GIS, InSAR, and Deep Learning in Earth Hazard Monitoring

A special issue of Geosciences (ISSN 2076-3263). This special issue belongs to the section "Natural Hazards".

Deadline for manuscript submissions: 31 March 2026

Special Issue Editors

National Institute of Natural Hazards, Ministry of Emergency Management of China, Beijing 100085, China
Interests: remote sensing; GIS; deep learning; InSAR; geological hazards
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Guest Editor
Department of Earth and Environmental Sciences, University of Michigan, Ann Arbor, MI, USA
Interests: landslides; engineering geology; remote sensing; UAV photogrammetry; 3D analysis
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Guest Editor
School of Surveying and Geoinformation Engineering, East China University of Technology, Nanchang, China
Interests: remote sensing; InSAR; natural hazards monitoring

Special Issue Information

Dear Colleagues,

Geohazards, encompassing phenomena like earthquakes, volcanoes, landslides, floods, and subsidence, pose significant and growing threats to society and critical infrastructure. The effective characterization and continuous monitoring of these Earth processes are paramount for risk assessment, mitigation, and ensuring public safety. This special issue focuses on the transformative convergence of three powerful technologies—Geographic Information Systems (GIS), Interferometric Synthetic Aperture Radar (InSAR), and deep learning—in the field of Earth hazard monitoring.

We invite manuscripts that explore the synergy of these advanced technologies. Submissions demonstrating new applications for hazard characterization, early warning, and dynamic risk assessment are particularly encouraged, aiming to showcase the frontier of data-driven approaches for building a more resilient and safer world. Review papers will also be considered.

The topics for this Special Issue include, but are not limited to the following:

  • GIS-driven multi-hazard mapping and exposure analysis
  • Time-series InSAR for deformation monitoring and failure forecasting
  • Advanced deep learning for disaster detection, monitoring, and prediction
  • Dynamic geohazard risk assessment combining multi-source technologies with observation data
  • Combining multi-source technologies to improve the efficiency of data processing and analysis
  • Benchmark datasets, open-source tools, and reproducible workflows

Dr. Yaning Yi
Dr. Stratis Karantanellis
Dr. Guangyu Xu
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. Geosciences is an international peer-reviewed open access monthly 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 1800 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

  • geohazards
  • disaster monitoring
  • risk assessment
  • GIS
  • remote sensing
  • SAR/InSAR
  • deep learning
  • artificial intelligence

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Published Papers

This special issue is now open for submission.
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