged to be the least accurate in labelling. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Open AccessArticle A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition
ISPRS Int. J. Geo-Inf. 2018, 7(3), 81; doi:10.3390/ijgi7030081
Received: 2 December 2017 / Revised: 31 January 2018 / Accepted: 1 February 2018 / Published: 27 February 2018
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Abstract
The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several
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The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct. Full article
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Open AccessArticle A Novel Approach to Site Selection: Collaborative Multi-Criteria Decision Making through Geo-Social Network (Case Study: Public Parking)
ISPRS Int. J. Geo-Inf. 2018, 7(3), 82; doi:10.3390/ijgi7030082
Received: 29 November 2017 / Revised: 9 February 2018 / Accepted: 17 February 2018 / Published: 1 March 2018
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Abstract
There are many potential factors that are involved in the decision making process of site selection, which makes it a challenging issue. This paper addresses the collaborative decision making concept through a geo-social network to predict site selection for public parking in Tehran,
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There are many potential factors that are involved in the decision making process of site selection, which makes it a challenging issue. This paper addresses the collaborative decision making concept through a geo-social network to predict site selection for public parking in Tehran, Iran. The presented approach utilized the analytic hierarchy process (AHP) as a multi-criteria decision method (MCDM) for weighting the criteria, which was completed in two stages; once by 50 experts, and then by three different levels of users, including 50 experts, 25 urban managers, and 150 pubic citizens, with respect to the case study area. The fuzzy majority method aggregates the archived results of AHP to determine the preferred locations that are suitable for public parking. The proposed method was implemented using a telegram bot platform. Two main advantages of the collaborative decision making scenario for public urban site selection are the fair distribution of the selected locations and the high satisfaction of users, which increased from 65% to 85%. This study presents an application for site selection based on multi-criteria decision making in a geo-social network context. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle A Spatial Analysis of the Relationship between Vegetation and Poverty
ISPRS Int. J. Geo-Inf. 2018, 7(3), 83; doi:10.3390/ijgi7030083
Received: 11 January 2018 / Revised: 5 February 2018 / Accepted: 18 February 2018 / Published: 1 March 2018
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Abstract
The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cities with a population ≥250,000 for 16
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The goal of this paper was to investigate poverty and inequities that are associated with vegetation. First, we performed a pixel-level linear regression on time-series and Normalized Difference Vegetation Index (NDVI) for 72 United States (U.S.) cities with a population ≥250,000 for 16 years (1990, 1991, 1995, 1996, 1997, 1998, and 2001 to 2010) using Advanced Very High Resolution Radiometer 1-kilometer (1-km). Second, from the pixel-level regression, we selected five U.S. cities (Shrinking: Chicago, Detroit, Philadelphia, and Growing: Dallas and Tucson) that were one standard deviation above the overall r-squared mean and one standard deviation below the overall r-squared mean to show cities that were different from the typical cities. Finally, we used spatial statistics to investigate the relationship between census tract level data (i.e., poverty, population, and race) and vegetation for 2010, based on the 1-km grid cells using Ordinary Least Squares Regression and Geographically Weighted Regression. Our results revealed poverty related areas were significantly correlated with positive high and/or negative high vegetation in both shrinking and growing cities. This paper makes a contribution to the academic body of knowledge on U.S. urban shrinking and growing cities by using a comparative analysis with global and local spatial statistics to understand the relationship between vegetation and socioeconomic inequality. Full article
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Open AccessArticle Generative Street Addresses from Satellite Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(3), 84; doi:10.3390/ijgi7030084
Received: 9 January 2018 / Revised: 13 February 2018 / Accepted: 17 February 2018 / Published: 8 March 2018
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Abstract
We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude
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We describe our automatic generative algorithm to create street addresses from satellite images by learning and labeling roads, regions, and address cells. Currently, 75% of the world’s roads lack adequate street addressing systems. Recent geocoding initiatives tend to convert pure latitude and longitude information into a memorable form for unknown areas. However, settlements are identified by streets, and such addressing schemes are not coherent with the road topology. Instead, we propose a generative address design that maps the globe in accordance with streets. Our algorithm starts with extracting roads from satellite imagery by utilizing deep learning. Then, it uniquely labels the regions, roads, and structures using some graph- and proximity-based algorithms. We also extend our addressing scheme to (i) cover inaccessible areas following similar design principles; (ii) be inclusive and flexible for changes on the ground; and (iii) lead as a pioneer for a unified street-based global geodatabase. We present our results on an example of a developed city and multiple undeveloped cities. We also compare productivity on the basis of current ad hoc and new complete addresses. We conclude by contrasting our generative addresses to current industrial and open solutions. Full article
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
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Open AccessArticle Assessment of Multiple GNSS Real-Time SSR Products from Different Analysis Centers
ISPRS Int. J. Geo-Inf. 2018, 7(3), 85; doi:10.3390/ijgi7030085
Received: 15 January 2018 / Revised: 24 February 2018 / Accepted: 7 March 2018 / Published: 8 March 2018
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Abstract
The real-time State Space Representation (SSR) product of the GNSS (Global Navigation Satellite System) orbit and clock is one of the most essential corrections for real-time precise point positioning (PPP). In this work, the performance of current SSR products from eight analysis centers
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The real-time State Space Representation (SSR) product of the GNSS (Global Navigation Satellite System) orbit and clock is one of the most essential corrections for real-time precise point positioning (PPP). In this work, the performance of current SSR products from eight analysis centers were assessed by comparing it with the final product and the accuracy of real-time PPP. Numerical results showed that (1) the accuracies of the GPS SSR product were better than 8 cm for the satellite orbit and 0.3 ns for the satellite clock; (2) the accuracies of the GLONASS (GLObalnaya NAvigatsionnaya Sputnikovaya Sistema) SSR product were better than 10 cm for orbit RMS (Root Mean Square) and 0.6 ns for clock STD (Standard Deviation); and (3) the accuracies of the BDS (BeiDou Navigation Satellite System) and Galileo SSR products from CLK93 were about 14.54 and 4.42 cm for the orbit RMS and 0.32 and 0.18 ns for the clock STD, respectively. The simulated kinematic PPP results obtained using the SSR products from CLK93 and CLK51 performed better than those using other SSR products; and the accuracy of PPP based on all products was better than 6 and 10 cm in the horizontal and vertical directions, respectively. The real-time kinematic PPP experiment carried out in Beijing, Tianjin, and Shijiazhuang, China indicated that the SSR product CLK93 from Centre National d’Etudes Spatiales (CNES) had a better performance than CAS01. Moreover, the PPP with GPS + BDS dual systems had a higher accuracy than those with only a GPS single system. Full article
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Open AccessArticle Short-Term and Long-Term Forecasting for the 3D Point Position Changing by Using Artificial Neural Networks
ISPRS Int. J. Geo-Inf. 2018, 7(3), 86; doi:10.3390/ijgi7030086
Received: 13 January 2018 / Revised: 21 February 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
Forecasting is one of the most growing areas in most sciences attracting the attention of many researchers for more extensive study. Therefore, the goal of this study is to develop an integrated forecasting methodology based on an Artificial Neural Network (ANN), which is
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Forecasting is one of the most growing areas in most sciences attracting the attention of many researchers for more extensive study. Therefore, the goal of this study is to develop an integrated forecasting methodology based on an Artificial Neural Network (ANN), which is a modern and attractive intelligent technique. The final result is to provide short-term and long-term forecasts for point position changing, i.e., the displacement or deformation of the surface they belong to. The motivation was the combination of two thoughts, the insertion of the forecasting concept in Geodesy as in the most scientific disciplines (e.g., Economics, Medicine) and the desire to know the future position of any point on a construction or on the earth’s crustal. This methodology was designed to be accurate, stable and general for different kind of geodetic data. The basic procedure consists of the definition of the forecasting problem, the preliminary data analysis (data pre-processing), the definition of the most suitable ANN, its evaluation using the proper criteria and finally the production of forecasts. The methodology gives particular emphasis on the stages of the pre-processing and the evaluation. Additionally, the importance of the prediction intervals (PI) is emphasized. A case study, which includes geodetic data from the year 2003 to the year 2016—namely X, Y, Z coordinates—is implemented. The data were acquired by 1000 permanent Global Navigation Satellite System (GNSS) stations. During this case study, 2016 ANNs—with different hyper-parameters—are trained and tested for short-term forecasting and 2016 for long-term forecasting, for each of the GNSS stations. In addition, other conventional statistical forecasting methods are used for the same purpose using the same data set. Finally the most appropriate Non-linear Autoregressive Recurrent network (NAR) or Non-linear Autoregressive with eXogenous inputs (NARX) for the forecasting of 3D point position changing is presented and evaluated. It is proved that the use of ANNs, in order to make short-term and long-term forecasts, provides forecasting changes of the order of 2 mm with Mean Absolute Error (MAE) of the order of 0.5 mm. Full article
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Open AccessArticle Land Consolidation Suitability Ranking of Cadastral Municipalities: Information-Based Decision-Making Using Multi-Criteria Analyses of Official Registers’ Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 87; doi:10.3390/ijgi7030087
Received: 5 February 2018 / Revised: 28 February 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
Fragmented agricultural land raises the costs of agricultural production. The land fragmentation manifests as a large number of relatively small and spatially divided land parcels of each owner. Additionally, the parcels are often very irregular in shape, which hinders an effective application of
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Fragmented agricultural land raises the costs of agricultural production. The land fragmentation manifests as a large number of relatively small and spatially divided land parcels of each owner. Additionally, the parcels are often very irregular in shape, which hinders an effective application of modern agricultural machinery. A land consolidation procedure, i.e., regrouping and merging partitioned agricultural land into larger and more regular parcels, and simultaneously arranging road and canal networks, enables a significant improvement in the conditions of agricultural production. The basis for conducting land consolidation is the legal framework. Multi-annual and annual plans are to specify priority areas for conducting consolidation. These plans should take into consideration the costs and benefits of land consolidation. To ascertain this, it is necessary to determine areas suitable for consolidation and express their qualitative features in a quantitative manner. The aim of this paper is to explore possibilities of using the official registers’ data to broad selection of land consolidation priority areas. To rank the chosen spatial units, various indicators have been selected and calculated at the state level. Multi-criteria analyses are commonly used as a tool for selection of the optimal solution scenario, using possibly conflicting indicators and measures. The paper used three different multi-criteria methods to determine Cadastral municipalities rankings. These rankings could be used by national agricultural or other spatial planning agencies to increase transparency and effectiveness through information-based decision making. Full article
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Open AccessArticle Measuring the Spatial Relationship Information of Multi-Layered Vector Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 88; doi:10.3390/ijgi7030088
Received: 22 January 2018 / Revised: 3 March 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
Geospatial data is a carrier of information that represents the geography of the real world. Measuring the information contents of geospatial data is always a hot topic in spatial-information science. As the main type of geospatial data, spatial vector data models provide an
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Geospatial data is a carrier of information that represents the geography of the real world. Measuring the information contents of geospatial data is always a hot topic in spatial-information science. As the main type of geospatial data, spatial vector data models provide an effective framework for encoding spatial relationships and manipulating spatial data. In particular, the spatial relationship information of vector data is a complicated problem but meaningful to help human beings evaluate the complexity of spatial data and thus guide further analysis. However, existing measures of spatial information usually focus on the ‘disjointed’ relationship in one layer and cannot cover the various spatial relationships within the multi-layered structure of vector data. In this study, a new method is proposed to measure the spatial relationship information of multi-layered vector data. The proposed method focuses on spatial distance and topological relationships and provides quantitative measurements by extending the basic thought of Shannon’s entropy. The influence of any vector feature is modeled by introducing the concept of the energy field, and the energy distribution of one layer is described by an energy map and a weight map. An operational process is also proposed to measure the overall information content. Two experiments are conducted to validate the proposed method. In the experiment with real-life data, the proposed method shows the efficiency of the quantification of spatial relationship information under a multi-layered structure. In another experiment with simulated data, the characteristics and advantages of our method are demonstrated through a comparison with classical measurements. Full article
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Open AccessArticle Geographic Information Retrieval Method for Geography Mark-Up Language Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 89; doi:10.3390/ijgi7030089
Received: 27 November 2017 / Revised: 11 February 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
Geography Mark-up Language (GML) is the geographic information coding specification based on the Extensible Markup Language (XML) technology, which was developed by the Open GIS Consortium (OGC). GML expresses spatial and non-spatial attributes of geographic objects. Retrievals for traditional XML and geographic information
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Geography Mark-up Language (GML) is the geographic information coding specification based on the Extensible Markup Language (XML) technology, which was developed by the Open GIS Consortium (OGC). GML expresses spatial and non-spatial attributes of geographic objects. Retrievals for traditional XML and geographic information have some limitations with respect to GML data, such as mismatching of the retrieval model, a single search form, and low retrieval quality. Based on analysis of the attributes, spatial relations, and structural features of GML data, this paper takes GML data elements as retrieval units and summarizes the GML retrieval mode. Then, the GML retrieval mode is constructed and formalized. On this basis, the GML Geographic Information Retrieval (GML_GIR) model is presented. The method implements the construction of a comprehensive index and the relative ordering of retrieval results by means of Lucene, an open-source full-text retrieval framework, and its components. For different features of GML data, corresponding relevance calculations are proposed. This study designs several different retrieval forms for GML data and simplifies the process of user information acquisitions. It provides reference methods for exploring geographical information retrieval based on semi-structured data represented by GML. Experimental results showed the efficiency and accuracy of the retrieval method. Full article
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Open AccessArticle Similarity Measurement of Metadata of Geospatial Data: An Artificial Neural Network Approach
ISPRS Int. J. Geo-Inf. 2018, 7(3), 90; doi:10.3390/ijgi7030090
Received: 24 December 2017 / Revised: 25 February 2018 / Accepted: 7 March 2018 / Published: 9 March 2018
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Abstract
To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or
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To help users discover the most relevant spatial datasets in the ever-growing global spatial data infrastructures (SDIs), a number of similarity measures of geospatial data based on metadata have been proposed. Researchers have assessed the similarity of geospatial data according to one or more characteristics of the geospatial data. They created different similarity algorithms for each of the selected characteristics and then combined these elementary similarities to the overall similarity of the geospatial data. The existing combination methods are mainly linear and may not be the most accurate. This paper reports our experiences in attempting to learn the optimal non-linear similarity integration functions, from the knowledge of experts, using an artificial neural network. First, a multiple-layer feed forward neural network (MLFFN) was created. Then, the intrinsic characteristics were used to represent the metadata of geospatial data and the similarity algorithms for each of the intrinsic characteristics were built. The training and evaluation data of MLFFN were derived from the knowledge of domain experts. Finally, the MLFFN was trained, evaluated, and compared with traditional linear combination methods, which was mainly a weighted sum. The results show that our method outperformed the existing methods in terms of precision. Moreover, we found that the combination of elementary similarities of experts to the overall similarity of geospatial data was not linear. Full article
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Open AccessArticle Evaluating the Societal Impact of Using Drones to Support Urban Upgrading Projects
ISPRS Int. J. Geo-Inf. 2018, 7(3), 91; doi:10.3390/ijgi7030091
Received: 29 January 2018 / Revised: 23 February 2018 / Accepted: 7 March 2018 / Published: 10 March 2018
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Abstract
Unmanned Aerial Vehicles (UAVs), or drones, have been gaining enormous popularity for many applications including informal settlement upgrading. Although UAVs can be used to efficiently collect highly detailed geospatial information, there are concerns regarding the ethical implications of its usage and the potential
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Unmanned Aerial Vehicles (UAVs), or drones, have been gaining enormous popularity for many applications including informal settlement upgrading. Although UAVs can be used to efficiently collect highly detailed geospatial information, there are concerns regarding the ethical implications of its usage and the potential misuse of data. The aim of this study is therefore to evaluate the societal impacts of using UAVs for informal settlement mapping through two case studies in Eastern Africa. We discuss how the geospatial information they provide is beneficial from a technical perspective and analyze how the use of UAVs can be aligned with the values of: participation, empowerment, accountability, transparency, and equity. The local concept of privacy is investigated by asking citizens of the informal settlements to identify objects appearing in UAV images which they consider to be sensitive or private. As such, our research is an explicit example of how to increase citizen participation in the discussion of geospatial data security and privacy issues over urban areas and provides a framework of strategies illustrating how such issues can be addressed. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle Augmented Virtuality for Coastal Management: A Holistic Use of In Situ and Remote Sensing for Large Scale Definition of Coastal Dynamics
ISPRS Int. J. Geo-Inf. 2018, 7(3), 92; doi:10.3390/ijgi7030092
Received: 10 January 2018 / Revised: 25 February 2018 / Accepted: 7 March 2018 / Published: 11 March 2018
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Abstract
In this paper, the authors describe the architecture of a multidisciplinary data acquisition and visualization platform devoted to the management of coastal environments. The platform integrates heterogeneous data acquisition sub-systems that can be roughly divided into two main categories: remote sensing systems and
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In this paper, the authors describe the architecture of a multidisciplinary data acquisition and visualization platform devoted to the management of coastal environments. The platform integrates heterogeneous data acquisition sub-systems that can be roughly divided into two main categories: remote sensing systems and in situ sensing systems. Remote sensing solutions that are going to be implemented include aerial and underwater data acquisition while in situ sensing solutions include the use of Radio Frequency IDentification (RFID) tracers, Wireless Sensor Networks and imaging techniques. All the data collected by these subsystems are stored, integrated and fused on a single platform that is also in charge of data visualization and analysis. This last task is carried out according to the paradigm of Augmented Virtuality that foresees the augmentation of a virtually reconstructed environment with data collected in the real world. The described solution proposes a novel holistic approach where different disciplines concur, with different data acquisition techniques, to a large scale definition of coastal dynamics, in order to better describe and face the coastal erosion phenomenon. The overall framework has been conceived by the so-called Team COSTE, a joint research team between the Universities of Pisa, Siena and Florence. Full article
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Open AccessArticle Evaluation of Close-Range Photogrammetry Image Collection Methods for Estimating Tree Diameters
ISPRS Int. J. Geo-Inf. 2018, 7(3), 93; doi:10.3390/ijgi7030093
Received: 18 January 2018 / Revised: 22 February 2018 / Accepted: 7 March 2018 / Published: 11 March 2018
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Abstract
The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For
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The potential of close-range photogrammetry (CRP) to compete with terrestrial laser scanning (TLS) to produce dense and accurate point clouds has increased in recent years. The use of CRP for estimating tree diameter at breast height (DBH) has multiple advantages over TLS. For example, point clouds from CRP are similar to TLS, but hardware costs are significantly lower. However, a number of data collection issues need to be clarified before the use of CRP in forested areas is considered effective. In this paper we focused on different CRP data collection methods to estimate DBH. We present seven methods that differ in camera orientation, shooting mode, data collection path, and other important factors. The methods were tested on a research plot comprised of European beeches (Fagus sylvatica L.). The circle-fitting algorithm was used to estimate DBH. Four of the seven methods were capable of producing a dense point cloud. The tree detection rate varied from 49% to 81%. Estimates of DBH produced a root mean square error that varied from 4.41 cm to 5.98 cm. The most accurate method was achieved using a vertical camera orientation, stop-and-go shooting mode, and a path leading around the plot with two diagonal paths through the plot. This method also had the highest rate of tree detection (81%). Full article
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Open AccessArticle A Distance-Adaptive Refueling Recommendation Algorithm for Self-Driving Travel
ISPRS Int. J. Geo-Inf. 2018, 7(3), 94; doi:10.3390/ijgi7030094
Received: 12 December 2017 / Revised: 2 March 2018 / Accepted: 7 March 2018 / Published: 12 March 2018
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Abstract
Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were
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Taking the maximum vehicle driving distance, the distances from gas stations, the route length, and the number of refueling gas stations as the decision conditions, recommendation rules and an early refueling service warning mechanism for gas stations along a self-driving travel route were constructed by using the algorithm presented in this research, based on the spatial clustering characteristics of gas stations and the urgency of refueling. Meanwhile, by combining ArcEngine and Matlab capabilities, a scenario simulation system of refueling for self-driving travel was developed by using c#.net in order to validate and test the accuracy and applicability of the algorithm. A total of nine testing schemes with four simulation scenarios were designed and executed using this algorithm, and all of the simulation results were consistent with expectations. The refueling recommendation algorithm proposed in this study can automatically adapt to changes in the route length of self-driving travel, the maximum driving distance of the vehicle, and the distance from gas stations, which could provide variable refueling recommendation strategies according to differing gas station layouts along the route. Therefore, the results of this study could provide a scientific reference for the reasonable planning and timely supply of vehicle refueling during self-driving travel. Full article
(This article belongs to the Special Issue Mapping for Autonomous Vehicles)
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Open AccessArticle Improving the Separability of Deep Features with Discriminative Convolution Filters for RSI Classification
ISPRS Int. J. Geo-Inf. 2018, 7(3), 95; doi:10.3390/ijgi7030095
Received: 31 December 2017 / Revised: 5 March 2018 / Accepted: 7 March 2018 / Published: 12 March 2018
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Abstract
The extraction of activation vectors (or deep features) from the fully connected layers of a convolutional neural network (CNN) model is widely used for remote sensing image (RSI) representation. In this study, we propose to learn discriminative convolution filter (DCF) based on class-specific
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The extraction of activation vectors (or deep features) from the fully connected layers of a convolutional neural network (CNN) model is widely used for remote sensing image (RSI) representation. In this study, we propose to learn discriminative convolution filter (DCF) based on class-specific separability criteria for linear transformation of deep features. In particular, two types of pretrained CNN called CaffeNet and VGG-VD16 are introduced to illustrate the generality of the proposed DCF. The activation vectors extracted from the fully connected layers of a CNN are rearranged into the form of an image matrix, from which a spatial arrangement of local patches is extracted using sliding window strategy. DCF learning is then performed on each local patch individually to obtain the corresponding discriminative convolution kernel through generalized eigenvalue decomposition. The proposed DCF learning characterizes that a convolutional kernel with small size (e.g., 3 × 3 pixels) can be effectively learned on a small-size local patch (e.g., 8 × 8 pixels), thereby ensuring that the linear transformation of deep features can maintain low computational complexity. Experiments on two RSI datasets demonstrate the effectiveness of DCF in improving the classification performances of deep features without increasing dimensionality. Full article
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Open AccessArticle Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part I: Overview and Key Issues
ISPRS Int. J. Geo-Inf. 2018, 7(3), 97; doi:10.3390/ijgi7030097
Received: 24 January 2018 / Revised: 1 March 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
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Abstract
Cesium Markup Language (CZML) is an emerging specification for the representation and exchange of time-dynamic geospatial objects on virtual globes. The principal focus of CZML is on the definition of time-varying characteristics that are important for applications of geospatial objects, such as changeable
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Cesium Markup Language (CZML) is an emerging specification for the representation and exchange of time-dynamic geospatial objects on virtual globes. The principal focus of CZML is on the definition of time-varying characteristics that are important for applications of geospatial objects, such as changeable positions/extents, graphical appearances, and other geospatial properties. Due to its unique ability to stream massive geospatial datasets, CZML is ideally suited for efficient, incremental streaming to the client in the network environment. Our goal is to explore and outline the overall perspective of CZML as an efficient schema for representing time-dynamic geospatial objects on virtual globes. Such a perspective is the topic of the two present companion papers. Here, in the first part, we provide an overview of CZML and explore two key issues, and their associated solutions, for representing time-dynamic geospatial objects using CZML: one is how to use CZML properties to describe time-varying characteristics of geospatial objects, and the other is how to use CZML to support streaming data. These innovative improvements provide highly-efficient and more reliable supports for representing time-dynamic geospatial objects. The relevant applications, academic influence, and future developments of CZML are explored in a second paper. Full article
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Open AccessArticle An Approach to Measuring Semantic Relatedness of Geographic Terminologies Using a Thesaurus and Lexical Database Sources
ISPRS Int. J. Geo-Inf. 2018, 7(3), 98; doi:10.3390/ijgi7030098
Received: 12 December 2017 / Revised: 2 March 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
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Abstract
In geographic information science, semantic relatedness is important for Geographic Information Retrieval (GIR), Linked Geospatial Data, geoparsing, and geo-semantics. But computing the semantic similarity/relatedness of geographic terminology is still an urgent issue to tackle. The thesaurus is a ubiquitous and sophisticated knowledge representation
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In geographic information science, semantic relatedness is important for Geographic Information Retrieval (GIR), Linked Geospatial Data, geoparsing, and geo-semantics. But computing the semantic similarity/relatedness of geographic terminology is still an urgent issue to tackle. The thesaurus is a ubiquitous and sophisticated knowledge representation tool existing in various domains. In this article, we combined the generic lexical database (WordNet or HowNet) with the Thesaurus for Geographic Science and proposed a thesaurus–lexical relatedness measure (TLRM) to compute the semantic relatedness of geographic terminology. This measure quantified the relationship between terminologies, interlinked the discrete term trees by using the generic lexical database, and realized the semantic relatedness computation of any two terminologies in the thesaurus. The TLRM was evaluated on a new relatedness baseline, namely, the Geo-Terminology Relatedness Dataset (GTRD) which was built by us, and the TLRM obtained a relatively high cognitive plausibility. Finally, we applied the TLRM on a geospatial data sharing portal to support data retrieval. The application results of the 30 most frequently used queries of the portal demonstrated that using TLRM could improve the recall of geospatial data retrieval in most situations and rank the retrieval results by the matching scores between the query of users and the geospatial dataset. Full article
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Open AccessArticle Extraction of Tourist Destinations and Comparative Analysis of Preferences Between Foreign Tourists and Domestic Tourists on the Basis of Geotagged Social Media Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 99; doi:10.3390/ijgi7030099
Received: 29 January 2018 / Revised: 28 February 2018 / Accepted: 12 March 2018 / Published: 13 March 2018
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Abstract
Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country
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Inbound tourism plays an important role in local economies. To stimulate local economies, it is necessary to attract foreign tourists to various areas of a country. This research aims to develop a method of extracting the locations of tourist destinations in a country and to understand what characteristics foreign tourists expect of areas near tourist attractions compared with what domestic tourists expect. In this paper, a tourist destination is defined as a small area that has places of interests for tourists such as historic sites, theme parks, hotels, and restaurants. The methods proposed in this paper are applied to data acquired from Twitter and Foursquare in Japan. The proposed method successfully extracts the locations of tourist destinations and characterizes those locations based on the points of interest in the neighborhood. The results indicate that foreign tourists who come to Japan expect nightlife spots (bars, nightclubs, etc.) to be located in the neighborhood of tourist destinations, in contrast to the expectations of domestic tourists. The proposed methods are applicable to not only Japan, but to any country. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Do Charitable Foundations Spend Money Where People Need It Most? A Spatial Analysis of China
ISPRS Int. J. Geo-Inf. 2018, 7(3), 100; doi:10.3390/ijgi7030100
Received: 6 February 2018 / Revised: 8 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Charitable foundations are a critical part of public services. However, there is a large gap between the locations and expenditures of charitable foundations and the real population needs for most nations. Three types of Chinese local charity foundations, i.e., those for poverty, education
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Charitable foundations are a critical part of public services. However, there is a large gap between the locations and expenditures of charitable foundations and the real population needs for most nations. Three types of Chinese local charity foundations, i.e., those for poverty, education and medical assistance, are used as examples to explore the distinct gaps. The spatial distributions of local charity foundations are characterized by spatial scan statistics and spatial autocorrelation models. The local population needs of charitable assistance for poverty, education and medical services are quantified with their respective weighted proxy indexes of the current conditions. Thus, the nonlinear relationships between population needs and the expenditures of local charitable foundations are described with generalized additive models. The results show that both the participation rate and the charity expenditures of the foundations are highly clustered within a few cities where the population needs are relatively small and are furthermore rare among the other cities. The charity expenditures of local foundations are nonlinearly correlated with the current conditions of socioeconomic development, education and medical levels due to the diverse development stages of the cities. This study provides quantitative evidence for local authorities and charitable foundations to make targeted and constructive decisions to gradually reduce the distinct gaps. Full article
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Open AccessArticle Investigating the Influences of Tree Coverage and Road Density on Property Crime
ISPRS Int. J. Geo-Inf. 2018, 7(3), 101; doi:10.3390/ijgi7030101
Received: 22 December 2017 / Revised: 18 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a
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With the development of Geographic Information Systems (GIS), crime mapping has become an effective approach for investigating the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment reveals possible strategies for reducing crime in a neighborhood. The relationship between vegetation density and crime has long been under debate. The convenience of a road network is another important factor that can influence a criminal’s selection of locations. This research is conducted to investigate the correlations between tree coverage and property crime, and road density and property crime in the City of Vancouver. High spatial resolution airborne LiDAR data and road network data collected in 2013 were used to extract tree covered areas for cross-sectional analysis. The independent variables were inserted into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their relationships to property crime rates. The results of the cross-sectional analysis provide statistical evidence that there are negative correlations between property crime rates and both tree coverage and road density, with the stronger correlations occurring around Downtown Vancouver. Full article
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Open AccessArticle Representing Time-Dynamic Geospatial Objects on Virtual Globes Using CZML—Part II: Impact, Comparison, and Future Developments
ISPRS Int. J. Geo-Inf. 2018, 7(3), 102; doi:10.3390/ijgi7030102
Received: 24 January 2018 / Revised: 1 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
This is the second and final part of our Cesium Markup Language (CZML) study. Here, we describe the relevant applications, academic influence, and future developments of CZML. Since its emergence in 2011, CZML has become widely used in the geoscientific environment. It is
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This is the second and final part of our Cesium Markup Language (CZML) study. Here, we describe the relevant applications, academic influence, and future developments of CZML. Since its emergence in 2011, CZML has become widely used in the geoscientific environment. It is also having a positive impact on geoscience. Numerous applications use CZML for generating time-dynamic geovisualization, facilitating data interoperability, and promoting spatial data infrastructures. In this paper, we give an overview of the available tools and services, representative applications, as well as the role that CZML plays for geoscientific research. Furthermore, we also discuss key similarities and differences between CZML and KML (Keyhole Markup Language), and outline some of the future improvements for CZML’s research and development. Full article
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Open AccessArticle ENSO- and Rainfall-Sensitive Vegetation Regions in Indonesia as Identified from Multi-Sensor Remote Sensing Data
ISPRS Int. J. Geo-Inf. 2018, 7(3), 103; doi:10.3390/ijgi7030103
Received: 14 December 2017 / Revised: 23 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming
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Ongoing global warming has triggered extreme climate events of increasing magnitude and frequency. Under this effect, a series of extreme climate events such as drought and increased rainfall during the El Nino Southern Oscillation (ENSO) are expected to be amplified in the coming years. Adequate mapping of regions with climate-sensitive vegetation and its associated time lag is required for appropriate mitigation planning to avoid potential negative ecological impacts towards vegetation. In this study, ENSO and climate indicator time series data, for example, Multivariate ENSO Index (MEI) and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) data for rainfall were linked with long-term time series vegetation proxies from remote sensing (RS proxies). ENSO- and rainfall-sensitive areas were identified from each RS proxy using the bivariate Granger test, and the areas identified by multiple RS proxies were taken to identify climate-sensitive regions in Indonesia. Of the biome types in Indonesia, savanna was the most sensitive, with approximately 53% of the total savanna area in Indonesia shown to be sensitive to ENSO and rainfall by two or more RS proxies. Rolling correlation analysis also found that the ENSO effect on the vegetation region after rainfall was positively correlated with the RS proxies with a time lag of +5 months. Therefore, rainfall can be taken as a proxy of the effects of ENSO on the temporal dynamics of sensitive vegetation regions in Indonesia. Full article
(This article belongs to the Special Issue Earth/Community Observations for Climate Change Research)
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Open AccessArticle Impacts of Street-Visible Greenery on Housing Prices: Evidence from a Hedonic Price Model and a Massive Street View Image Dataset in Beijing
ISPRS Int. J. Geo-Inf. 2018, 7(3), 104; doi:10.3390/ijgi7030104
Received: 9 February 2018 / Revised: 4 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Street greenery is a component of urban green infrastructure. By forming foundational green corridors in urban ecological systems, street greenery provides vital ecological, social, and cultural functions, and benefits the wellbeing of citizens. However, because of the difficulty of quantifying people’s visual perceptions,
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Street greenery is a component of urban green infrastructure. By forming foundational green corridors in urban ecological systems, street greenery provides vital ecological, social, and cultural functions, and benefits the wellbeing of citizens. However, because of the difficulty of quantifying people’s visual perceptions, the impact of street-visible greenery on housing prices has not been fully studied. Using Beijing, which has a mature real estate market, as an example, this study evaluated 22,331 transactions in 2014 in 2370 private housing estates. We selected 25 variables that were classified into three categories—location, housing, and neighbourhood characteristics—and introduced an index called the horizontal green view index (HGVI) into a hedonic pricing model to measure the value of the visual perception of street greenery in neighbouring residential developments. The results show that (1) Beijing’s homebuyers would like to reside in residential units with a higher HGVI; (2) Beijing’s homebuyers favour larger lakes; and (3) Beijing’s housing prices were impacted by the spatial development patterns of the city centre and multiple business centres. We used computer vision to quantify the street-visible greenery and estimated the economic benefits that the neighbouring visible greenery would have on residential developments in Beijing. This study provides a scientific basis and reference for policy makers and city planners in road greening, and a tool for formulating street greening policy, studying housing price characteristics, and evaluating real estate values. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessFeature PaperArticle Multitemporal SAR Data and 2D Hydrodynamic Model Flood Scenario Dynamics Assessment
ISPRS Int. J. Geo-Inf. 2018, 7(3), 105; doi:10.3390/ijgi7030105
Received: 29 January 2018 / Revised: 7 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
The increasing number of floods and the severity of their consequences, which is caused by phenomena, such as climate change and uncontrolled urbanization, create a growing need to develop operational procedures and tools for accurate and timely flood mapping and management. Synthetic Aperture
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The increasing number of floods and the severity of their consequences, which is caused by phenomena, such as climate change and uncontrolled urbanization, create a growing need to develop operational procedures and tools for accurate and timely flood mapping and management. Synthetic Aperture Radar (SAR), with its day, night, and cloud-penetrating capacity, has proven to be a very useful source of information during calibration of hydrodynamic models considered indispensable tools for near real-time flood forecasting and monitoring. The paper begins with the analysis of radar signatures of temporal series of SAR data, by exploiting the short revisit time of the images that are provided by the Cosmo-SkyMed constellation of four satellites, in combination with a Digital Elevation Model for the extraction of flood extent and spatially distributed water depth in a flat area with complex topography during a flood event. These SAR-based hazard maps were then used to perform a bi-dimensional hydraulic model calibration on the November 2010 flood event at the mouth of the Bradano River in Basilicata, Italy. Once the best fit between flood predictions of hydrodynamic models was identified and the efficacy of SAR data in correcting hydrodynamic inconsistencies with regard to reliable assessment of flood extent and water-depth maps was shown by validation with the December 2013 Bradano River event. Based on calibration and validation results, the paper aims to show how the combination of the time series of Synthetic Aperture Radar (SAR) and Digital Elevation Model (DEM) derived water-depth maps with the data from the hydrodynamic model can provide valuable information for flood dynamics monitoring in a flat area with complex topography. Future research should focus on the integration and implementation of the semi-automatic proposed method in an operational system for near real-time flood management. Full article
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Open AccessArticle On the Use of Geographic Information in Humanities Research Infrastructure: A Case Study on Cultural Heritage
ISPRS Int. J. Geo-Inf. 2018, 7(3), 106; doi:10.3390/ijgi7030106
Received: 14 January 2018 / Revised: 13 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
As an invaluable source of knowledge about the past, cultural heritage may be an important element of the humanities research infrastructure, along with other elements, such as spatial references. Therefore, this paper attempts to provide an answer to the questions concerning the ways
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As an invaluable source of knowledge about the past, cultural heritage may be an important element of the humanities research infrastructure, along with other elements, such as spatial references. Therefore, this paper attempts to provide an answer to the questions concerning the ways in which spatial information can contribute to the development of this infrastructure and the aspects of storytelling based on cultural resources that can be supported by such infrastructure. The objective of the methodology that was used was to combine the aspects that refer to spatial information and cultural items into a single, common issue, and to describe them in a formalized way with use of Unified Modeling Language (UML). As a result, the study presents a proposal of the Humanities Infrastructure Architecture based on spatially-oriented movable cultural items, taking into account their use in the context of interoperability, along with the concept of creating spatial databases that would include movable monuments. The authors also demonstrate that the ISO 19100 series of geographical information standards may be a source of interesting conceptual solutions that may be used in the process of the standardization of geographical information that was recorded in the descriptions of cultural heritage items in form of metadata and data structure descriptions. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessArticle Fine Resolution Probabilistic Land Cover Classification of Landscapes in the Southeastern United States
ISPRS Int. J. Geo-Inf. 2018, 7(3), 107; doi:10.3390/ijgi7030107
Received: 12 February 2018 / Revised: 8 March 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the
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Land cover classification provides valuable information for prioritizing management and conservation operations across large landscapes. Current regional scale land cover geospatial products within the United States have a spatial resolution that is too coarse to provide the necessary information for operations at the local and project scales. This paper describes a methodology that uses recent advances in spatial analysis software to create a land cover classification over a large region in the southeastern United States at a fine (1 m) spatial resolution. This methodology used image texture metrics and principle components derived from National Agriculture Imagery Program (NAIP) aerial photographic imagery, visually classified locations, and a softmax neural network model. The model efficiently produced classification surfaces at 1 m resolution across roughly 11.6 million hectares (28.8 million acres) with less than 10% average error in modeled probability. The classification surfaces consist of probability estimates of 13 visually distinct classes for each 1 m cell across the study area. This methodology and the tools used in this study constitute a highly flexible fine resolution land cover classification that can be applied across large extents using standard computer hardware, common and open source software and publicly available imagery. Full article
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Open AccessArticle Spatio-Temporal Database of Places Located in the Border Area
ISPRS Int. J. Geo-Inf. 2018, 7(3), 108; doi:10.3390/ijgi7030108
Received: 18 January 2018 / Revised: 14 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
As a result of changes in boundaries, the political affiliation of locations also changes. Data on such locations are now collected in datasets with reference to the present or to the past space. Therefore, they can refer to localities that either no longer
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As a result of changes in boundaries, the political affiliation of locations also changes. Data on such locations are now collected in datasets with reference to the present or to the past space. Therefore, they can refer to localities that either no longer exist, have a different name now, or lay outside of the current borders of the country. Moreover, thematic data describing the past are related to events, customs, items that are always “somewhere”. Storytelling about the past is incomplete without knowledge about the places in which the given story has happened. Therefore, the objective of the article is to discuss the concept of spatio-temporal database for border areas as an “engine” for visualization of thematic data in time-oriented geographical space. The paper focuses on studying the place names on the Polish-Ukrainian border, analyzing the changes that have occurred in this area over the past 80 years (where there were three different countries during this period), and defining the changeability rules. As a result of the research, the architecture of spatio-temporal databases is defined, as well as the rules for using them for data geovisualisation in historical context. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessArticle Pixel-Wise Classification Method for High Resolution Remote Sensing Imagery Using Deep Neural Networks
ISPRS Int. J. Geo-Inf. 2018, 7(3), 110; doi:10.3390/ijgi7030110
Received: 8 January 2018 / Revised: 28 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation
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Considering the classification of high spatial resolution remote sensing imagery, this paper presents a novel classification method for such imagery using deep neural networks. Deep learning methods, such as a fully convolutional network (FCN) model, achieve state-of-the-art performance in natural image semantic segmentation when provided with large-scale datasets and respective labels. To use data efficiently in the training stage, we first pre-segment training images and their labels into small patches as supplements of training data using graph-based segmentation and the selective search method. Subsequently, FCN with atrous convolution is used to perform pixel-wise classification. In the testing stage, post-processing with fully connected conditional random fields (CRFs) is used to refine results. Extensive experiments based on the Vaihingen dataset demonstrate that our method performs better than the reference state-of-the-art networks when applied to high-resolution remote sensing imagery classification. Full article
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Open AccessArticle WebGIS for Geography Education: Towards a GeoCapabilities Approach
ISPRS Int. J. Geo-Inf. 2018, 7(3), 111; doi:10.3390/ijgi7030111
Received: 11 January 2018 / Revised: 1 March 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
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Abstract
Recent developments in webGIS are transforming how geospatial information can be used in schools. Smart mapping, mobile applications, editable feature services (EFS), and web map services (WMS) are all now more freely available. These have made prior technological, cost and access challenges for
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Recent developments in webGIS are transforming how geospatial information can be used in schools. Smart mapping, mobile applications, editable feature services (EFS), and web map services (WMS) are all now more freely available. These have made prior technological, cost and access challenges for teachers largely redundant but are only part of ensuring that geospatial information is used to its full educational potential in geography education. This paper argues that drawing on a GeoCapabilities approach can enhance teacher’s use of webGIS in deepening their students’ abilities to think and reason with geographical knowledge and ideas. To illustrate this line of argument, a geography curriculum artefact constructed in ArcGIS Online is presented and analysed. The discussion identifies a range of specific educational benefits of geography teachers adopting a GeoCapabilities approach to using webGIS including how powerful disciplinary knowledge (PDK) can be constructed. The discussion also identifies a number of significant implications for teacher education of adopting such a methodology. The paper concludes with recommendations for the future use of webGIS in schools and geography teacher education. Full article
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Open AccessArticle An Indoor Scene Recognition-Based 3D Registration Mechanism for Real-Time AR-GIS Visualization in Mobile Applications
ISPRS Int. J. Geo-Inf. 2018, 7(3), 112; doi:10.3390/ijgi7030112
Received: 6 February 2018 / Revised: 10 March 2018 / Accepted: 14 March 2018 / Published: 15 March 2018
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Abstract
Mobile Augmented Reality (MAR) systems are becoming ideal platforms for visualization, permitting users to better comprehend and interact with spatial information. Subsequently, this technological development, in turn, has prompted efforts to enhance mechanisms for registering virtual objects in real world contexts. Most existing
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Mobile Augmented Reality (MAR) systems are becoming ideal platforms for visualization, permitting users to better comprehend and interact with spatial information. Subsequently, this technological development, in turn, has prompted efforts to enhance mechanisms for registering virtual objects in real world contexts. Most existing AR 3D Registration techniques lack the scene recognition capabilities needed to describe accurately the positioning of virtual objects in scenes representing reality. Moreover, the application of such registration methods in indoor AR-GIS systems is further impeded by the limited capacity of these systems to detect the geometry and semantic information in indoor environments. In this paper, we propose a novel method for fusing virtual objects and indoor scenes, based on indoor scene recognition technology. To accomplish scene fusion in AR-GIS, we first detect key points in reference images. Then, we perform interior layout extraction using a Fully Connected Networks (FCN) algorithm to acquire layout coordinate points for the tracking targets. We detect and recognize the target scene in a video frame image to track targets and estimate the camera pose. In this method, virtual 3D objects are fused precisely to a real scene, according to the camera pose and the previously extracted layout coordinate points. Our results demonstrate that this approach enables accurate fusion of virtual objects with representations of real world indoor environments. Based on this fusion technique, users can better grasp virtual three-dimensional representations on an AR-GIS platform. Full article
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Open AccessArticle OSM Data Import as an Outreach Tool to Trigger Community Growth? A Case Study in Miami
ISPRS Int. J. Geo-Inf. 2018, 7(3), 113; doi:10.3390/ijgi7030113
Received: 1 January 2018 / Revised: 26 February 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
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Abstract
This paper presents the results of a study that explored if and how an OpenStreetMap (OSM) data import task can contribute to OSM community growth. Different outreach techniques were used to introduce a building import task to three targeted OSM user groups. First,
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This paper presents the results of a study that explored if and how an OpenStreetMap (OSM) data import task can contribute to OSM community growth. Different outreach techniques were used to introduce a building import task to three targeted OSM user groups. First, existing OSM members were contacted and asked to join the data import project. Second, several local community events were organized with Maptime Miami to engage local mappers in OSM contribution activities. Third, the import task was introduced as an extra credit assignment in two GIS courses at the University of Florida. The paper analyzes spatio-temporal user contributions of these target groups to assess the effectiveness of the different outreach techniques for recruitment and retention of OSM contributors. Results suggest that the type of prospective users that were contacted through our outreach efforts, and their different motivations play a major role in their editing activity. Results also revealed differences in editing patterns between newly recruited users and already established mappers. More specifically, long-term engagement of newly registered OSM mappers did not succeed, whereas already established contributors continued to import and improve data. In general, we found that an OSM data import project can add valuable data to the map, but also that encouraging long-term engagement of new users, whether it be within the academic environment or outside, proved to be challenging. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Open AccessArticle Accuracy Assessment of Different Digital Surface Models
ISPRS Int. J. Geo-Inf. 2018, 7(3), 114; doi:10.3390/ijgi7030114
Received: 21 January 2018 / Revised: 19 February 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
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Abstract
Digital elevation models (DEMs), which can occur in the form of digital surface models (DSMs) or digital terrain models (DTMs), are widely used as important geospatial information sources for various remote sensing applications, including the precise orthorectification of high-resolution satellite images, 3D spatial
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Digital elevation models (DEMs), which can occur in the form of digital surface models (DSMs) or digital terrain models (DTMs), are widely used as important geospatial information sources for various remote sensing applications, including the precise orthorectification of high-resolution satellite images, 3D spatial analyses, multi-criteria decision support systems, and deformation monitoring. The accuracy of DEMs has direct impacts on specific calculations and process chains; therefore, it is important to select the most appropriate DEM by considering the aim, accuracy requirement, and scale of each study. In this research, DSMs obtained from a variety of satellite sensors were compared to analyze their accuracy and performance. For this purpose, freely available Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m, and Advanced Land Observing Satellite (ALOS) 30 m resolution DSM data were obtained. Additionally, 3 m and 1 m resolution DSMs were produced from tri-stereo images from the SPOT 6 and Pleiades high-resolution (PHR) 1A satellites, respectively. Elevation reference data provided by the General Command of Mapping, the national mapping agency of Turkey—produced from 30 cm spatial resolution stereo aerial photos, with a 5 m grid spacing and ±3 m or better overall vertical accuracy at the 90% confidence interval (CI)—were used to perform accuracy assessments. Gross errors and water surfaces were removed from the reference DSM. The relative accuracies of the different DSMs were tested using a different number of checkpoints determined by different methods. In the first method, 25 checkpoints were selected from bare lands to evaluate the accuracies of the DSMs on terrain surfaces. In the second method, 1000 randomly selected checkpoints were used to evaluate the methods’ accuracies for the whole study area. In addition to the control point approach, vertical cross-sections were extracted from the DSMs to evaluate the accuracies related to land cover. The PHR and SPOT DSMs had the highest accuracies of all of the testing methods, followed by the ALOS DSM, which had very promising results. Comparatively, the SRTM and ASTER DSMs had the worst accuracies. Additionally, the PHR and SPOT DSMs captured man-made objects and above-terrain structures, which indicated the need for post-processing to attain better representations. Full article
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Open AccessArticle Spatial Transformation of Equality – Generalized Travelling Salesman Problem to Travelling Salesman Problem
ISPRS Int. J. Geo-Inf. 2018, 7(3), 115; doi:10.3390/ijgi7030115
Received: 9 February 2018 / Revised: 23 February 2018 / Accepted: 23 February 2018 / Published: 15 March 2018
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Abstract
The Equality-Generalized Travelling Salesman Problem (E-GTSP), which is an extension of the Travelling Salesman Problem (TSP), is stated as follows: given groups of points within a city, like banks, supermarkets, etc., find a minimum cost Hamiltonian cycle that visits each group exactly once.
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The Equality-Generalized Travelling Salesman Problem (E-GTSP), which is an extension of the Travelling Salesman Problem (TSP), is stated as follows: given groups of points within a city, like banks, supermarkets, etc., find a minimum cost Hamiltonian cycle that visits each group exactly once. It can model many real-life combinatorial optimization scenarios more efficiently than TSP. This study presents five spatially driven search-algorithms for possible transformation of E-GTSP to TSP by considering the spatial spread of points in a given urban city. Presented algorithms are tested over 15 different cities, classified by their street-network’s fractal-dimension. Obtained results denote that the R-Search algorithm, which selects the points from each group based on their radial separation with respect to the start–end point, is the best search criterion for any E-GTSP to TSP conversion modelled for a city street network. An 8.8% length error has been reported for this algorithm. Full article
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Open AccessArticle Progressive Amalgamation of Building Clusters for Map Generalization Based on Scaling Subgroups
ISPRS Int. J. Geo-Inf. 2018, 7(3), 116; doi:10.3390/ijgi7030116
Received: 7 January 2018 / Revised: 23 February 2018 / Accepted: 13 March 2018 / Published: 15 March 2018
PDF Full-text (3054 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting
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Map generalization utilizes transformation operations to derive smaller-scale maps from larger-scale maps, and is a key procedure for the modelling and understanding of geographic space. Studies to date have largely applied a fixed tolerance to aggregate clustered buildings into a single object, resulting in the loss of details that meet cartographic constraints and may be of importance for users. This study aims to develop a method that amalgamates clustered buildings gradually without significant modification of geometry, while preserving the map details as much as possible under cartographic constraints. The amalgamation process consists of three key steps. First, individual buildings are grouped into distinct clusters by using the graph-based spatial clustering application with random forest (GSCARF) method. Second, building clusters are decomposed into scaling subgroups according to homogeneity with regard to the mean distance of subgroups. Thus, hierarchies of building clusters can be derived based on scaling subgroups. Finally, an amalgamation operation is progressively performed from the bottom-level subgroups to the top-level subgroups using the maximum distance of each subgroup as the amalgamating tolerance instead of using a fixed tolerance. As a consequence of this step, generalized intermediate scaling results are available, which can form the multi-scale representation of buildings. The experimental results show that the proposed method can generate amalgams with correct details, statistical area balance and orthogonal shape while satisfying cartographic constraints (e.g., minimum distance and minimum area). Full article
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Open AccessArticle Graph-Based Matching of Points-of-Interest from Collaborative Geo-Datasets
ISPRS Int. J. Geo-Inf. 2018, 7(3), 117; doi:10.3390/ijgi7030117
Received: 30 November 2017 / Revised: 6 February 2018 / Accepted: 12 March 2018 / Published: 15 March 2018
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Abstract
Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper,
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Several geospatial studies and applications require comprehensive semantic information from points-of-interest (POIs). However, this information is frequently dispersed across different collaborative mapping platforms. Surprisingly, there is still a research gap on the conflation of POIs from this type of geo-dataset. In this paper, we focus on the matching aspect of POI data conflation by proposing two matching strategies based on a graph whose nodes represent POIs and edges represent matching possibilities. We demonstrate how the graph is used for (1) dynamically defining the weights of the different POI similarity measures we consider; (2) tackling the issue that POIs should be left unmatched when they do not have a corresponding POI on the other dataset and (3) detecting multiple POIs from the same place in the same dataset and jointly matching these to the corresponding POI(s) from the other dataset. The strategies we propose do not require the collection of training samples or extensive parameter tuning. They were statistically compared with a “naive”, though commonly applied, matching approach considering POIs collected from OpenStreetMap and Foursquare from the city of London (England). In our experiments, we sequentially included each of our methodological suggestions in the matching procedure and each of them led to an increase in the accuracy in comparison to the previous results. Our best matching result achieved an overall accuracy of 91%, which is more than 10% higher than the accuracy achieved by the baseline method. Full article
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Open AccessArticle A Multiresolution Grid Structure Applied to Seafloor Shape Modeling
ISPRS Int. J. Geo-Inf. 2018, 7(3), 119; doi:10.3390/ijgi7030119
Received: 31 January 2018 / Revised: 27 February 2018 / Accepted: 14 March 2018 / Published: 16 March 2018
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Abstract
This paper proposes a method of creating a multiresolution depth grid containing bathymetric data describing a stretch of sea floor. The included literature review presents current solutions in the area of the creation of digital terrain models (DTMs) focusing on methods employing regular
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This paper proposes a method of creating a multiresolution depth grid containing bathymetric data describing a stretch of sea floor. The included literature review presents current solutions in the area of the creation of digital terrain models (DTMs) focusing on methods employing regular grids, with a discussion on the strong and weak points of such an approach. As a basis for the investigations, some important recommendations from the International Hydrographic Organization are provided and are related to the accuracy of created models. The authors propose a novel method of storing DTM data, involving multiresolution depth grids. The paper presents the characteristics of this method, numerical algorithms of a conversion between a regular grid and the multiresolution one, and experiments on typical seafloor surfaces. The results are discussed, focusing on the data reduction rate and the variable resolution of the grid structure. The proposed method can be applied in Geographical Information Systems, especially for the purposes of solving sea survey problems. Full article
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Open AccessArticle A Co-Citation and Cluster Analysis of Scientometrics of Geographic Information Ontology
ISPRS Int. J. Geo-Inf. 2018, 7(3), 120; doi:10.3390/ijgi7030120
Received: 14 December 2017 / Revised: 11 February 2018 / Accepted: 12 March 2018 / Published: 16 March 2018
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Abstract
Geographic information ontology represents an effective means of expressing geographic concepts and relationships between them. As an emerging field of study, it has drawn the attention of increasing numbers of scholars worldwide. In this study, both co-citation and cluster analysis methods of scientometrics
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Geographic information ontology represents an effective means of expressing geographic concepts and relationships between them. As an emerging field of study, it has drawn the attention of increasing numbers of scholars worldwide. In this study, both co-citation and cluster analysis methods of scientometrics are used to perform a comprehensive analysis of the papers on the topic of geographic information ontology indexed by the Web of Science (WoS) and published between 2001 and 2016. The results show that the history of the study of geographic information ontology can be divided roughly into three periods. Computer science and mathematics play important roles in this field of study. The International Journal of Geographical Information Science is an important periodical that provides knowledge resources for the study of geographic information ontology. The papers of Gruber TR and Guarino N are referenced most frequently, as well as that of Smith B., who formally introduced information ontology to the field of geographic information science. Providing personalized and intelligent geographic information services for users is an important focus of geographic information ontology. Full article
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Open AccessArticle Efficient Method for POI/ROI Discovery Using Flickr Geotagged Photos
ISPRS Int. J. Geo-Inf. 2018, 7(3), 121; doi:10.3390/ijgi7030121
Received: 17 January 2018 / Revised: 14 February 2018 / Accepted: 12 March 2018 / Published: 16 March 2018
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Abstract
In the era of big data, ubiquitous Flickr geotagged photos have opened a considerable opportunity for discovering valuable geographic information. Point of interest (POI) and region of interest (ROI) are significant reference data that are widely used in geospatial applications. This study aims
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In the era of big data, ubiquitous Flickr geotagged photos have opened a considerable opportunity for discovering valuable geographic information. Point of interest (POI) and region of interest (ROI) are significant reference data that are widely used in geospatial applications. This study aims to develop an efficient method for POI/ROI discovery from Flickr. Attractive footprints in photos with a local maximum that is beneficial for distinguishing clusters are first exploited. Pattern discovery is combined with a novel algorithm, the spatial overlap (SO) algorithm, and the naming and merging method is conducted for attractive footprint clustering. POI and ROI, which are derived from the peak value and range of clusters, indicate the most popular location and range for appreciating attractions. The discovered ROIs have a particular spatial overlap available which means the satisfied region of ROIs can be shared for appreciating attractions. The developed method is demonstrated in two study areas in Taiwan: Tainan and Taipei, which are the oldest and densest cities, respectively. Results show that the discovered POI/ROIs nearly match the official data in Tainan, whereas more commercial POI/ROIs are discovered in Taipei by the algorithm than official data. Meanwhile, our method can address the clustering issue in a dense area. Full article
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Open AccessArticle Single-Frequency Kinematic Performance Comparison between Galileo, GPS, and GLONASS Satellite Positioning Systems Using an MMS-Generated Trajectory as a Reference: Preliminary Results
ISPRS Int. J. Geo-Inf. 2018, 7(3), 122; doi:10.3390/ijgi7030122
Received: 24 January 2018 / Revised: 27 February 2018 / Accepted: 14 March 2018 / Published: 18 March 2018
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Abstract
The initial Galileo satellite positioning services, started on December 15, 2016, became available with a formal announcement by the European Commission. This first step toward the Galileo system Full Operational Capability (FOC) has allowed many researchers to test the new system. The aim
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The initial Galileo satellite positioning services, started on December 15, 2016, became available with a formal announcement by the European Commission. This first step toward the Galileo system Full Operational Capability (FOC) has allowed many researchers to test the new system. The aim of this paper is to illustrate the results and the conclusions of a kinematic test involving a GNSS (Global Navigation Satellite System) multi-constellation receiver able to acquire the Galileo Open Service (OS) signal. The produced outputs were compared to a reference trajectory obtained from a Mobile Mapping System (MMS) implementing integrated high-performance GPS/INS measurements. By exploiting the CUI (command user interface) of the open source library RTKLIB, a reduced operative status was simulated for GPS and GLONASS. Specifically, all the possible operative combinations were tested and, when possible, statistically assessed. This was necessary to offer a fair comparison among the tested constellations. The results, referred to the reference trajectory, show that the new European system is characterized by a better planimetric performance with respect to the other systems, whereas, from an altimetric point of view, the GPS and GLONASS systems perform better. Full article
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Open AccessArticle Storytelling in Interactive 3D Geographic Visualization Systems
ISPRS Int. J. Geo-Inf. 2018, 7(3), 123; doi:10.3390/ijgi7030123
Received: 12 January 2018 / Revised: 2 March 2018 / Accepted: 14 March 2018 / Published: 19 March 2018
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Abstract
The objective of interactive geographic maps is to provide geographic information to a large audience in a captivating and intuitive way. Storytelling helps to create exciting experiences and to explain complex or otherwise hidden relationships of geospatial data. Furthermore, interactive 3D applications offer
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The objective of interactive geographic maps is to provide geographic information to a large audience in a captivating and intuitive way. Storytelling helps to create exciting experiences and to explain complex or otherwise hidden relationships of geospatial data. Furthermore, interactive 3D applications offer a wide range of attractive elements for advanced visual story creation and offer the possibility to convey the same story in many different ways. In this paper, we discuss and analyze storytelling techniques in 3D geographic visualizations so that authors and developers working with geospatial data can use these techniques to conceptualize their visualization and interaction design. Finally, we outline two examples which apply the given concepts. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessArticle Factors Affecting the Number of Visitors in National Parks in the Czech Republic, Germany and Austria
ISPRS Int. J. Geo-Inf. 2018, 7(3), 124; doi:10.3390/ijgi7030124
Received: 22 January 2018 / Revised: 12 February 2018 / Accepted: 13 March 2018 / Published: 19 March 2018
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Abstract
In the context of national-level strategies, the importance of tourism in national parks is on the rise. The objective of this study is to investigate the relationship between the number of visitors to national parks and five variables: area, number of employees, budget,
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In the context of national-level strategies, the importance of tourism in national parks is on the rise. The objective of this study is to investigate the relationship between the number of visitors to national parks and five variables: area, number of employees, budget, average employee salary and number of researchers in 12 national parks in the Czech Republic, Germany and Austria. Analysis of factors influencing the number of visitors to national parks uses the method of retrospective analysis of the data contained in internal documents and questionnaires among managers of national parks. The number of candidate predictors is relatively high when compared with the number of observations. Due to this fact, the Gilmour method for statistical analysis is used. Statistical results represented by the parameter β2 for number of employees is −33,016 (95% CI, −50,592–−15,441) and by the parameter β3 for budget is 0.586 (95% CI, 0.295–0.878), showing that the number of visitors increases with budget, while it decreases with the number of employees. The results of this study are a useful starting point for managers in their efforts to focus on developing key areas in an appropriate way. In conclusion, results show that increasing the economic benefits accruing from national parks regional policy could aim at a qualitative upgrading of tourist services, increased marketing of the unique national park label and the promotion of a diverse regional supply base. Full article
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Open AccessArticle Digital Story Mapping to Advance Educational Atlas Design and Enable Student Engagement
ISPRS Int. J. Geo-Inf. 2018, 7(3), 125; doi:10.3390/ijgi7030125
Received: 15 January 2018 / Revised: 8 March 2018 / Accepted: 14 March 2018 / Published: 19 March 2018
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Abstract
Storytelling is recognized as a valid and important method of communicating information and knowledge gleaned from volumes of ever-accumulating data. Practices of data-driven storytelling in journalism and geovisual analytics have contributed to the development of geovisual stories; also called story maps. The benefits
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Storytelling is recognized as a valid and important method of communicating information and knowledge gleaned from volumes of ever-accumulating data. Practices of data-driven storytelling in journalism and geovisual analytics have contributed to the development of geovisual stories; also called story maps. The benefits of student-focused multi-thematic atlases and digital storytelling methods in education can also be realized in story maps. An online, interactive version of the original paper version of the Wyoming Student Atlas was developed using story mapping technology. Studies on best practices for data-driven storytelling and web map interaction were used to inform the transition of the atlas from a traditional paper format to a collection of story maps. Evaluation of the atlas story maps for educational purposes was conducted by observing students from multiple classrooms as they used the story maps in a lesson. The students and educators responded to a survey after using the story maps. Results of the survey show positive responses to the atlas story maps, including ease of use and preference over a traditional paper atlas. However, certain types of interaction with the map resulted in increased negative or uncertain responses from students concerning their perception of the atlas story maps. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessArticle Mining Individual Similarity by Assessing Interactions with Personally Significant Places from GPS Trajectories
ISPRS Int. J. Geo-Inf. 2018, 7(3), 126; doi:10.3390/ijgi7030126
Received: 29 January 2018 / Revised: 16 March 2018 / Accepted: 17 March 2018 / Published: 19 March 2018
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Abstract
Human mobility is closely associated with places. Due to advancements in GPS devices and related sensor technologies, an unprecedented amount of tracking data has been generated in recent years, thus providing a new way to investigate the interactions between individuals and places, which
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Human mobility is closely associated with places. Due to advancements in GPS devices and related sensor technologies, an unprecedented amount of tracking data has been generated in recent years, thus providing a new way to investigate the interactions between individuals and places, which are vital for depicting individuals’ characteristics. In this paper, we propose a framework for mining individual similarity based on long-term trajectory data. In contrast to most existing studies, which have focused on the sequential properties of individuals’ visits to public places, this paper emphasizes the essential role of the spatio-temporal interactions between individuals and their personally significant places. Specifically, rather than merely using public geographic databases, which include only public places and lack personal meanings, we attempt to interpret the semantics of places that are significant to individuals from the perspectives of personal behavior. Next, we propose a new individual similarity measurement that incorporates both the spatio-temporal and semantic properties of individuals’ visits to significant places. By experimenting on real-world GPS datasets, we demonstrate that our approach is more capable of distinguishing individuals and characterizing individual features than the previous methods. Additionally, we show that our approach can be used to effectively measure individual similarity and to aggregate individuals into meaningful subgroups. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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Open AccessArticle Procedural Generation of Large-Scale Forests Using a Graph-Based Neutral Landscape Model
ISPRS Int. J. Geo-Inf. 2018, 7(3), 127; doi:10.3390/ijgi7030127
Received: 7 February 2018 / Revised: 15 March 2018 / Accepted: 17 March 2018 / Published: 20 March 2018
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Abstract
Specifying the positions and attributes of plants (e.g., species, size, and height) during the procedural generation of large-scale forests in virtual geographic environments is challenging, especially when reflecting the characteristics of vegetation distributions. To address this issue, a novel graph-based neutral landscape model
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Specifying the positions and attributes of plants (e.g., species, size, and height) during the procedural generation of large-scale forests in virtual geographic environments is challenging, especially when reflecting the characteristics of vegetation distributions. To address this issue, a novel graph-based neutral landscape model (NLM) is proposed to generate forest landscapes with varying compositions and configurations. Our model integrates a set of class-level landscape metrics and generates more realistic and variable landscapes compared with existing NLMs controlled by limited global-level landscape metrics. To produce patches with particular sizes and shapes, a region adjacency graph is transformed from a cluster map that is generated based upon percolation theory; subsequently, optimal neighboring nodes in the graph are merged under restricted growth conditions from a source node. The locations of seeds are randomly placed and their species are classified according to the generated forest landscapes to obtain the final tree distributions. The results demonstrate that our method can generate realistic vegetation distributions representing different spatial patterns of species with a time efficiency that satisfies the requirements for constructing large-scale virtual forests. Full article
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Jump to: Research

Open AccessConcept Paper Once upon a Spacetime: Visual Storytelling in Cognitive and Geotemporal Information Spaces
ISPRS Int. J. Geo-Inf. 2018, 7(3), 96; doi:10.3390/ijgi7030096
Received: 24 January 2018 / Revised: 20 February 2018 / Accepted: 7 March 2018 / Published: 12 March 2018
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Abstract
Stories are an essential mode, not only of human communication—but also of thinking. This paper reflects on the internalization of stories from a cognitive perspective and outlines a visualization framework for supporting the analysis of narrative geotemporal data. We discuss the strengths and
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Stories are an essential mode, not only of human communication—but also of thinking. This paper reflects on the internalization of stories from a cognitive perspective and outlines a visualization framework for supporting the analysis of narrative geotemporal data. We discuss the strengths and limitations of standard techniques for representing spatiotemporal data (coordinated views, animation or slideshow, layer superimposition, juxtaposition, and space-time cube representation) and think about their effects on mental representations of a story. Many current visualization systems offer multiple views and allow the user to investigate different aspects of a story. From a cognitive point of view, it is important to assist users in reconnecting these multiple perspectives into a coherent picture—e.g., by utilizing coherence techniques like seamless transitions. A case study involving visualizing biographical narratives illustrates how the design of advanced visualization systems can be cognitively and conceptually grounded to support the construction of an integrated internal representation. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessTechnical Note Development of a QGIS Plugin to Obtain Parameters and Elements of Plantation Trees and Vineyards with Aerial Photographs
ISPRS Int. J. Geo-Inf. 2018, 7(3), 109; doi:10.3390/ijgi7030109
Received: 4 January 2018 / Revised: 27 February 2018 / Accepted: 12 March 2018 / Published: 14 March 2018
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Abstract
Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques
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Unmanned Aerial Vehicle (UAV) imagery allows for a new way of obtaining geographic information. In this work, a Geographical Information System (GIS) open source application was developed in QGIS software that estimates several parameters and metrics on tree crown through image analysis techniques (image segmentation and image classification) and fractal analysis. The metrics that have been estimated were: area, perimeter, number of trees, distance between trees, and a missing tree check. This methodology was tested on three different plantations: olive, eucalyptus, and vineyard. The application developed is free, open source and takes advantage of QGIS integration with external software. Several tools available from Orfeo Toolbox and Geographic Resources Analysis Support System (GRASS) GIS were employed to generate a classified raster image which allows calculating the metrics referred before. The application was developed in the Python 2.7 language. Also, some functions, modules, and classes from the QGIS Application Programming Interface (API) and PyQt4 API were used. This new plugin is a valuable tool, which allowed for automatizing several parameters and metrics on tree crown using GIS analysis tools, while considering data acquired by UAV. Full article
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Open AccessShort Note Validation of Pleiades Tri-Stereo DSM in Urban Areas
ISPRS Int. J. Geo-Inf. 2018, 7(3), 118; doi:10.3390/ijgi7030118
Received: 4 January 2018 / Revised: 12 March 2018 / Accepted: 14 March 2018 / Published: 15 March 2018
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Abstract
We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1
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We present an accurate digital surface model (DSM) derived from high-resolution Pleiades-1B 0.5 m panchromatic tri-stereo images, covering an area of 400 km2 over the Athens Metropolitan Area. Remote sensing and photogrammetry tools were applied, resulting in a 1 m × 1 m posting DSM over the study area. The accuracy of the produced DSM was evaluated against measured elevations by a differential Global Positioning System (d-GPS) and a reference DSM provided by the National Cadaster and Mapping Agency S.A. Different combinations of stereo and tri-stereo images were used and tested on the quality of the produced DSM. Results revealed that the DSM produced by the tri-stereo analysis has a root mean square error (RMSE) of 1.17 m in elevation, which lies within the best reported in the literature. On the other hand, DSMs derived by standard analysis of stereo-pairs from the same sensor were found to perform worse. Line profile data showed similar patterns between the reference and produced DSM. Pleiades tri-stereo high-quality DSM products have the necessary accuracy to support applications in the domains of urban planning, including climate change mitigation and adaptation, hydrological modelling, and natural hazards, being an important input for simulation models and morphological analysis at local scales. Full article
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