Sustainable Environmental Solutions Based on Advanced Geospatial Technologies
A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Urban and Rural Development".
Deadline for manuscript submissions: 27 May 2026 | Viewed by 11
Special Issue Editors
Interests: remote sensing; deep learning; mineral resources; natural hazards; environmental pollution monitoring
Interests: remote sensing image processing and interpretation; remote sensing of environment; synthetic aperture radar; target detection on remote sensing images; image denoising; deep learning; computer vision
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Escalating climate and anthropogenic pressures necessitate advanced environmental monitoring. Remote sensing (RS) now delivers unprecedented multi-scale geospatial data, while Geographic Information Systems (GIS) enable robust spatial analysis. The integration of deep learning (DL) with RS/GIS revolutionizes high-precision pattern extraction beyond traditional methods.
This Special Issue, "Sustainable Environmental Solutions based on Advanced Geospatial Technologies", seeks cutting-edge research leveraging AI-enhanced technologies to model spatiotemporal patterns of Earth's critical resources and environments, and improve disaster (especially geohazards) monitoring, early warning, and risk evaluation, supporting sustainable development. Potential topics may include (but are not limited to) the following:
(1) Model spatiotemporal dynamics of Earth’s critical resources (forests/water/soils) and multi-sphere interactions;
(2) Innovate geohazard early warning and prediction systems (floods, wildfires, landslides) via multi-source data fusion;
(3) Track environmental pollution using multispectral/hyperspectral/SAR imagery;
(4) Quantify carbon fluxes through atmosphere–biosphere–hydrosphere coupling.
We prioritize DL innovations (e.g., CNNs, Transformers):
- Automating feature extraction (land degradation, biodiversity loss);
- Modeling nonlinear sphere couplings (e.g., anthropogenic carbon–climate impacts);
- Real-time hazard forecasting with uncertainty quantification;
- Optimizing resource governance via predictive simulations.
Submissions must demonstrate DL-powered RS/GIS frameworks transcending conventional approaches in accuracy, scalability, and policy relevance. Original research/reviews covering algorithms, cross-sphere modeling, and sustainability applications are invited.
We look forward to receiving your submissions
Dr. Shuo Zheng
Dr. Xiaoshuang Ma
Dr. Yang Bai
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 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. Sustainability 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 2400 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
- remote sensing
- GIS
- deep leaning
- natural resources
- natural hazards
- environmental pollution
- carbon cycle
- multiple sphere coupling
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