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ISPRS Int. J. Geo-Inf., Volume 7, Issue 2 (February 2018)

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Cover Story (view full-size image) We present a Spatial Information System (SIS) developed in the research project, “Traditional [...] Read more.
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Open AccessArticle Social Force Model-Based Group Behavior Simulation in Virtual Geographic Environments
ISPRS Int. J. Geo-Inf. 2018, 7(2), 79; https://doi.org/10.3390/ijgi7020079
Received: 16 December 2017 / Revised: 14 February 2018 / Accepted: 18 February 2018 / Published: 24 February 2018
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Abstract
Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals,
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Virtual geographic environments (VGEs) are extensively used to explore the relationship between humans and environments. Crowd simulation provides a method for VGEs to represent crowd behaviors that are observed in the real world. The social force model (SFM) can simulate interactions among individuals, but it has not sufficiently accounted for inter-group and intra-group behaviors which are important components of crowd dynamics. We present the social group force model (SGFM), based on an extended SFM, to simulate group behaviors in VGEs with focuses on the avoiding behaviors among different social groups and the coordinate behaviors among subgroups that belong to one social group. In our model, psychological repulsions between social groups make them avoid with the whole group and group members can stick together as much as possible; while social groups are separated into several subgroups, the rear subgroups try to catch up and keep the whole group cohesive. We compare the simulation results of the SGFM with the extended SFM and the phenomena in videos. Then we discuss the function of Virtual Reality (VR) in crowd simulation visualization. The results indicate that the SGFM can enhance social group behaviors in crowd dynamics. Full article
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Open AccessArticle Deriving Animal Movement Behaviors Using Movement Parameters Extracted from Location Data
ISPRS Int. J. Geo-Inf. 2018, 7(2), 78; https://doi.org/10.3390/ijgi7020078
Received: 14 December 2017 / Revised: 9 February 2018 / Accepted: 18 February 2018 / Published: 24 February 2018
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Abstract
We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through
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We present a methodology for distinguishing between three types of animal movement behavior (foraging, resting, and walking) based on high-frequency tracking data. For each animal we quantify an individual movement path. A movement path is a temporal sequence consisting of the steps through space taken by an animal. By selecting a set of appropriate movement parameters, we develop a method to assess movement behavioral states, reflected by changes in the movement parameters. The two fundamental tasks of our study are segmentation and clustering. By segmentation, we mean the partitioning of the trajectory into segments, which are homogeneous in terms of their movement parameters. By clustering, we mean grouping similar segments together according to their estimated movement parameters. The proposed method is evaluated using field observations (done by humans) of movement behavior. We found that on average, our method agreed with the observational data (ground truth) at a level of 80.75% ± 5.9% (SE). Full article
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Open AccessArticle Reliable Rescue Routing Optimization for Urban Emergency Logistics under Travel Time Uncertainty
ISPRS Int. J. Geo-Inf. 2018, 7(2), 77; https://doi.org/10.3390/ijgi7020077
Received: 20 December 2017 / Revised: 6 February 2018 / Accepted: 18 February 2018 / Published: 24 February 2018
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Abstract
The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid
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The reliability of rescue routes is critical for urban emergency logistics during disasters. However, studies on reliable rescue routing under stochastic networks are still rare. This paper proposes a multiobjective rescue routing model for urban emergency logistics under travel time reliability. A hybrid metaheuristic integrating ant colony optimization (ACO) and tabu search (TS) was designed to solve the model. An experiment optimizing rescue routing plans under a real urban storm event, was carried out to validate the proposed model. The experimental results showed how our approach can improve rescue efficiency with high travel time reliability. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Open AccessProject Report The Rural Development Policy in Extremadura (SW Spain): Spatial Location Analysis of Leader Projects
ISPRS Int. J. Geo-Inf. 2018, 7(2), 76; https://doi.org/10.3390/ijgi7020076
Received: 24 January 2018 / Revised: 16 February 2018 / Accepted: 21 February 2018 / Published: 24 February 2018
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Abstract
Since the 1990s, a series of rural development aid programs (LEADER Approach) has been implemented in European rural areas, including Extremadura, in order to solve the demographic, social, and economic problems that rural areas experience. The main objective of these programs is to
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Since the 1990s, a series of rural development aid programs (LEADER Approach) has been implemented in European rural areas, including Extremadura, in order to solve the demographic, social, and economic problems that rural areas experience. The main objective of these programs is to diversify the economy to reverse these problems. The purpose of this present paper is to study the distribution of the investments committed during the period of 2000–2013 in Extremadura according to the geolocation and to perform the analysis of clusters through Local Moran’s I, Getis-Ord Gi*, and Kernel Density in order to determine whether the results are related to the demographic and economic behavior of each territory of action and if these act as location factors for investments. We found that most dynamic towns receive more investments, leaving out the more physically, economically, and demographically disadvantaged ones. Full article
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Open AccessArticle An Open-Boundary Locally Weighted Dynamic Time Warping Method for Cropland Mapping
ISPRS Int. J. Geo-Inf. 2018, 7(2), 75; https://doi.org/10.3390/ijgi7020075
Received: 28 December 2017 / Revised: 15 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
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Abstract
This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and
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This paper proposes an open-boundary locally weighted dynamic time warping (OLWDTW) method using MODIS Normalized Difference Vegetation Index (NDVI) time-series data for cropland recognition. The method solves the problem of flexible planting times for crops in Southeast Asia, which has sufficient thermal and water conditions. For NDVI time series starting at the beginning of the year and terminating at the end of the year, the method can separate the non-growing season cycle and growing season cycle for crops. The non-growing season cycle may provide some useful information for crop recognition, such as soil conditions. However, the shape of the growing season’s NDVI time series for crops is the key to separating cropland from other land cover types because the shape contains all of the crop growth information. The principle of the OLWDTW method is to enhance the effects of the growing season cycle on the NDVI time series by adding a local weight to the growing season when comparing the similarity of time series based on the open-boundary dynamic time warping (DTW) method. Experiments with two satellite datasets located near the Khorat Plateau in the Lower Mekong Basin validate that OLWDTW effectively improves the precision of cropland recognition compared to a non-weighted open-boundary DTW method in terms of overall accuracy. The method’s classification accuracy on cropland exceeds the non-weighted open-boundary DTW by 5–7%. In future studies, an open-boundary self-adaption locally weighted DTW and a more effective combination rule for different crop types should be explored for the method’s best performance and highest extraction accuracy for cropland. Full article
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Open AccessFeature PaperArticle Classification of PolSAR Images by Stacked Random Forests
ISPRS Int. J. Geo-Inf. 2018, 7(2), 74; https://doi.org/10.3390/ijgi7020074
Received: 30 January 2018 / Revised: 16 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
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Abstract
This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To
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This paper proposes the use of Stacked Random Forests (SRF) for the classification of Polarimetric Synthetic Aperture Radar images. SRF apply several Random Forest instances in a sequence where each individual uses the class estimate of its predecessor as an additional feature. To this aim, the internal node tests are designed to work not only directly on the complex-valued image data, but also on spatially varying probability distributions and thus allow a seamless integration of RFs within the stacking framework. Experimental results show that the classification performance is consistently improved by the proposed approach, i.e., the achieved accuracy is increased by 4 % and 7 % for one fully- and one dual-polarimetric dataset. This increase only comes at the cost of a linear increased training and prediction time, which is rather limited as the method converges quickly. Full article
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
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Open AccessArticle Forecasting Transplanted Rice Yield at the Farm Scale Using Moderate-Resolution Satellite Imagery and the AquaCrop Model: A Case Study of a Rice Seed Production Community in Thailand
ISPRS Int. J. Geo-Inf. 2018, 7(2), 73; https://doi.org/10.3390/ijgi7020073
Received: 23 November 2017 / Revised: 17 January 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
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Abstract
Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be
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Thailand has recently introduced agricultural policies to promote large-scale rice farming through supporting and integrating small-scale farmers. However, achieving these policies requires agricultural tools that can assist farmers in rice farming planning and management. Crop models, along with remote sensing technologies, can be useful for farmers and field managers in this regard. In this study, we used the AquaCrop model along with moderate-resolution satellite images (30 m) to simulate the rice yield for small-scale farmers. We conducted field surveys on rice characteristics in order to calibrate the crop model parameters. Data on rice crop, leaf area index (LAI), canopy cover (CC) and agricultural practices were used to calibrate the model. In addition, the optimal rice constant value for conversion of CC was investigated. HJ-1A/B satellite images were used to calculate the CC value, which was then used to simulate yield. The validated results were applied to 126 sample pixels within transplanted rice fields, which were extracted from satellite imagery of activated rice plots using equivalent transplanting methods to the study area. The rice yield simulated using the AquaCrop model and assimilated with the results of HJ-1A/B, produced a satisfactory outcome when implemented into the validated rice plots, with RMSE = 0.18 t ha−1 and R2 = 0.88. These results suggest that integration of moderate-resolution satellite imagery and this crop model are useful tools for assisting rice farmers and field managers in their planning and management. Full article
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Open AccessArticle Adaptive Component Selection-Based Discriminative Model for Object Detection in High-Resolution SAR Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(2), 72; https://doi.org/10.3390/ijgi7020072
Received: 6 December 2017 / Revised: 14 February 2018 / Accepted: 18 February 2018 / Published: 23 February 2018
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Abstract
This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and
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This paper proposes an innovative Adaptive Component Selection-Based Discriminative Model (ACSDM) for object detection in high-resolution synthetic aperture radar (SAR) imagery. In order to explore the structural relationships between the target and the components, a multi-scale detector consisting of a root filter and several part filters is established, using Histogram of Oriented Gradient (HOG) features to describe the object from different resolutions. To make the detected components of practical significance, the size and anchor position of each component are determined through statistical methods. When training the root model and the corresponding part models, manual annotation is adopted to label the target in the training set. Besides, a penalty factor is introduced to compensate information loss in preprocessing. In the detection stage, the Small Area-Based Non-Maximum Suppression (SANMS) method is utilised for filtering out duplicate results. In the experiments, the aeroplanes in TerraSAR-X SAR images are detected by the ACSDM algorithm and different comparative methods. The results indicate that the proposed method has a lower false alarm rate and can detect the components accurately. Full article
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Open AccessArticle Spatial Footprints of Human Perceptual Experience in Geo-Social Media
ISPRS Int. J. Geo-Inf. 2018, 7(2), 71; https://doi.org/10.3390/ijgi7020071
Received: 25 December 2017 / Revised: 15 February 2018 / Accepted: 17 February 2018 / Published: 23 February 2018
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Abstract
Analyses of social media have increased in importance for understanding human behaviors, interests, and opinions. Business intelligence based on social media can reduce the costs of managing customer trend complexities. This paper focuses on analyzing sensation information representing human perceptual experiences in social
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Analyses of social media have increased in importance for understanding human behaviors, interests, and opinions. Business intelligence based on social media can reduce the costs of managing customer trend complexities. This paper focuses on analyzing sensation information representing human perceptual experiences in social media through the five senses: sight, hearing, touch, smell, and taste. First a measurement is defined to estimate social sensation intensities, and subsequently sensation characteristics on geo-social media are identified using geo-spatial footprints. Finally, we evaluate the accuracy and F-measure of our approach by comparing with baselines. Full article
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Open AccessArticle Evaluating and Optimizing Urban Green Spaces for Compact Urban Areas: Cukurova District in Adana, Turkey
ISPRS Int. J. Geo-Inf. 2018, 7(2), 70; https://doi.org/10.3390/ijgi7020070
Received: 13 December 2017 / Revised: 8 February 2018 / Accepted: 17 February 2018 / Published: 22 February 2018
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Abstract
In recent decades, the ever-decreasing number of green spaces have become insufficient to meet public demands in terms of accessibility, spatial distribution and the size of urban green areas. This is mainly due to increasing attention on the issue of accessibility to urban
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In recent decades, the ever-decreasing number of green spaces have become insufficient to meet public demands in terms of accessibility, spatial distribution and the size of urban green areas. This is mainly due to increasing attention on the issue of accessibility to urban green spaces. This paper aims to quantify accessibility according to existing qualitative and quantitative characteristics of urban green spaces (UGS) in Çukurova district in Adana, Turkey. Firstly, qualitative and quantitative characteristics of UGS are divided into five main categories: area size, amenities of the UGS, transportation, focal points and population density. A set of 59 criteria are used by referring to the literature and expert views. Secondly, the Weighted Criteria Method was used to determine the significance of levels within these criteria and the existing situation of each park was identified and scored via field work. Thirdly, accounts of the distance of UGS service areas distance from people or users were optimized according to the total scores of existing UGS sites. Finally, the service areas of UGS were mapped by using Network Analysis tools. Results highlight some practical implications of optimizing accessibility for urban planning, for instance, specific land uses might be chosen for highly accessible UGS particularly those characterized by their high area size and equipment variety, low population density, and proximity to units. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(2), 69; https://doi.org/10.3390/ijgi7020069
Received: 29 November 2017 / Revised: 7 February 2018 / Accepted: 18 February 2018 / Published: 22 February 2018
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Abstract
The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data
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The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle Integrated Participatory and Collaborative Risk Mapping for Enhancing Disaster Resilience
ISPRS Int. J. Geo-Inf. 2018, 7(2), 68; https://doi.org/10.3390/ijgi7020068
Received: 29 November 2017 / Revised: 22 January 2018 / Accepted: 17 February 2018 / Published: 21 February 2018
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Abstract
Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this
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Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this challenge, we develop a general methodological approach that integrates community-based participatory mapping processes, one that has been widely used by governments and non-government organizations in the fields of natural resources management, disaster risk reduction and rural development, with emerging collaborative digital mapping techniques. We demonstrate the value and potential of this integrated participatory and collaborative mapping approach by conducting a pilot study in the flood-prone lower Karnali river basin in Western Nepal. The process engaged a wide range of stakeholders and non-stakeholder citizens to co-produce locally relevant geographic information on resources, capacities, and flood risks of selected communities. The new digital community maps are richer in content, more accurate, and easier to update and share than those produced by conventional Vulnerability and Capacity Assessments (VCAs), a variant of Participatory Rural Appraisal (PRA), that is widely used by various government and non-government organizations. We discuss how this integrated mapping approach may provide an effective link between coordinating and implementing local disaster risk reduction and resilience building interventions to designing and informing regional development plans, as well as its limitations in terms of technological barrier, map ownership, and empowerment potential. Full article
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Open AccessArticle An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks
ISPRS Int. J. Geo-Inf. 2018, 7(2), 67; https://doi.org/10.3390/ijgi7020067
Received: 29 December 2017 / Revised: 10 February 2018 / Accepted: 18 February 2018 / Published: 21 February 2018
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Abstract
Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs) provide rich content, such as
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Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs) provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users’ free days, and the social relationships among users are extracted automatically from location histories and users’ profiles in the LBSN. These factors are combined to determine the similarities among users. The users are partitioned into groups based on these similarities. Group size is the key to coordinating group members and enhancing their satisfaction. Therefore, a modified k-medoids method is developed to cluster users into groups with specific sizes. To evaluate the efficiency of the proposed method, its mean intra-cluster distance and its distribution of cluster sizes are compared to those of general clustering algorithms. The results reveal that the proposed method compares favourably with general clustering approaches, such as k-medoids and spectral clustering, in separating users into groups of a specific size with a lower mean intra-cluster distance. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Open AccessReview A Critical Review of the Integration of Geographic Information System and Building Information Modelling at the Data Level
ISPRS Int. J. Geo-Inf. 2018, 7(2), 66; https://doi.org/10.3390/ijgi7020066
Received: 5 February 2018 / Revised: 5 February 2018 / Accepted: 18 February 2018 / Published: 20 February 2018
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Abstract
The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant,
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The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration. Full article
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Open AccessArticle Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review
ISPRS Int. J. Geo-Inf. 2018, 7(2), 65; https://doi.org/10.3390/ijgi7020065
Received: 29 December 2017 / Revised: 12 February 2018 / Accepted: 17 February 2018 / Published: 20 February 2018
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Abstract
This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find
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This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components. Full article
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