Multimodal Remote Sensing and Artificial Intelligence for Natural Disasters Monitoring, Early Warning, and Risk Management

Dear Colleagues,

Natural disasters, including floods, landslides, earthquakes, mining subsidence, wildfires, droughts, tropical cyclones, volcanic eruptions, and extreme climate events, are increasing in frequency, intensity, and complexity under global environmental change. Timely and accurate disaster monitoring, early warning, impact assessment, and risk management are therefore essential for protecting lives, infrastructure, ecosystems, and socioeconomic systems. Recent advances in multimodal remote sensing—such as optical, SAR, LiDAR, thermal, hyperspectral, UAV, GNSS, meteorological, hydrological, and in situ observations—together with rapid progress in artificial intelligence, offer unprecedented opportunities to improve disaster-related information extraction, prediction, and decision support.

This Special Issue aims to provide a platform for innovative studies that integrate multimodal data and AI techniques for natural disaster monitoring, early warning, emergency response, and risk management. The topic is closely aligned with the scope of Remote Sensing, particularly in advancing sensor-based Earth observation, data fusion, intelligent interpretation, spatiotemporal analysis, and operational applications for environmental and societal challenges.

We welcome original research articles, review papers, communications, technical notes, data descriptors, and case studies. Potential topics include, but are not limited to, the following: multimodal data fusion for disaster detection and assessment; deep learning, machine learning, foundation models, and explainable AI for hazard mapping; near-real-time disaster monitoring and forecasting; early warning systems; remote sensing of floods, landslides, earthquakes, wildfires, droughts, storms, volcanic and coastal hazards; vulnerability, exposure, and risk assessment; damage mapping and post-disaster recovery monitoring; uncertainty analysis; transfer learning and domain adaptation; benchmark datasets; and decision-support frameworks for disaster resilience.

Prof. Dr. Yu Chen
Dr. Bingqian Chen
Prof. Dr. Jie Dou
Dr. Teng Wang
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-anonymized 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.