Advanced Hyperspectral Imaging and AI for Geological Applications
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 1373
Editors
Interests: hyperspectral remote sensing for geological environment; digital earth; big data computing
Special Issues, Collections and Topics in MDPI journals
Interests: hyperspectral remote sensing information processing and application; geologic hazard
Interests: remote sensing technology and its applications in geoscience
Special Issue Information
Dear Colleagues,
Hyperspectral imaging (HSI) has emerged as a transformative tool for geological studies, enabling the detailed identification of mineral compositions, surface alterations, and environmental changes. Coupled with artificial intelligence (AI), HSI has unprecedented capabilities for automating and enhancing the interpretation of complex geological features. This Special Issue seeks to highlight cutting-edge research and applications of HSI and AI in geology, fostering advancements in resource exploration, hazard monitoring, and sustainable land management.
We welcome contributions that address innovative methodologies, algorithms, and case studies leveraging HSI and AI for geological purposes. Topics of interest include, but are not limited to, the following:
- Mineral and resource exploration: The AI-driven detection of ore deposits, alteration zones, and critical minerals.
- Geohazard monitoring: The identification of landslides, debris flow, subsidence, and earthquake precursors using HSI.
- Environmental geology: The mapping of soil contamination, weathering processes, and anthropogenic impacts.
- Sensor and data fusion: The integration of HSI with LiDAR, multispectral, or SAR data for improved geological interpretation.
- Machine learning/deep learning: Novel algorithms for feature extraction, classification, and anomaly detection in HSI data.
- Field applications: Case studies demonstrating HSI’s utility in mining, tectonics, or disaster risk reduction.
This Special Issue aims to bridge the gap between theoretical advancements and practical solutions, providing a platform for researchers to share their insights on how HSI and AI can address pressing geological challenges. Submissions addressing their scalability, validation, and real-world implementation are particularly encouraged.
Prof. Dr. Lizhe Wang
Dr. Yingjun Zhao
Dr. Fuping Gan
Dr. Ruyi Feng
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.
Keywords
- hyperspectral imaging
- artificial intelligence (AI)
- machine learning
- mineral mapping
- ore deposit prediction
- geological exploration
- landslide detection
- environmental geology
- anomaly detection
- critical minerals
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