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Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (30 April 2021) | Viewed by 61005

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Special Issue Editors

Prof. Dr. Chang-Wook Lee

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Guest Editor

Division of Science Education, Kangwon National University, Chuncheon-si 200-701, Republic of Korea
Interests: radar remote sensing; geoscience education; artificial intelligence; machine learning; natural hazards monitoring
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hyangsun Han

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Guest Editor

Department of Geophysics, Kangwon National University, Chuncheon, Korea
Interests: surface displacements; artificial intelligence; deep learning; ice dynamics; microwave remote sensing

Prof. Dr. Hoonyol Lee

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Guest Editor

Kangwon National University, Chuncheon, Korea
Interests: SAR interferometry; cryosphere; geophysical inversion
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Yu-Chul Park

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Guest Editor

Kangwon National University, Chuncheon, Korea
Interests: modeling; groundwater simulation; GIS technique; hydrological modeling; water resources management

Special Issue Information

Dear Colleagues,

Recently, remote sensing and GIS techniques have gained increasing importance in rapid urbanization, the expansion of urban growth, and the enlargement of populations, due to the application of artificial intelligence, machine learning, and deep learning algorithms. This Special Issue aims to present the state-of-the-art research in optic, SAR, hyperspectral images, and GIS techniques for monitoring urban area environment corresponding to change of times using publicly available and commercial datasets such as satellite and UAV data.

Given the reasons above, the aim of this Special Issue is to present the observation urban area and monitoring surrounding urban area in “Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS”. This research paper will provide readers of Remote Sensing with a wide range of GIS, remote sensing, earth science, computer science, and environmental fields to analyze the urbanization phenomenon along with theoretical research and practical developments. Some of the prospective/encouraged topics for this Issue include:

Remote sensing applications in urban disaster monitoring using AI;
Groundwater monitoring in urban areas;
Fusion of multispectral and SAR image applications;
Hyperspectral image applications in urban area classification;
Natural/artificial disaster monitoring;
Deep/machine learning method algorithms;
Change detection monitoring in urban areas;
UAV/drone image processing and analysis;
Water, river, and lake monitoring in and surrounding urban areas;
Land subsidence, sink holes, and landslide monitoring;
Urban river and stream ice monitoring;
Survey research for citizens’ perceptions of urban disaster.

Prof. Chang-Wook Lee
Prof. Hyangsun Han
Prof. Hoonyol Lee
Prof. Yu-Chul Park
Guest Editor

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Keywords

Artificial intelligence
Machine learning/deep learning
Remote sensing applications
Urban monitoring
Urban disaster
Water monitoring
Multispectral/hyperspectral image
UAV/drone
SAR interferometry
Surface deformation
Chang detection and classification
Big data
Cal/val activities
Survey research

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Published Papers (8 papers)

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34 pages, 17051 KiB

Open AccessArticle

Deep/Transfer Learning with Feature Space Ensemble Networks (FeatSpaceEnsNets) and Average Ensemble Networks (AvgEnsNets) for Change Detection Using DInSAR Sentinel-1 and Optical Sentinel-2 Satellite Data Fusion

by Zainoolabadien Karim and Terence L. van Zyl

Remote Sens. 2021, 13(21), 4394; https://doi.org/10.3390/rs13214394 - 31 Oct 2021

Cited by 4 | Viewed by 3943

Abstract

Differential interferometric synthetic aperture radar (DInSAR), coherence, phase, and displacement are derived from processing SAR images to monitor geological phenomena and urban change. Previously, Sentinel-1 SAR data combined with Sentinel-2 optical imagery has improved classification accuracy in various domains. However, the fusing of Sentinel-1 DInSAR processed imagery with Sentinel-2 optical imagery has not been thoroughly investigated. Thus, we explored this fusion in urban change detection by creating a verified balanced binary classification dataset comprising 1440 blobs. Machine learning models using feature descriptors and non-deep learning classifiers, including a two-layer convolutional neural network (ConvNet2), were used as baselines. Transfer learning by feature extraction (TLFE) using various pre-trained models, deep learning from random initialization, and transfer learning by fine-tuning (TLFT) were all evaluated. We introduce a feature space ensemble family (FeatSpaceEnsNet), an average ensemble family (AvgEnsNet), and a hybrid ensemble family (HybridEnsNet) of TLFE neural networks. The FeatSpaceEnsNets combine TLFE features directly in the feature space using logistic regression. AvgEnsNets combine TLFEs at the decision level by aggregation. HybridEnsNets are a combination of FeatSpaceEnsNets and AvgEnsNets. Several FeatSpaceEnsNets, AvgEnsNets, and HybridEnsNets, comprising a heterogeneous mixture of different depth and architecture models, are defined and evaluated. We show that, in general, TLFE outperforms both TLFT and classic deep learning for the small dataset used and that larger ensembles of TLFE models do not always improve accuracy. The best performing ensemble is an AvgEnsNet (84.862%) comprised of a ResNet50, ResNeXt50, and EfficientNet B4. This was matched by a similarly composed FeatSpaceEnsNet with an F1 score of 0.001 and variance of 0.266 less. The best performing HybridEnsNet had an accuracy of 84.775%. All of the ensembles evaluated outperform the best performing single model, ResNet50 with TLFE (83.751%), except for AvgEnsNet 3, AvgEnsNet 6, and FeatSpaceEnsNet 5. Five of the seven similarly composed FeatSpaceEnsNets outperform the corresponding AvgEnsNet. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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18 pages, 48749 KiB

Open AccessArticle

Mapping Urban Green Spaces at the Metropolitan Level Using Very High Resolution Satellite Imagery and Deep Learning Techniques for Semantic Segmentation

by Roberto E. Huerta, Fabiola D. Yépez, Diego F. Lozano-García, Víctor H. Guerra Cobián, Adrián L. Ferriño Fierro, Héctor de León Gómez, Ricardo A. Cavazos González and Adriana Vargas-Martínez

Remote Sens. 2021, 13(11), 2031; https://doi.org/10.3390/rs13112031 - 21 May 2021

Cited by 31 | Viewed by 9592

Abstract

Urban green spaces (UGSs) provide essential environmental services for the well-being of ecosystems and society. Due to the constant environmental, social, and economic transformations of cities, UGSs pose new challenges for management, particularly in fast-growing metropolitan areas. With technological advancement and the evolution of deep learning, it is possible to optimize the acquisition of UGS inventories through the detection of geometric patterns present in satellite imagery. This research evaluates two deep learning model techniques for semantic segmentation of UGS polygons with the use of different convolutional neural network encoders on the U-Net architecture and very high resolution (VHR) imagery to obtain updated information on UGS polygons at the metropolitan area level. The best model yielded a Dice coefficient of 0.57, IoU of 0.75, recall of 0.80, and kappa coefficient of 0.94 with an overall accuracy of 0.97, which reflects a reliable performance of the network in detecting patterns that make up the varied geometry of UGSs. A complete database of UGS polygons was quantified and categorized by types with location and delimited by municipality, allowing for the standardization of the information at the metropolitan level, which will be useful for comparative analysis with a homogenized and updated database. This is of particular interest to urban planners and UGS decision-makers. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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25 pages, 18259 KiB

Open AccessArticle

Improvement of Earthquake Risk Awareness and Seismic Literacy of Korean Citizens through Earthquake Vulnerability Map from the 2017 Pohang Earthquake, South Korea

by Ju Han, Arip Syaripudin Nur, Mutiara Syifa, Minsu Ha, Chang-Wook Lee and Ki-Young Lee

Remote Sens. 2021, 13(7), 1365; https://doi.org/10.3390/rs13071365 - 2 Apr 2021

Cited by 23 | Viewed by 5543

Abstract

Earthquake activities in and around the Korean Peninsula are relatively low in number and intensity compared with neighboring countries such as Japan and China. However, recent seismic activity caused great alarm and concern among citizens and government authorities, and uncovered the level of preparedness toward earthquake disasters. A survey has been conducted on 1256 participants to investigate the seismic literacy of Korean citizens, including seismic knowledge, awareness and management using a questionnaire of citizen earthquake literacy (CEL). The results declared that the citizens had low awareness and literacy, which means that they are not properly prepared for earthquake hazards. To develop an earthquake risk reduction plan and program efficiently and effectively, not only must it appropriately characterize the target audience, but also indicate high potential earthquake zones and potential earthquake damage. Therefore, this study mapped and analyzed the seismic vulnerability in southeast Korea using LogitBoost, logistic model tree (LMT), and logistic regression (LR) machine learning algorithms based on a building damage inventory map. The damaged buildings’ locations were generated after the 2017 Pohang earthquake using the damage proxy map (DPM) method from the Sentinel-1 synthetic aperture radar (SAR) data. DPMs detected coherence loss, which indicates damaged buildings in urban areas in the Pohang earthquake and shows a good correlation with the Korea Meteorological Administration (KMA) report with modified Mercalli intensity (MMI) scale values of more than VII (seven). The damage locations were randomly divided into two datasets: 50% for training the vulnerability models and 50% for validating the models in terms of accuracy and reliability. Fifteen seismic-related factors were used to construct a model of each algorithm. Model validation based on the area under the receiver operating curve (AUC) was used to determine model accuracy. The AUC values of seismic vulnerability maps using the LogitBoost, LMT, and LR algorithms were 0.769, 0.851, and 0.749, respectively. We suggest that earthquake preparedness efforts should focus on reconstruction, retrofitting, renovation, and seismic education in areas with high seismic vulnerability in South Korea. The results of this study are expected to be beneficial for engineers and policymakers aiming at developing disaster risk reduction plans, policies, and programs due to future seismic activity in South Korea. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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25 pages, 51463 KiB

Open AccessEditor’s ChoiceArticle

Land Subsidence Susceptibility Mapping in Jakarta Using Functional and Meta-Ensemble Machine Learning Algorithm Based on Time-Series InSAR Data

by Wahyu Luqmanul Hakim, Arief Rizqiyanto Achmad and Chang-Wook Lee

Remote Sens. 2020, 12(21), 3627; https://doi.org/10.3390/rs12213627 - 4 Nov 2020

Cited by 65 | Viewed by 9640

Abstract

Areas at risk of land subsidence in Jakarta can be identified using a land subsidence susceptibility map. This study evaluates the quality of a susceptibility map made using functional (logistic regression and multilayer perceptron) and meta-ensemble (AdaBoost and LogitBoost) machine learning algorithms based on a land subsidence inventory map generated using the Sentinel-1 synthetic aperture radar (SAR) dataset from 2017 to 2020. The land subsidence locations were assessed using the time-series interferometry synthetic aperture radar (InSAR) method based on the Stanford Method for Persistent Scatterers (StaMPS) algorithm. The mean vertical deformation maps from ascending and descending tracks were compared and showed a good correlation between displacement patterns. Persistent scatterer points with mean vertical deformation value were randomly divided into two datasets: 50% for training the susceptibility model and 50% for validating the model in terms of accuracy and reliability. Additionally, 14 land subsidence conditioning factors correlated with subsidence occurrence were used to generate land subsidence susceptibility maps from the four algorithms. The receiver operating characteristic (ROC) curve analysis showed that the AdaBoost algorithm has higher subsidence susceptibility prediction accuracy (81.1%) than the multilayer perceptron (80%), logistic regression (79.4%), and LogitBoost (79.1%) algorithms. The land subsidence susceptibility map can be used to mitigate disasters caused by land subsidence in Jakarta, and our method can be applied to other study areas. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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25 pages, 10616 KiB

Open AccessArticle

Integration of InSAR Time-Series Data and GIS to Assess Land Subsidence along Subway Lines in the Seoul Metropolitan Area, South Korea

by Muhammad Fulki Fadhillah, Arief Rizqiyanto Achmad and Chang-Wook Lee

Remote Sens. 2020, 12(21), 3505; https://doi.org/10.3390/rs12213505 - 25 Oct 2020

Cited by 45 | Viewed by 7364

Abstract

The aims of this research were to map and analyze the risk of land subsidence in the Seoul Metropolitan Area, South Korea using satellite interferometric synthetic aperture radar (InSAR) time-series data, and three ensemble machine-learning models, Bagging, LogitBoost, and Multiclass Classifier. Of the types of infrastructure present in the Seoul Metropolitan Area, subway lines may be vulnerable to land subsidence. In this study, we analyzed Persistent Scatterer InSAR time-series data using the Stanford Method for Persistent Scatterers (StaMPS) algorithm to generate a deformation time-series map. Subsidence occurred at four locations, with a deformation rate that ranged from 6–12 mm/year. Subsidence inventory maps were prepared using deformation time-series data from Sentinel-1. Additionally, 10 potential subsidence-related factors were selected and subjected to Geographic Information System analysis. The relationship between each factor and subsidence occurrence was analyzed by using the frequency ratio. Land subsidence susceptibility maps were generated using Bagging, Multiclass Classifier, and LogitBoost models, and map validation was carried out using the area under the curve (AUC) method. Of the three models, Bagging produced the largest AUC (0.883), with LogitBoost and Multiclass Classifier producing AUCs of 0.871 and 0.856, respectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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17 pages, 5894 KiB

Open AccessArticle

Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method

by Sunmin Lee, Jungyoon Jang, Yunjee Kim, Namwook Cho and Moung-Jin Lee

Remote Sens. 2020, 12(16), 2663; https://doi.org/10.3390/rs12162663 - 18 Aug 2020

Cited by 14 | Viewed by 3992

Abstract

Every year, many countries carry out landslide susceptibility analyses to establish and manage countermeasures and reduce the damage caused by landslides. Because increases in the areas of landslides lead to new landslides, there is a growing need for landslide prediction to reduce such damage. Among the various methods for landslide susceptibility analysis, statistical methods require information about the landslide occurrence point. Meanwhile, analysis based on physical slope models can estimate stability by considering the slope characteristics, which can be applied based on information about the locations of landslides. Therefore, in this study, a probabilistic method based on a physical slope model was developed to analyze landslide susceptibility. To this end, an infinite slope model was used as the physical slope model, and Monte Carlo simulation was applied based on landslide inventory including landslide locations, elevation, slope gradient, specific catchment area (SCA), soil thickness, unit weight, cohesion, friction angle, hydraulic conductivity, and rainfall intensity; deterministic analysis was also performed for the comparison. The Mt. Umyeon area, a representative case for urban landslides in South Korea where large scale human damage occurred in 2011, was selected for a case study. The landslide prediction rate and receiver operating characteristic (ROC) curve were used to estimate the prediction accuracy so that we could compare our approach to the deterministic analysis. The landslide prediction rate of the deterministic analysis was 81.55%; in the case of the Monte Carlo simulation, when the failure probabilities were set to 1%, 5%, and 10%, the landslide prediction rates were 95.15%, 91.26%, and 90.29%, respectively, which were higher than the rate of the deterministic analysis. Finally, according to the area under the curve of the ROC curve, the prediction accuracy of the probabilistic model was 73.32%, likely due to the variability and uncertainty in the input variables. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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22 pages, 5054 KiB

Open AccessArticle

Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques

by Sunmin Lee, Yunjung Hyun, Saro Lee and Moung-Jin Lee

Remote Sens. 2020, 12(7), 1200; https://doi.org/10.3390/rs12071200 - 8 Apr 2020

Cited by 112 | Viewed by 13498

Abstract

Adequate groundwater development for the rural population is essential because groundwater is an important source of drinking water and agricultural water. In this study, ensemble models of decision tree-based machine learning algorithms were used with geographic information system (GIS) to map and test groundwater yield potential in Yangpyeong-gun, South Korea. Groundwater control factors derived from remote sensing data were used for mapping, including nine topographic factors, two hydrological factors, forest type, soil material, land use, and two geological factors. A total of 53 well locations with both specific capacity (SPC) data and transmissivity (T) data were selected and randomly divided into two classes for model training (70%) and testing (30%). First, the frequency ratio (FR) was calculated for SPC and T, and then the boosted classification tree (BCT) method of the machine learning model was applied. In addition, an ensemble model, FR-BCT, was applied to generate and compare groundwater potential maps. Model performance was evaluated using the receiver operating characteristic (ROC) method. To test the model, the area under the ROC curve was calculated; the curve for the predicted dataset of SPC showed values of 80.48% and 87.75% for the BCT and FR-BCT models, respectively. The accuracy rates from T were 72.27% and 81.49% for the BCT and FR-BCT models, respectively. Both the BCT and FR-BCT models measured the contributions of individual groundwater control factors, which showed that soil was the most influential factor. The machine learning techniques used in this study showed effective modeling of groundwater potential in areas where data are relatively scarce. The results of this study may be used for sustainable development of groundwater resources by identifying areas of high groundwater potential. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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18 pages, 10063 KiB

Open AccessLetter

Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)

by Paul D. Rosero-Montalvo, Vivian F. López-Batista, Jaime A. Riascos and Diego H. Peluffo-Ordóñez

Remote Sens. 2020, 12(12), 1988; https://doi.org/10.3390/rs12121988 - 20 Jun 2020

Cited by 10 | Viewed by 4302

Abstract

This work presents a wireless sensor network (WSN) system able to determine the water quality of rivers. Particularly, we consider the Tahuando River from Ibarra, Ecuador, as a case study. The main goal of this research is to determine the river’s status throughout its route, by generating data reports into an interactive user interface. To this end, we use an array of sensors collecting several measures such as: turbidity, temperature, water quality, pH, and temperature. Subsequently, from the information collected on an Internet-of-Things (IoT) server, we develop a data analysis scheme with both data representation and supervised classification. As an important result, our system outputs a map that shows the contamination levels of the river at different regions. Furthermore, in terms of data analysis performance, the proposed system reduces the data matrix by 97% from its original size, while it reaches a classification performance over 90%. Furthermore, as an additional remarkable result, we here introduce the so-called quantitative metric of balance (QMB), which measures the balance or ratio between performance and power consumption. Full article

(This article belongs to the Special Issue Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS)

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