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Geospatial Infrastructure Management Ecosystem (GeoIME) and Digital Geological Mapping

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 6735

Special Issue Editors


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Guest Editor
1. Department of Surveying and Geoinformatics, Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, The Western Park of the Hi-Tech Industrial Development Zone, Chengdu 611756, China
2. Mobile Sensing and Geodata Science Lab, Department of Geography and Environmental Management, University of Waterloo, Waterloo, ON, Canada
Interests: LiDAR processing and applications; drone, remote sensing and GIS of geoenvironmental hazards and disaster management; sustainable development goals (SDGs) 2030; development of algorithms; models; software; GeoAI; Web App, Geo App and smart mapping, and application beyond
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, The Western Park of the Hi-Tech Industrial Development Zone, Chengdu 611756, Sichuan, China
Interests: In-SAR satellite data processing; InSAR monitoring earthquake; landslide and rock-glacier; deformation changes monitoring; ground subsidence of high-speed railway; GNSS; UAV

E-Mail Website
Guest Editor
Geospatial Sensing and Data Intelligence Lab, Faculty of Environment, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
Interests: LiDAR remote sensing; point cloud understanding; deep learning; 3D vision; HD maps for smart cities and autonomous vehicles
Special Issues, Collections and Topics in MDPI journals

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Associate Guest Editor
Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, The Western Park of the Hi-Tech Industrial Development Zone, Chengdu 611756, Sichuan, China
Interests: AI; machine learning; computer vision; image processing; deep learning; mobile and web applications development; geospatial information for disaster risk reduction

Special Issue Information

Dear Colleagues,

Geospatial information technologies have been increasingly used in various disciplines and professions, including digital geological mapping, disaster management, and risk reduction. In July 2018, the United Nations adopted a resolution stressing the implementation of geospatial information for disaster risk reduction. 

In this regard, we proposed the topic of the Geospatial Infrastructure Management Ecosystem (GeoIME) and digital geological mapping to the Remote Sensing Journal for a Special Issue. This Special Issue focuses on Geospatial Artificial Intelligence (GeoAI) and the Internet of Things (IoT), including seamlessly cloud-based platforms as well as the integration of technologies to improve pre- and post-disaster management and how to determine vulnerability and estimation of risk of infrastructures such as buildings, bridges, and roads, etc.

The expected outcomes of this Special Issue are to put together ideas in implementing Geospatial information for building a better environment and ecosystem for smart cities and the future digital world. 

Everyone in various disciplines, including remote sensing computer science and environment, GeoAI, LiDAR processing, geological engineering, geoinformatics for natural hazards and disasters management, GIS, surveying and geodesy, structure and earthquake, civil engineering are welcome to submit research or technical papers to this Special Issue. 

Dr. Saeid Pirasteh
Dr. Guoxiang Liu
Dr. Jonathan Li
Ms. Ghazal Shamsipour
Guest Editors

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 submissions that pass pre-check are 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 2700 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

  • Geological engineering
  • Geospatial information
  • Remote Sensing applications-image processing
  • Natural hazards
  • Pre and post-disaster management
  • GeoAI and Smart mapping
  • Internet of Things
  • Cloud-based
  • LiDAR
  • GIS
  • Web Apps
  • Geo Apps

Published Papers (2 papers)

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22 pages, 10837 KiB  
Article
Incorporating Persistent Scatterer Interferometry and Radon Anomaly to Understand the Anar Fault Mechanism and Observing New Evidence of Intensified Activity
by Ali Mehrabi, Saied Pirasteh, Ahmad Rashidi, Mohsen Pourkhosravani, Reza Derakhshani, Guoxiang Liu, Wenfei Mao and Wei Xiang
Remote Sens. 2021, 13(11), 2072; https://doi.org/10.3390/rs13112072 - 24 May 2021
Cited by 20 | Viewed by 2557
Abstract
Interferometric Synthetic Aperture Radar (InSAR) monitors surface change and displacement over a large area with millimeter-level precision and meter-level resolution. Anar fault, with a length of ~200 km, is located in central Iran. Recent seismological studies on the fault indicated that it is [...] Read more.
Interferometric Synthetic Aperture Radar (InSAR) monitors surface change and displacement over a large area with millimeter-level precision and meter-level resolution. Anar fault, with a length of ~200 km, is located in central Iran. Recent seismological studies on the fault indicated that it is approaching the end of its seismic cycle. Although a large earthquake is imminent, the mechanism of the fault is not well understood. Therefore, understanding and discovering the mechanism of Anar fault remains a challenge. Here, we present an approach of displacement fault analysis utilizing a combination of InSAR data obtained from the persistent scatterer interferometry (PSI) method and 178 Sentinel-1 images (ascending and descending) (2017–2020). We incorporated groundwater samples from 40 wells, radon concentration anomaly mapping, Global Positioning System (GPS), and 3D displacement measurement acquired over four years (2016–2020). We investigated and monitored the deformation of the fault plate’s behavior over the last three years (2017–2020) to explore new evidence and signature of displacement. The results show that the time series analysis in the fault range has an increasing displacement rate in all dimensions. We observed that the line-of-sight (LOS) displacement rate varied from −15 mm to 5 mm per year. Our calculations show that the E–W, N–S, and vertical displacement rates of the fault blocks are 2 mm to −2 mm, 6 mm to −6 mm, and 2 mm to −4 mm per year, respectively. An anomaly map of the radon concentration shows that the complete alignment of the high concentration ranges with the fault strike and the radon concentration increased on average from 23.85 Bq/L to 25.30 Bq/L over these three years. Therefore, we predict rising the radon concentration is due to the increase in activity which resulted in a deformation. Finally, our findings show that the Anar fault is an oblique and right-lateral strike-slip with a normal component mechanism. We validated the proposed method and our results by comparing the GPS field data and PSI measurements. The root mean square error (RMSE) of the PSI measurement is estimated to be 0.142 mm. Based on the supporting evidence and signature, we conclude that the Anar fault activity increased between 2017 and 2020. Full article
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17 pages, 13939 KiB  
Technical Note
Automated Mapping of Ms 7.0 Jiuzhaigou Earthquake (China) Post-Disaster Landslides Based on High-Resolution UAV Imagery
by Rubing Liang, Keren Dai, Xianlin Shi, Bin Guo, Xiujun Dong, Feng Liang, Roberto Tomás, Ningling Wen and Xuanmei Fan
Remote Sens. 2021, 13(7), 1330; https://doi.org/10.3390/rs13071330 - 31 Mar 2021
Cited by 12 | Viewed by 3129
Abstract
The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of [...] Read more.
The Ms 7.0 Jiuzhaigou earthquake that occurred on 8 August 2017 triggered hundreds of landslides in the Jiuzhaigou valley scenic and historic-interest area in Sichuan, China, causing heavy casualties and serious property losses. Quick and accurate mapping of post-disaster landslide distribution is of paramount importance for earthquake emergency rescue and the analysis of post-seismic landslides distribution characteristics. The automatic identification of landslides is mostly based on medium- and low-resolution satellite-borne optical remote-sensing imageries, and the high-accuracy interpretation of earthquake-triggered landslides still relies on time-consuming manual interpretation. This paper describes a methodology based on the use of 1 m high-resolution unmanned aerial vehicle (UAV) imagery acquired after the earthquake, and proposes a support vector machine (SVM) classification method combining the roads and villages mask from pre-seismic remote sensing imagery to accurately and automatically map the landslide inventory. Compared with the results of manual visual interpretation, the automatic recognition accuracy could reach 99.89%, and the Kappa coefficient was higher than 0.9, suggesting that the proposed method and 1 m high-resolution UAV imagery greatly improved the mapping accuracy of the landslide area. We also analyzed the spatial-distribution characteristics of earthquake-triggered landslides with the influenced factors of altitude, slope gradient, slope aspect, and the nearest faults, which provided important support for the further study of post-disaster landslide distribution characteristics, susceptibility prediction, and risk assessment. Full article
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