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Remote Sensing
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Remote Sensing and GeoAI in Natural Hazard Assessment: Emerging Trends...
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Remote Sensing and GeoAI in Natural Hazard Assessment: Emerging Trends and Applications

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

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2346

Special Issue Editors

Dr. Husam A. H. Al-Najjar

E-Mail Website
Guest Editor
School of Computer Science, Faculty of Engineering and IT, University of Technology Sydney, Sydney 2007, Australia
Interests: GeoAI; damage assessment; natural hazards; landslides; wildfire; machine/deep learning
Special Issues, Collections and Topics in MDPI journals
Dr. Bahareh Kalantar

E-Mail Website
Guest Editor
RIKEN Center for Advanced Intelligence Project, Goal-Oriented Technology Research Group, Disaster Resilience Science Team, Tokyo 103-0027, Japan
Interests: remote sensing; machine learning algorithms; natural hazard modeling applied machine learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Alfian Abdul Halin

E-Mail Website
Guest Editor
Department of Multimedia, Faculty of Computer Science & Information Technology, Universiti Putra Malaysia, Serdang 43400, Malaysia
Interests: applied machine learning; computer vision; image processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Natural hazards such as floods, landslides, earthquakes, erosion, wildfires, and droughts continue to pose significant risks to human societies and the environment. As climate change exacerbates these hazards, improving their assessment and management is becoming increasingly important. Recent advancements in remote sensing, GeoAI (geospatial artificial intelligence), and machine learning techniques are playing a pivotal role in the real-time monitoring, prediction, and analysis of these natural disasters. These innovations enable more accurate hazard prediction, vulnerability mapping, and damage assessment, leading to more efficient emergency response strategies, disaster risk reduction, and resilience-building. 

This Special Issue aims to showcase the latest developments in the integration of remote sensing and GeoAI, including the application of machine learning and deep learning models for natural hazard assessment. It seeks to demonstrate the utility of these technologies to improve hazard monitoring, enhance early warning systems, and optimize disaster response strategies. This Special Issue aligns with the journal’s focus on advancing geospatial technologies, Earth observation, and AI methodologies for addressing pressing environmental challenges and disaster management issues. 

We invite original research articles, reviews, and case studies that address the following themes:

  • Machine learning/deep learning applications in natural hazard detection and assessment.
  • The integration of remote sensing and GeoAI (including AI-based models) for improved hazard prediction and monitoring.
  • The fusion of various datasets (e.g., SAR, optical, LiDAR, and UAV imagery) for enhanced hazard analysis.
  • The remote sensing-based monitoring of specific hazards such as floods, landslides, wildfires, and earthquakes.
  • GeoAI, machine learning, and deep learning applications in vulnerability assessment and risk mapping.
  • Explainable AI (XAI) models in geohazard applications to improve the transparency and interpretability of AI-based predictions.
  • Earth observation technologies for improving disaster management and emergency response.
  • Case studies demonstrating the use of machine learning, deep learning, or XAI models in geohazard assessment.

Dr. Husam A. H. Al-Najjar
Dr. Bahareh Kalantar
Dr. Alfian Abdul Halin
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 250 words) can be sent to the Editorial Office for assessment.

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

  • natural hazards (floods, landslides, earthquakes, erosion, wildfires, droughts, etc.)
  • remote sensing
  • GeoAI (geospatial artificial intelligence)
  • damage assessment
  • data fusion (e.g., SAR, optical, LiDAR)
  • intelligent geohazard computation
  • explainable AI (XAI) for geohazards
  • hazard and risk assessment
  • Earth observation
  • vulnerability mapping

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 36594 KB  
Article
Deformation Prediction and Potential Landslide Identification in the Upstream of Sarez Lake Based on Time Series InSAR and Stacked LSTM
by Hang Zhu, Qian Shen, Junli Li, Majid Gulayozov, Yakui Shao, Bingqian Chen and Changming Zhu
Remote Sens. 2026, 18(5), 811; https://doi.org/10.3390/rs18050811 - 6 Mar 2026
Viewed by 553
Abstract
The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric [...] Read more.
The identification of potential landslides and targeted risk analysis is crucial for the warning and prevention of geological landslide disasters. This article presents a time series deformation prediction framework based on a Long Short-Term Memory (LSTM) network deep learning model for analyzing Interferometric Synthetic Aperture Radar (InSAR) data. By employing an advanced stacked LSTM network model, we effectively capture temporal dependencies and move beyond traditional methods that depend on explicit deformation. This approach enables short- to medium-term deformation prediction through structured time dynamic modeling, identifies potential landslide targets in the high-altitude regions upstream of Lake Sarez, and classifies associated risk levels. The results indicate that: (1) In short-term forecasting, the stacked LSTM model effectively captures trend turning points, producing stable and reliable predictions with a Mean Absolute Error (MAE) of 0.164 mm and a Root Mean Square Error (RMSE) of 0.194 mm; (2) From 2019 to 2022, regional surface deformation characteristics exhibited significant spatial heterogeneity, with the potential landslide on the right bank identified as the most critical settlement center, demonstrating a line of sight (LOS) deformation rate consistently exceeding 49 mm per year, while the Usoi Dam displayed relatively good stability during this period; (3) By integrating InSAR deformation rate maps with Sentinel-2 optical images, we identified a total of 72 potential landslide targets in the region, four of which exhibited deformation rates exceeding −30 mm per year, indicating significant activity and classifying them as high-risk areas requiring attention. This provides a targeted reference list for the prevention and control of geological landslides around Lake Sarez and establishes a reliable technical pathway for the early identification of landslides under complex geological conditions in high-altitude mountainous areas. Full article
(This article belongs to the Special Issue Remote Sensing and GeoAI in Natural Hazard Assessment: Emerging Trends and Applications)
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19 pages, 9385 KB  
Article
YOLOv11-MDD: YOLOv11 in an Encoder–Decoder Architecture for Multi-Label Post-Wildfire Damage Detection—A Case Study of the 2023 US and Canada Wildfires
by Masoomeh Gomroki, Negar Zahedi, Majid Jahangiri, Bahareh Kalantar and Husam Al-Najjar
Remote Sens. 2026, 18(2), 280; https://doi.org/10.3390/rs18020280 - 15 Jan 2026
Viewed by 735
Abstract
Natural disasters occur worldwide and cause significant financial and human losses. Wildfires are among the most important natural disasters, occurring more frequently in recent years due to global warming. Fast and accurate post-disaster damage detection could play an essential role in swift rescue [...] Read more.
Natural disasters occur worldwide and cause significant financial and human losses. Wildfires are among the most important natural disasters, occurring more frequently in recent years due to global warming. Fast and accurate post-disaster damage detection could play an essential role in swift rescue planning and operations. Remote sensing (RS) data is an important source for tracking damage detection. Deep learning (DL) methods, as efficient tools, can extract valuable information from RS data to generate an accurate damage map for future operations. The present study proposes an encoder–decoder architecture composed of pre-trained Yolov11 blocks as the encoder path and Modified UNet (MUNet) blocks as the decoder path. The proposed network includes three main steps: (1) pre-processing, (2) network training, (3) prediction multilabel damage map and accuracy evaluation. To evaluate the network’s performance, the US and Canada datasets were considered. The datasets are satellite images of the 2023 wildfires in the US and Canada. The proposed method reaches the Overall Accuracy (OA) of 97.36, 97.47, and Kappa Coefficient (KC) of 0.96, 0.87 for the US and Canada 2023 wildfire datasets, respectively. Regarding the high OA and KC, an accurate final burnt map can be generated to assist in rescue and recovery efforts after the wildfire. The proposed YOLOv11–MUNet framework introduces an efficient and accurate post-event-only approach for wildfire damage detection. By overcoming the dependency on pre-event imagery and reducing model complexity, this method enhances the applicability of DL in rapid post-disaster assessment and management. Full article
(This article belongs to the Special Issue Remote Sensing and GeoAI in Natural Hazard Assessment: Emerging Trends and Applications)
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