Intelligent Methods and Deep Learning Advances with Multimodal Remote Sensing Data for Environmental Hazard Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".
Deadline for manuscript submissions: 31 May 2026 | Viewed by 1181
Special Issue Editors
Interests: remote sensing data intelligent processing; machine learning; intelligent identification of geological hazards
Interests: low-altitude UAV photogrammetry; combined camera data preprocessing
Interests: geological hazards identification and monitoring; remote sensing intelligent object recognition
Interests: hyperspectral analysis; land cover classification; machine learning; superresolution enhancement
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of artificial intelligence, intelligent methods—particularly deep learning and its emerging paradigms—are reshaping the field of environmental hazard identification, monitoring, etc. Traditional remote sensing tasks such as classification, detection, semantic segmentation, change detection, and 3D reconstruction are being revolutionized by the introduction of optical, hyperspectral, SAR, and LiDAR data; large-scale foundation models; and advanced neural architectures, which represents a significant opportunity to improve the identification and monitoring of environmental hazards using these AI methods and multimodal data.
This Special Issue aims to bring together the latest research on intelligent algorithms tailored to multimodal remote sensing (optical, hyperspectral, SAR, LiDAR, etc.) applications in environmental hazards. It will highlight both methodological innovations and novel applications that demonstrate the potential of intelligent algorithms to tackle core challenges with multimodal remote sensing data. Contributions addressing multimodal data (optical, hyperspectral, SAR, LiDAR etc.) integration, foundation and advanced large AI models, and applications for environmental hazard identification and monitoring are particularly welcome.
Suggested topics include, but are not limited to, the following:
- Intelligent algorithms for multimodal remote sensing applications for environmental hazard identification and monitoring;
- Multimodal remote sensing data (optical, hyperspectral, SAR, LiDAR, thermal, and social sensing data) used for classification, super-resolution enhancement, etc.;
- Advanced neural architectures (transformers, diffusion models, graph neural networks, and MoE structures) for remote sensing analysis;
- Foundation models, pre-training, and transfer learning for Earth observation;
- Physics-aware AI for environmental hazard identification and monitoring;
- Efficient and scalable computing strategies (edge cloud, distributed learning, and lightweight models) for large-scale Earth observation.
This Special Issue welcomes original research papers, review articles, and methodological contributions. By focusing on intelligent algorithms and deep learning advances for multimodal remote sensing data (optical, hyperspectral, SAR, LiDAR) and applications, especially for environmental hazard identification and monitoring, it seeks to foster cross-disciplinary collaboration and accelerate the methodological progress of remote sensing research on environmental hazard applications in the era of artificial intelligence.
Dr. Jiangbo Xi
Dr. Chaofeng Ren
Dr. Qiong Wu
Prof. Dr. Jonathan C-W Chan
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
- environmental hazard
- intelligent algorithms
- deep learning
- multimodal data
- hyperspectral data
- foundation models
- interpretable AI
- self-supervised learning
- change detection
- 3D reconstruction
- AI4EO (artificial intelligence for earth observation)
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