Special Issue "Artificial Intelligence Methods Applied to Urban Remote Sensing and GIS"

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

Deadline for manuscript submissions: 30 November 2020.

Special Issue Editors

Prof. Chang-Wook Lee
Website
Guest Editor
Kangwon National University, Chuncheon, Korea
Interests: radar remote sensing; geoscience education; artificial intelligence; machine learning; natural hazards monitoring
Prof. Hyangsun Han
Website
Guest Editor
Unit of Arctic Sea-Ice Prediction, Kangwon National University, Chuncheon, Korea
Interests: surface displacements; artificial intelligence; deep learning; ice dynamics; microwave remote sensing
Prof. Hoonyol Lee
Website
Guest Editor
Kangwon National University, Chuncheon, Korea
Interests: SAR interferometry; cryosphere; geophysical inversion
Special Issues and Collections in MDPI journals
Prof. Yu-Chul Park
Website
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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Remote Sensing 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 2200 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

  • 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

Published Papers (3 papers)

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Research

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Open AccessArticle
Susceptibility Analysis of the Mt. Umyeon Landslide Area Using a Physical Slope Model and Probabilistic Method
Remote Sens. 2020, 12(16), 2663; https://doi.org/10.3390/rs12162663 - 18 Aug 2020
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 [...] Read more.
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
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Open AccessArticle
Groundwater Potential Mapping Using Remote Sensing and GIS-Based Machine Learning Techniques
Remote Sens. 2020, 12(7), 1200; https://doi.org/10.3390/rs12071200 - 08 Apr 2020
Cited by 2
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 [...] Read more.
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
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Other

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Open AccessLetter
Intelligent WSN System for Water Quality Analysis Using Machine Learning Algorithms: A Case Study (Tahuando River from Ecuador)
Remote Sens. 2020, 12(12), 1988; https://doi.org/10.3390/rs12121988 - 20 Jun 2020
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 [...] Read more.
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
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Analysis of survey research for citizens’ perception by artificial intelligence from Pohang earthquake hazard in Korea
Authors: Mutiara Syifa; Mahdi Panahi,; Ki-Young Lee; Chang‐Wook Lee; Ju Han
Affiliation: Kangwon National University

Title: Application of artificial intelligence on time-series displacement of land subsidence along Seoul subway using InSAR and GIS technique
Authors: Arief Rizqiyanto Achmad; Mahdi Panahi; Sungjae Park; Chang‐Wook Lee
Affiliation: Kangwon National University

Title: Time-series analysis for reclaimed land subsidence using InSAR and GIS method by machine learning approaching in Noksan complex area
Authors: Seulik Lee; Mahdi Panahi; Sungjae Park; Chang‐Wook Lee
Affiliation: Kangwon National University

Title: Groundwater Potential Mapping Using Remote Sensing- and GIS-based Machine Learning
Authors: Sunmin Lee,; Yunjung Hyun; Saro Lee; Moung-Jin Lee
Affiliation: University of Seoul

Title: Change detection monitoring in urban areas; Deep/machine learning method algorithms
Authors: Hynagsun Han; Hoonyol Lee
Affiliation: Unit of Arctic Sea-Ice Prediction, Kangwon National University, Chuncheon, Korea

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