Journal Browser

Journal Browser

Special Issue "Technological Advancements in Disaster Damage Assessment Using Earth Observation, Machine Learning, and Numerical Simulation"

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

Deadline for manuscript submissions: 31 December 2023 | Viewed by 2168

Special Issue Editors

International Research Institute of Disaster Science (IRIDeS), Tohoku University, Sendai 980-8572, Japan
Interests: remote sensing; machine learning; numerical simulation; disaster science
Special Issues, Collections and Topics in MDPI journals
International Research Institute of Disaster Science, Tohoku University, Sendai 980-8572, Japan
Interests: multi-agent systems and agent-based simulation; tsunami simulation; evacuation simulation; remote sensing
Special Issues, Collections and Topics in MDPI journals
Civil Engineering Division, Pontifical Catholic University of Peru, Lima 15088, Peru
Interests: remote sensing; machine learning; intelligent evacuation systems; natural hazards; disaster management; earthquake engineering; informal urban growth
Graduate School of Advanced Science and Engineering, Hiroshima University, Higashi-Hiroshima 739-8527, Japan
Interests: earthquake engineering; geospatial analysis for damage assessment; remote sensing for disaster response; DEM analysis for geomorphology
Special Issues, Collections and Topics in MDPI journals
Japan Peru Center for Earthquake Engineering Research and Disaster Mitigation, Faculty of Civil Engineering, National University of Engineering, Lima 15333, Peru
Interests: remote sensing; geographic information systems; digital image processing; catastral information; damage assessment; internet of things; machine learning; infrastructure risk assessment

Special Issue Information

Dear Colleagues,

The combination of remote sensing, physics-based numerical simulation, and advanced machine learning technologies is instrumental in understanding several aspects of the Earth's ever-changing surface. Mainly, disaster science research has seen considerable attention, yielding novel methodologies for rapid post-disaster damage assessments and an accurate understanding of hazard scenarios before disasters. On the one hand, integrating numerical modeling and machine learning technologies allows us to analyze several Earth phenomena, such as ground deformations, growing urban environments, and local-site characterization. Furthermore, earth observation technologies, such as optical imaging and radar-based sensing, can capture changes before and after disasters. However, complex and unique disaster conditions induced by earthquakes, heavy rain, and other natural phenomena present significant challenges due to several factors, such as data accessibility, missing information, and high computation cost. Thus, this Special Issue explores the theory and combined application of numerical modeling, machine learning, and remote sensing technologies for disaster damage and loss assessments.

This Special Issue is open to all contributions on recent advances and novel developments of methodologies and best-case study application of computer simulation, remote sensing, and machine to earthquakes, tsunamis, volcanic, and flooding events. We encourage submissions of both review and original research articles related, but not limited, to the following topics:

  • Analysis of changes in urban environment;
  • Damage recognition and mapping;
  • Machine learning for disaster research;
  • Detection and classification of building damage;
  • Extraction and mapping of flooded areas;
  • Time-series analysis of surface deformations;
  • Open data and big data for multi-hazard analysis;
  • Natural hazard modeling and prediction.

Dr. Bruno Adriano
Dr. Erick Mas
Dr. Luis Angel Moya Huallpa
Dr. Hiroyuki Miura
Prof. Dr. Miguel Estrada
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at 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.


  • earthquake damage
  • tsunami events
  • flood damage
  • loss estimation
  • optical imaging
  • synthetic aperture radar
  • machine learning

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:


20 pages, 7174 KiB  
Contemporaneous Thick- and Thin-Skinned Seismotectonics in the External Zagros: The Case of the 2021 Fin Doublet, Iran
Remote Sens. 2023, 15(12), 2981; - 07 Jun 2023
Cited by 1 | Viewed by 825
In this work, we propose a geodetic model for the seismic sequence, with doublet earthquakes, that occurred in Bandar Abbas, Iran, in November 2021. A dataset of Sentinel-1 images, processed using the InSAR (Interferometric Synthetic Aperture Radar) technique, was employed to identify the [...] Read more.
In this work, we propose a geodetic model for the seismic sequence, with doublet earthquakes, that occurred in Bandar Abbas, Iran, in November 2021. A dataset of Sentinel-1 images, processed using the InSAR (Interferometric Synthetic Aperture Radar) technique, was employed to identify the surface deformation caused by the major events of the sequence and to constrain their geometry and kinematics using seismological constraints. A Coulomb stress transfer analysis was also applied to investigate the sequence’s structural evolution in space and time. A linear inversion of the InSAR data provided a non-uniform distribution of slip over the fault planes. We also performed an accurate relocation of foreshocks and aftershocks recorded by locally established seismographs, thereby allowing us to determine the compressional tectonic stress regime affecting the crustal volume. Despite the very short time span of the sequence, our results clearly suggest that distinct blind structures that were previously unknown or only suspected were the causative faults. The first Mw 6.0 earthquake occurred on an NNE-dipping, intermediate-angle, reverse-oblique plane, while the Mw 6.4 earthquake occurred on almost horizontal or very low-angle (SSE-dipping) reverse segments with top-to-the-south kinematics. The former, which cut through and displaced the Pan-African pre-Palaeozoic basement, indicates a thick-skinned tectonic style, while the latter rupture(s), which occurred within the Palaeozoic–Cenozoic sedimentary succession and likely exploited the stratigraphic mechanical discontinuities, clearly depicts a thin-skinned style. Full article
Show Figures

Graphical abstract

Back to TopTop