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Machine Learning Techniques Applied to Geoscience Information System and Remote...
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Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Mechanical Engineering".

Deadline for manuscript submissions: closed (28 February 2019) | Viewed by 102096

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Prof. Dr. Saro Lee

E-Mail Website
Guest Editor
1. Geological Research Division, Korea Institute of Geoscience and Mineral Resources (KIGAM), 124, Gwahak-ro Yuseong-gu, Daejeon 34132, Republic of Korea
2. Department of Geophysical Exploration, Korea University of Science and Technology, 217 Gajeong-ro Yuseong-gu, Daejeon 34113, Republic of Korea
Interests: GIS application; geological hazard; geological resources
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Hyung-Sup Jung

E-Mail Website
Guest Editor
1. Department of Smart Cities, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
2. Department of Geoinformatics, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul 02120, Republic of Korea
Interests: SAR; InSAR; GeoAI; sensor modeling; satellite image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS), which deal with the spatial information, have been maturing rapidly. In addition, over the last few decades, machine learning techniques have been successfully applied to science and engineering research fields. Machine learning techniques have been widely applied to GIS and RS research fields and have recently produced valuable results in the areas of geoscience, environment, natural hazards and natural resources.

This Special Issue of Applied Sciences, “Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing”, aims to attract novel contributions covering machine learning techniques applied to GIS and RS.

Topics of interest include, but are not limited to:

  • Application of machine learning techniques combined with GIS
  • Application of machine learning techniques to remote sensing
  • Application of machine learning techniques to Global Positioning System (GPS)
  • Spatial analysis and geocomputation based on machine learning techniques
  • Spatial Prediction using machine learning techniques
  • Data processing of geoinformation using machine learning techniques
  • Comparison analysis among several machine learning techniques applied to GIS and RS
  • Application of machine learning techniques on geosciences, environments, natural hazards and natural resources as case studies

Prof. Dr. Saro Lee
Prof. Dr. Hyung-Sup Jung
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Machine learning
  • Data mining
  • Spatial Database
  • Geoinformatics
  • Geoscience Information System (GIS)
  • Remote Sensing
  • Global Positioning System (GPS)
  • Spatial Analysis

Published Papers (22 papers)

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Editorial

Jump to: Research, Review

5 pages, 175 KiB  
Editorial
Special Issue on Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing
by Hyung-Sup Jung and Saro Lee
Appl. Sci. 2019, 9(12), 2446; https://doi.org/10.3390/app9122446 - 14 Jun 2019
Cited by 7 | Viewed by 2387
Abstract
As computer and space technologies have been developed, geoscience information systems (GIS) and remote sensing (RS) technologies, which deal with the geospatial information, have been maturing rapidly [...] Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)

Research

Jump to: Editorial, Review

18 pages, 6714 KiB  
Article
Mapping Areal Precipitation with Fusion Data by ANN Machine Learning in Sparse Gauged Region
by Guoyin Xu, Zhongjing Wang and Ting Xia
Appl. Sci. 2019, 9(11), 2294; https://doi.org/10.3390/app9112294 - 04 Jun 2019
Cited by 7 | Viewed by 2955
Abstract
Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural [...] Read more.
Focusing on water resources assessment in ungauged or sparse gauged areas, a comparative evaluation of areal precipitation was conducted by remote sensing data, limited gauged data, and a fusion of gauged data and remote sensing data based on machine learning. The artificial neural network (ANN) model was used to fuse the remote sensing precipitation and ground gauge precipitation. The correlation coefficient, root mean square deviation, relative deviation and consistency principle were used to evaluate the reliability of the remote sensing precipitation. The case study in the Qaidam Basin, northwest of China, shows that the precision of the original remote sensing precipitation product of Tropical Precipitation Measurement Satellite (TRMM)-3B42RT and TRMM-3B43 was 0.61, 72.25 mm, 36.51%, 27% and 0.70, 64.24 mm, 31.63%, 32%, respectively, comparing with gauged precipitation. The precision of corrected TRMM-3B42RT and TRMM-3B43 improved to 0.89, 37.51 mm, –0.08%, 41% and 0.91, 34.22 mm, 0.11%, 42%, respectively, which indicates that the data mining considering elevation, longitude and latitude as the main influencing factors of precipitation is efficient and effective. The evaluation of areal precipitation in the Qaidam Basin shows that the mean annual precipitation is 104.34 mm, 186.01 mm and 174.76 mm based on the gauge data, corrected TRMM-3B42RT and corrected TRMM-3B43. The results show many differences in the areal precipitation based on sparse gauge precipitation data and fusion remote sensing data. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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24 pages, 2986 KiB  
Article
Landslide Prediction with Model Switching
by Darmawan Utomo, Shi-Feng Chen and Pao-Ann Hsiung
Appl. Sci. 2019, 9(9), 1839; https://doi.org/10.3390/app9091839 - 04 May 2019
Cited by 15 | Viewed by 6407
Abstract
Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, [...] Read more.
Landslides could cause huge damages to properties and severe loss of lives. Landslides can be detected by analyzing the environmental data collected by wireless sensor networks (WSNs). However, environmental data are usually complex and undergo rapid changes. Thus, if landslides can be predicted, people can leave the hazardous areas earlier. A good prediction mechanism is, thus, critical. Currently, a widely-used method is Artificial Neural Networks (ANNs), which give accurate predictions and exhibit high learning ability. Through training, the ANN weight coefficients can be made precise enough such that the network works in analogy to a human brain. However, when there is an imbalanced distribution of data, an ANN will not be able to learn the pattern of the minority class; that is, the class having very few data samples. As a result, the predictions could be inaccurate. To overcome this shortcoming of ANNs, this work proposes a model switching strategy that can choose between different predictors, according to environmental states. In addition, ANN-based error models have also been designed to predict future errors from prediction models and to compensate for these errors in the prediction phase. As a result, our proposed method can improve prediction performance, and the landslide prediction system can give warnings, on average, 44.2 min prior to the occurrence of a landslide. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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18 pages, 2166 KiB  
Article
Spatial Data Reconstruction via ADMM and Spatial Spline Regression
by Bang Liu, Borislav Mavrin, Linglong Kong and Di Niu
Appl. Sci. 2019, 9(9), 1733; https://doi.org/10.3390/app9091733 - 26 Apr 2019
Cited by 3 | Viewed by 2294
Abstract
Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this paper, we propose a novel Constrained Spatial Smoothing (CSS) approach for [...] Read more.
Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this paper, we propose a novel Constrained Spatial Smoothing (CSS) approach for the problem of spatial data reconstruction. We observe that local continuity exists in many types of spatial data. Based on this observation, our approach performs sparse recovery via a finite element method, while in the meantime enforcing the aggregated observation constraints through an innovative use of the Alternating Direction Method of Multipliers (ADMM) algorithm framework. Furthermore, our approach is able to incorporate external information as a regression add-on to further enhance recovery performance. To evaluate our approach, we study the problem of reconstructing the spatial distribution of cellphone traffic volumes based on aggregate volumes recorded at sparsely scattered base stations. We perform extensive experiments based on a large dataset of Call Detail Records and a geographical and demographical attribute dataset from the city of Milan, and compare our approach with other methods such as Spatial Spline Regression. The evaluation results show that our approach significantly outperforms various baseline approaches. This proves that jointly modeling the underlying spatial continuity and the local features that characterize the heterogeneity of different locations can help improve the performance of spatial recovery. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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25 pages, 16004 KiB  
Article
Comparison of Machine Learning Regression Algorithms for Cotton Leaf Area Index Retrieval Using Sentinel-2 Spectral Bands
by Huihui Mao, Jihua Meng, Fujiang Ji, Qiankun Zhang and Huiting Fang
Appl. Sci. 2019, 9(7), 1459; https://doi.org/10.3390/app9071459 - 07 Apr 2019
Cited by 47 | Viewed by 5685
Abstract
Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) [...] Read more.
Leaf area index (LAI) is a crucial crop biophysical parameter that has been widely used in a variety of fields. Five state-of-the-art machine learning regression algorithms (MLRAs), namely, artificial neural network (ANN), support vector regression (SVR), Gaussian process regression (GPR), random forest (RF) and gradient boosting regression tree (GBRT), have been used in the retrieval of cotton LAI with Sentinel-2 spectral bands. The performances of the five machine learning models are compared for better applications of MLRAs in remote sensing, since challenging problems remain in the selection of MLRAs for crop LAI retrieval, as well as the decision as to the optimal number for the training sample size and spectral bands to different MLRAs. A comprehensive evaluation was employed with respect to model accuracy, computational efficiency, sensitivity to training sample size and sensitivity to spectral bands. We conducted the comparison of five MLRAs in an agricultural area of Northwest China over three cotton seasons with the corresponding field campaigns for modeling and validation. Results show that the GBRT model outperforms the other models with respect to model accuracy in average ( R 2 ¯ = 0.854, R M S E ¯ = 0.674 and M A E ¯ = 0.456). SVR achieves the best performance in computational efficiency, which means it is fast to train, and to validate that it has great potentials to deliver near-real-time operational products for crop management. As for sensitivity to training sample size, GBRT behaves as the most robust model, and provides the best model accuracy on the average among the variations of training sample size, compared with other models ( R 2 ¯ = 0.884, R M S E ¯ = 0.615 and M A E ¯ = 0.452). Spectral bands sensitivity analysis with dCor (distance correlation), combined with the backward elimination approach, indicates that SVR, GPR and RF provide relatively robust performance to the spectral bands, while ANN outperforms the other models in terms of model accuracy on the average among the reduction of spectral bands ( R 2 ¯ = 0.881, R M S E ¯ = 0.625 and M A E ¯ = 0.480). A comprehensive evaluation indicates that GBRT is an appealing alternative for cotton LAI retrieval, except for its computational efficiency. Despite the different performance of the ML models, all models exhibited considerable potential for cotton LAI retrieval, which could offer accurate crop parameters information timely and accurately for crop fields management and agricultural production decisions. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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17 pages, 5250 KiB  
Article
Fusion Network for Change Detection of High-Resolution Panchromatic Imagery
by Wahyu Wiratama and Donggyu Sim
Appl. Sci. 2019, 9(7), 1441; https://doi.org/10.3390/app9071441 - 05 Apr 2019
Cited by 17 | Viewed by 3354
Abstract
This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front- and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image- [...] Read more.
This paper proposes a fusion network for detecting changes between two high-resolution panchromatic images. The proposed fusion network consists of front- and back-end neural network architectures to generate dual outputs for change detection. Two networks for change detection were applied to handle image- and high-level changes of information, respectively. The fusion network employs single-path and dual-path networks to accomplish low-level and high-level differential detection, respectively. Based on two dual outputs, a two-stage decision algorithm was proposed to efficiently yield the final change detection results. The dual outputs were incorporated into the two-stage decision by operating logical operations. The proposed algorithm was designed to incorporate not only dual network outputs but also neighboring information. In this paper, a new fused loss function was presented to estimate the errors and optimize the proposed network during the learning stage. Based on our experimental evaluation, the proposed method yields a better detection performance than conventional neural network algorithms, with an average area under the curve of 0.9709, percentage correct classification of 99%, and Kappa of 75 for many test datasets. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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17 pages, 10406 KiB  
Article
Land Subsidence Susceptibility Mapping Using Bayesian, Functional, and Meta-Ensemble Machine Learning Models
by Hyun-Joo Oh, Mutiara Syifa, Chang-Wook Lee and Saro Lee
Appl. Sci. 2019, 9(6), 1248; https://doi.org/10.3390/app9061248 - 25 Mar 2019
Cited by 63 | Viewed by 4534
Abstract
To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were [...] Read more.
To effectively prevent land subsidence over abandoned coal mines, it is necessary to quantitatively identify vulnerable areas. In this study, we evaluated the performance of predictive Bayesian, functional, and meta-ensemble machine learning models in generating land subsidence susceptibility (LSS) maps. All models were trained using half of a land subsidence inventory, and validated using the other half of the dataset. The model performance was evaluated by comparing the area under the receiver operating characteristic (ROC) curve of the resulting LSS map for each model. Among all models tested, the logit boost, which is a meta-ensemble machine leaning model, generated LSS maps with the highest accuracy (91.44%), i.e., higher than that of the other Bayesian and functional machine learning models, including the Bayes net (86.42%), naïve Bayes (85.39%), logistic (88.92%), and multilayer perceptron models (86.76%). The LSS maps produced in this study can be used to mitigate subsidence risk for people and important facilities within the study area, and as a foundation for further studies in other regions. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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29 pages, 1442 KiB  
Article
Deep Fusion Feature Based Object Detection Method for High Resolution Optical Remote Sensing Images
by Eric Ke Wang, Yueping Li, Zhe Nie, Juntao Yu, Zuodong Liang, Xun Zhang and Siu Ming Yiu
Appl. Sci. 2019, 9(6), 1130; https://doi.org/10.3390/app9061130 - 18 Mar 2019
Cited by 12 | Viewed by 2948
Abstract
With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in [...] Read more.
With the rapid growth of high-resolution remote sensing image-based applications, one of the fundamental problems in managing the increasing number of remote sensing images is automatic object detection. In this paper, we present a fusion feature-based deep learning approach to detect objects in high-resolution remote sensing images. It employs fine-tuning from ImageNet as a pre-training model to address the challenge of it lacking a large amount of training datasets in remote sensing. Besides, we improve the binarized normed gradients algorithm by multiple weak feature scoring models for candidate window selection and design a deep fusion feature extraction method with the context feature and object feature. Experiments are performed on different sizes of high-resolution optical remote sensing images. The results show that our model is better than regular models, and the average detection accuracy is 8.86% higher than objNet. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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11 pages, 4789 KiB  
Article
Study of the Relationship between Urban Expansion and PM10 Concentration Using Multi-Temporal Spatial Datasets and the Machine Learning Technique: Case Study for Daegu, South Korea
by Yun-Jae Choung and Jin-Man Kim
Appl. Sci. 2019, 9(6), 1098; https://doi.org/10.3390/app9061098 - 15 Mar 2019
Cited by 13 | Viewed by 2870
Abstract
To protect the population from respiratory diseases and to prevent the damages due to air pollution, the main cause of air pollution should be identified. This research assessed the relationship between the airborne particulate concentrations (PM10) and the urban expansion in [...] Read more.
To protect the population from respiratory diseases and to prevent the damages due to air pollution, the main cause of air pollution should be identified. This research assessed the relationship between the airborne particulate concentrations (PM10) and the urban expansion in Daegu City in South Korea from 2007 to 2017 using multi-temporal spatial datasets (Landsat images, measured PM10 data) and the machine learning technique in the following steps. First, the expanded urban areas were detected from the multiple Landsat images using support vector machine (SVM), a widely used machine learning technique. Next, the annual PM10 concentrations were calculated using the long-term measured PM10 data. Finally, the degrees of increase of the expanded urban areas and of the PM10 concentrations in Daegu from 2007 to 2017 were calculated by counting the pixels representing the expanded urban areas and computing variation of the annual PM10 concentrations, respectively. The experiment results showed that there is a minimal or even no relationship at all between the urban expansion and the PM10 concentrations because the urban areas expanded by 55.27 km2 but the annual PM10 concentrations decreased by 17.37 μg/m³ in Daegu from 2007 to 2017. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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15 pages, 2302 KiB  
Article
A Single Point-Based Multilevel Features Fusion and Pyramid Neighborhood Optimization Method for ALS Point Cloud Classification
by Yong Li, Guofeng Tong, Xiance Du, Xiang Yang, Jianjun Zhang and Lin Yang
Appl. Sci. 2019, 9(5), 951; https://doi.org/10.3390/app9050951 - 06 Mar 2019
Cited by 20 | Viewed by 2662
Abstract
3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel [...] Read more.
3D point cloud classification has wide applications in the field of scene understanding. Point cloud classification based on points can more accurately segment the boundary region between adjacent objects. In this paper, a point cloud classification algorithm based on a single point multilevel features fusion and pyramid neighborhood optimization are proposed for a Airborne Laser Scanning (ALS) point cloud. First, the proposed algorithm determines the neighborhood region of each point, after which the features of each single point are extracted. For the characteristics of the ALS point cloud, two new feature descriptors are proposed, i.e., a normal angle distribution histogram and latitude sampling histogram. Following this, multilevel features of a single point are constructed by multi-resolution of the point cloud and multi-neighborhood spaces. Next, the features are trained by the Support Vector Machine based on a Gaussian kernel function, and the points are classified by the trained model. Finally, a classification results optimization method based on a multi-scale pyramid neighborhood constructed by a multi-resolution point cloud is used. In the experiment, the algorithm is tested by a public dataset. The experimental results show that the proposed algorithm can effectively classify large-scale ALS point clouds. Compared with the existing algorithms, the proposed algorithm has a better classification performance. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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19 pages, 5181 KiB  
Article
Landslide Susceptibility Mapping Based on Random Forest and Boosted Regression Tree Models, and a Comparison of Their Performance
by Soyoung Park and Jinsoo Kim
Appl. Sci. 2019, 9(5), 942; https://doi.org/10.3390/app9050942 - 06 Mar 2019
Cited by 146 | Viewed by 6010
Abstract
This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, [...] Read more.
This study aims to analyze and compare landslide susceptibility at Woomyeon Mountain, South Korea, based on the random forest (RF) model and the boosted regression tree (BRT) model. Through the construction of a landslide inventory map, 140 landslide locations were found. Among these, 42 (30%) were reserved to validate the model after 98 (70%) had been selected at random for model training. Fourteen landslide explanatory variables related to topography, hydrology, and forestry factors were considered and selected, based on the results of information gain for the modeling. The results were evaluated and compared using the receiver operating characteristic curve and statistical indices. The analysis showed that the RF model was better than the BRT model. The RF model yielded higher specificity, overall accuracy, and kappa index than the BRT model. In addition, the RF model, with a prediction rate of 0.865, performed slightly better than the BRT model, which had a prediction rate of 0.851. These results indicate that the landslide susceptibility maps (LSMs) produced in this study had good performance for predicting the spatial landslide distribution in the study area. These LSMs could be helpful for establishing mitigation strategies and for land use planning. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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17 pages, 4313 KiB  
Article
Impact of Texture Information on Crop Classification with Machine Learning and UAV Images
by Geun-Ho Kwak and No-Wook Park
Appl. Sci. 2019, 9(4), 643; https://doi.org/10.3390/app9040643 - 14 Feb 2019
Cited by 90 | Viewed by 5943
Abstract
Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often [...] Read more.
Unmanned aerial vehicle (UAV) images that can provide thematic information at much higher spatial and temporal resolutions than satellite images have great potential in crop classification. Due to the ultra-high spatial resolution of UAV images, spatial contextual information such as texture is often used for crop classification. From a data availability viewpoint, it is not always possible to acquire time-series UAV images due to limited accessibility to the study area. Thus, it is necessary to improve classification performance for situations when a single or minimum number of UAV images are available for crop classification. In this study, we investigate the potential of gray-level co-occurrence matrix (GLCM)-based texture information for crop classification with time-series UAV images and machine learning classifiers including random forest and support vector machine. In particular, the impact of combining texture and spectral information on the classification performance is evaluated for cases that use only one UAV image or multi-temporal images as input. A case study of crop classification in Anbandegi of Korea was conducted for the above comparisons. The best classification accuracy was achieved when multi-temporal UAV images which can fully account for the growth cycles of crops were combined with GLCM-based texture features. However, the impact of the utilization of texture information was not significant. In contrast, when one August UAV image was used for crop classification, the utilization of texture information significantly affected the classification performance. Classification using texture features extracted from GLCM with larger kernel size significantly improved classification accuracy, an improvement of 7.72%p in overall accuracy for the support vector machine classifier, compared with classification based solely on spectral information. These results indicate the usefulness of texture information for classification of ultra-high-spatial-resolution UAV images, particularly when acquisition of time-series UAV images is difficult and only one UAV image is used for crop classification. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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23 pages, 4060 KiB  
Article
Modeling of CO Emissions from Traffic Vehicles Using Artificial Neural Networks
by Omer Saud Azeez, Biswajeet Pradhan, Helmi Z. M. Shafri, Nagesh Shukla, Chang-Wook Lee and Hossein Mojaddadi Rizeei
Appl. Sci. 2019, 9(2), 313; https://doi.org/10.3390/app9020313 - 16 Jan 2019
Cited by 47 | Viewed by 6469
Abstract
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from [...] Read more.
Traffic emissions are considered one of the leading causes of environmental impact in megacities and their dangerous effects on human health. This paper presents a hybrid model based on data mining and GIS models designed to predict vehicular Carbon Monoxide (CO) emitted from traffic on the New Klang Valley Expressway, Malaysia. The hybrid model was developed based on the integration of GIS and the optimized Artificial Neural Network algorithm that combined with the Correlation based Feature Selection (CFS) algorithm to predict the daily vehicular CO emissions and generate prediction maps at a microscale level in a small urban area by using a field survey and open source data, which are the main contributions to this paper. The other contribution is related to the case study, which represents the spatial and quantitative variations in the vehicular CO emissions between toll plaza areas and road networks. The proposed hybrid model consists of three steps: the first step is the implementation of the correlation-based Feature Selection model to select the best model’s predictors; the second step is the prediction of vehicular CO by using a multilayer perceptron neural network model; and the third step is the creation of micro scale prediction maps. The model was developed using six traffic CO predictors: number of vehicles, number of heavy vehicles, number of motorbikes, temperature, wind speed and a digital surface model. The network architecture and its hyperparameters were optimized through a grid search approach. The traffic CO concentrations were observed at 15-min intervals on weekends and weekdays, four times per day. The results showed that the developed model had achieved validation accuracy of 80.6 %. Overall, the developed models are found to be promising tools for vehicular CO simulations in highly congested areas. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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26 pages, 7494 KiB  
Article
Landslide Susceptibility Modeling Using Integrated Ensemble Weights of Evidence with Logistic Regression and Random Forest Models
by Wei Chen, Zenghui Sun and Jichang Han
Appl. Sci. 2019, 9(1), 171; https://doi.org/10.3390/app9010171 - 04 Jan 2019
Cited by 126 | Viewed by 6560
Abstract
The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide [...] Read more.
The main aim of this study was to compare the performances of the hybrid approaches of traditional bivariate weights of evidence (WoE) with multivariate logistic regression (WoE-LR) and machine learning-based random forest (WoE-RF) for landslide susceptibility mapping. The performance of the three landslide models was validated with receiver operating characteristic (ROC) curves and area under the curve (AUC). The results showed that the areas under the curve obtained using the WoE, WoE-LR, and WoE-RF methods were 0.720, 0.773, and 0.802 for the training dataset, and were 0.695, 0.763, and 0.782 for the validation dataset, respectively. The results demonstrate the superiority of hybrid models and that the resultant maps would be useful for land use planning in landslide-prone areas. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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21 pages, 12625 KiB  
Article
A New Weighting Approach with Application to Ionospheric Delay Constraint for GPS/GALILEO Real-Time Precise Point Positioning
by Tianjun Liu, Jian Wang, Hang Yu, Xinyun Cao and Yulong Ge
Appl. Sci. 2018, 8(12), 2537; https://doi.org/10.3390/app8122537 - 07 Dec 2018
Cited by 14 | Viewed by 2729
Abstract
The real-time precise point positioning (RT PPP) technique has attracted increasing attention due to its high-accuracy and real-time performance. However, a considerable initialization time, normally a few hours, is required in order to achieve the proper convergence of the real-valued ambiguities and other [...] Read more.
The real-time precise point positioning (RT PPP) technique has attracted increasing attention due to its high-accuracy and real-time performance. However, a considerable initialization time, normally a few hours, is required in order to achieve the proper convergence of the real-valued ambiguities and other estimate parameters. The RT PPP convergence time may be reduced by combining quad-constellation global navigation satellite system (GNSS), or by using RT ionospheric products to constrain the ionosphere delay. But to improve the performance of convergence and achieve the best positioning solutions in the whole data processing, proper and precise variances of the observations and ionospheric constraints are important, since they involve the processing of measurements of different types and with different accuracy. To address this issue, a weighting approach is proposed by a combination of the weight factors searching algorithm and a moving-window average filter. In this approach, the variances of ionospheric constraints are adjusted dynamically according to the principle that the sum of the quadratic forms of weighted residuals is the minimum, and the filter is applied to combine all epoch-by-epoch weight factors within a time window. To evaluate the proposed approach, datasets from 31 Multi-GNSS Experiment (MGEX) stations during the period of DOY (day of year) 023-054 in 2018 are analyzed with different positioning modes and different data processing methods. Experimental results show that the new weighting approach can significantly improve the convergence performance, and that the maximum improvement rate reaches 35.9% in comparison to the traditional method of priori variance in the static dual-frequency positioning mode. In terms of the RMS (Root Mean Square) statistics of positioning errors calculated by the new method after filter convergence, the same accuracy level as that of RT PPP without constraints can be achieved. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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20 pages, 2013 KiB  
Article
Convolutional Neural Network-Based Remote Sensing Images Segmentation Method for Extracting Winter Wheat Spatial Distribution
by Chengming Zhang, Shuai Gao, Xiaoxia Yang, Feng Li, Maorui Yue, Yingjuan Han, Hui Zhao, Ya’nan Zhang and Keqi Fan
Appl. Sci. 2018, 8(10), 1981; https://doi.org/10.3390/app8101981 - 19 Oct 2018
Cited by 12 | Viewed by 3771
Abstract
When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution [...] Read more.
When extracting winter wheat spatial distribution by using convolutional neural network (CNN) from Gaofen-2 (GF-2) remote sensing images, accurate identification of edge pixel is the key to improving the result accuracy. In this paper, an approach for extracting accurate winter wheat spatial distribution based on CNN is proposed. A hybrid structure convolutional neural network (HSCNN) was first constructed, which consists of two independent sub-networks of different depths. The deeper sub-network was used to extract the pixels present in the interior of the winter wheat field, whereas the shallower sub-network extracts the pixels at the edge of the field. The model was trained by classification-based learning and used in image segmentation for obtaining the distribution of winter wheat. Experiments were performed on 39 GF-2 images of Shandong province captured during 2017–2018, with SegNet and DeepLab as comparison models. As shown by the results, the average accuracy of SegNet, DeepLab, and HSCNN was 0.765, 0.853, and 0.912, respectively. HSCNN was equally as accurate as DeepLab and superior to SegNet for identifying interior pixels, and its identification of the edge pixels was significantly better than the two comparison models, which showed the superiority of HSCNN in the identification of winter wheat spatial distribution. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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13 pages, 4223 KiB  
Article
Dual-Dense Convolution Network for Change Detection of High-Resolution Panchromatic Imagery
by Wahyu Wiratama, Jongseok Lee, Sang-Eun Park and Donggyu Sim
Appl. Sci. 2018, 8(10), 1785; https://doi.org/10.3390/app8101785 - 01 Oct 2018
Cited by 37 | Viewed by 4663
Abstract
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change [...] Read more.
This paper presents a robust change detection algorithm for high-resolution panchromatic imagery using a proposed dual-dense convolutional network (DCN). In this work, a joint structure of two deep convolutional networks with dense connectivity in convolution layers is designed in order to accomplish change detection for satellite images acquired at different times. The proposed network model detects pixel-wise temporal change based on local characteristics by incorporating information from neighboring pixels. Dense connection in convolution layers is designed to reuse preceding feature maps by connecting them to all subsequent layers. Dual networks are incorporated by measuring the dissimilarity of two temporal images. In the proposed algorithm for change detection, a contrastive loss function is used in a learning stage by running over multiple pairs of samples. According to our evaluation, we found that the proposed framework achieves better detection performance than conventional algorithms, in area under the curve (AUC) of 0.97, percentage correct classification (PCC) of 99%, and Kappa of 69, on average. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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23 pages, 7575 KiB  
Article
Single-Class Data Descriptors for Mapping Panax notoginseng through P-Learning
by Fei Deng and Shengliang Pu
Appl. Sci. 2018, 8(9), 1448; https://doi.org/10.3390/app8091448 - 24 Aug 2018
Cited by 3 | Viewed by 4252
Abstract
Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare [...] Read more.
Machine learning-based remote-sensing techniques have been widely used for the production of specific land cover maps at a fine scale. P-learning is a collection of machine learning techniques for training the class descriptors on the positive samples only. Panax notoginseng is a rare medicinal plant, which also has been a highly regarded traditional Chinese medicine resource in China for hundreds of years. Until now, Panax notoginseng has scarcely been observed and monitored from space. Remote sensing of natural resources provides us new insights into the resource inventory of Chinese materia medica resources, particularly of Panax notoginseng. Generally, land-cover mapping involves focusing on a number of landscape classes. However, sometimes a subset or one of the classes will be the only part of interest. In term of this study, the Panax notoginseng field is the right unit class. Such a situation makes single-class data descriptors (SCDDs) especially significant for specific land-cover interpretation. In this paper, we delineated the application such that a stack of SCDDs were trained for remote-sensing mapping of Panax notoginseng fields through P-learning. We employed and compared SCDDs, i.e., the simple Gaussian target distribution, the robust Gaussian target distribution, the minimum covariance determinant Gaussian, the mixture of Gaussian, the auto-encoder neural network, the k-means clustering, the self-organizing map, the minimum spanning tree, the k-nearest neighbor, the incremental support vector data description, the Parzen density estimator, and the principal component analysis; as well as three ensemble classifiers, i.e., the mean, median, and voting combiners. Experiments demonstrate that most SCDDs could achieve promising classification performance. Furthermore, this work utilized a set of the elaborate samples manually collected at a pixel-level by experts, which was intended to be a benchmark dataset for the future work. The measuring performance of SCDDs gives us challenging insights to define the selection criteria and scoring proof for choosing a fine SCDD in mapping a specific landscape class. With the increment of remotely sensed satellite data of the study area, the spatial distribution of Panax notoginseng could be continuously derived in the local area on the basis of SCDDs. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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21 pages, 4359 KiB  
Article
Spatial Modelling of Gully Erosion Using GIS and R Programing: A Comparison among Three Data Mining Algorithms
by Alireza Arabameri, Biswajeet Pradhan, Hamid Reza Pourghasemi, Khalil Rezaei and Norman Kerle
Appl. Sci. 2018, 8(8), 1369; https://doi.org/10.3390/app8081369 - 14 Aug 2018
Cited by 101 | Viewed by 7111
Abstract
Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), [...] Read more.
Gully erosion triggers land degradation and restricts the use of land. This study assesses the spatial relationship between gully erosion (GE) and geo-environmental variables (GEVs) using Weights-of-Evidence (WoE) Bayes theory, and then applies three data mining methods—Random Forest (RF), boosted regression tree (BRT), and multivariate adaptive regression spline (MARS)—for gully erosion susceptibility mapping (GESM) in the Shahroud watershed, Iran. Gully locations were identified by extensive field surveys, and a total of 172 GE locations were mapped. Twelve gully-related GEVs: Elevation, slope degree, slope aspect, plan curvature, convergence index, topographic wetness index (TWI), lithology, land use/land cover (LU/LC), distance from rivers, distance from roads, drainage density, and NDVI were selected to model GE. The results of variables importance by RF and BRT models indicated that distance from road, elevation, and lithology had the highest effect on GE occurrence. The area under the curve (AUC) and seed cell area index (SCAI) methods were used to validate the three GE maps. The results showed that AUC for the three models varies from 0.911 to 0.927, whereas the RF model had a prediction accuracy of 0.927 as per SCAI values, when compared to the other models. The findings will be of help for planning and developing the studied region. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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16 pages, 5888 KiB  
Article
Learning-Based Colorization of Grayscale Aerial Images Using Random Forest Regression
by Dae Kyo Seo, Yong Hyun Kim, Yang Dam Eo and Wan Yong Park
Appl. Sci. 2018, 8(8), 1269; https://doi.org/10.3390/app8081269 - 31 Jul 2018
Cited by 18 | Viewed by 5321
Abstract
Image colorization assigns colors to a grayscale image, which is an important yet difficult image-processing task encountered in various applications. In particular, grayscale aerial image colorization is a poorly posed problem that is affected by the sun elevation angle, seasons, sensor parameters, etc. [...] Read more.
Image colorization assigns colors to a grayscale image, which is an important yet difficult image-processing task encountered in various applications. In particular, grayscale aerial image colorization is a poorly posed problem that is affected by the sun elevation angle, seasons, sensor parameters, etc. Furthermore, since different colors may have the same intensity, it is difficult to solve this problem using traditional methods. This study proposes a novel method for the colorization of grayscale aerial images using random forest (RF) regression. The algorithm uses one grayscale image for input and one-color image for reference, both of which have similar seasonal features at the same location. The reference color image is then converted from the Red-Green-Blue (RGB) color space to the CIE L*a*b (Lab) color space in which the luminance is used to extract training pixels; this is done by performing change detection with the input grayscale image, and color information is used to establish color relationships. The proposed method directly establishes color relationships between features of the input grayscale image and color information of the reference color image based on the corresponding training pixels. The experimental results show that the proposed method outperforms several state-of-the-art algorithms in terms of both visual inspection and quantitative evaluation. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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22 pages, 22241 KiB  
Article
Enhancing Prediction Performance of Landslide Susceptibility Model Using Hybrid Machine Learning Approach of Bagging Ensemble and Logistic Model Tree
by Xuan Luan Truong, Muneki Mitamura, Yasuyuki Kono, Venkatesh Raghavan, Go Yonezawa, Xuan Quang Truong, Thi Hang Do, Dieu Tien Bui and Saro Lee
Appl. Sci. 2018, 8(7), 1046; https://doi.org/10.3390/app8071046 - 27 Jun 2018
Cited by 86 | Viewed by 7376
Abstract
The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively [...] Read more.
The objective of this research is introduce a new machine learning ensemble approach that is a hybridization of Bagging ensemble (BE) and Logistic Model Trees (LMTree), named as BE-LMtree, for improving the performance of the landslide susceptibility model. The LMTree is a relatively new machine learning algorithm that was rarely explored for landslide study, whereas BE is an ensemble framework that has proven highly efficient for landslide modeling. Upper Reaches Area of Red River Basin (URRB) in Northwest region of Viet Nam was employed as a case study. For this work, a GIS database for the URRB area has been established, which contains a total of 255 landslide polygons and eight predisposing factors i.e., slope, aspect, elevation, land cover, soil type, lithology, distance to fault, and distance to river. The database was then used to construct and validate the proposed BE-LMTree model. Quality of the final BE-LMTree model was checked using confusion matrix and a set of statistical measures. The result showed that the performance of the proposed BE-LMTree model is high with the classification accuracy is 93.81% on the training dataset and the prediction capability is 83.4% on the on the validation dataset. When compared to the support vector machine model and the LMTree model, the proposed BE-LMTree model performs better; therefore, we concluded that the BE-LMTree could prove to be a new efficient tool that should be used for landslide modeling. This research could provide useful results for landslide modeling in landslide prone areas. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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Review

Jump to: Editorial, Research

23 pages, 772 KiB  
Review
A Critical Review of Spatial Predictive Modeling Process in Environmental Sciences with Reproducible Examples in R
by Jin Li
Appl. Sci. 2019, 9(10), 2048; https://doi.org/10.3390/app9102048 - 17 May 2019
Cited by 15 | Viewed by 4351
Abstract
Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then [...] Read more.
Spatial predictive methods are increasingly being used to generate predictions across various disciplines in environmental sciences. Accuracy of the predictions is critical as they form the basis for environmental management and conservation. Therefore, improving the accuracy by selecting an appropriate method and then developing the most accurate predictive model(s) is essential. However, it is challenging to select an appropriate method and find the most accurate predictive model for a given dataset due to many aspects and multiple factors involved in the modeling process. Many previous studies considered only a portion of these aspects and factors, often leading to sub-optimal or even misleading predictive models. This study evaluates a spatial predictive modeling process, and identifies nine major components for spatial predictive modeling. Each of these nine components is then reviewed, and guidelines for selecting and applying relevant components and developing accurate predictive models are provided. Finally, reproducible examples using spm, an R package, are provided to demonstrate how to select and develop predictive models using machine learning, geostatistics, and their hybrid methods according to predictive accuracy for spatial predictive modeling; reproducible examples are also provided to generate and visualize spatial predictions in environmental sciences. Full article
(This article belongs to the Special Issue Machine Learning Techniques Applied to Geoscience Information System and Remote Sensing)
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