Technologies in Remote Sensing Images
A special issue of Technologies (ISSN 2227-7080).
Deadline for manuscript submissions: 30 December 2026 | Viewed by 118
Special Issue Editor
Interests: artificial intelligence; machine learning; environmental monitoring; water quality modeling; federated learning; sustainable development; computational science
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid development of Earth-observation technologies has led to an unprecedented expansion in the availability, spatial resolution, and temporal coverage of remote sensing imagery. Satellite constellations, unmanned aerial vehicles (UAVs), hyperspectral sensors, and radar platforms continuously generate massive volumes of geospatial data. While these datasets offer immense opportunities for environmental monitoring, climate analysis, urban studies, and resource management, their effective interpretation requires advanced computational methods capable of automated and scalable analysis.
Recent breakthroughs in artificial intelligence, machine learning, and deep learning have fundamentally transformed remote sensing image analysis. Modern AI approaches—including convolutional neural networks, transformer-based architectures, self-supervised learning, and foundation models for Earth observation—enable highly accurate land-cover classification, object detection, environmental change monitoring, and predictive modeling across large-scale spatial datasets.
This Special Issue focuses on AI-driven methodologies for remote sensing image analysis, emphasizing the integration of machine learning, computer vision, and geospatial data science. We invite contributions presenting novel algorithms, intelligent data-processing frameworks, and applied studies leveraging remote sensing imagery for environmental and geospatial applications.
Particular attention will be given to emerging research directions, such as foundation models for geospatial data, multimodal Earth observation, federated learning for distributed sensing systems, explainable AI for environmental decision-making, and autonomous AI agents supporting large-scale environmental monitoring.
We welcome original research articles, methodological contributions, and review papers that advance the state of the art in intelligent remote sensing, geospatial AI, and automated environmental analytics.
Dr. Tymoteusz I. Miller
Guest Editor
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. Technologies is an international peer-reviewed open access monthly 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 1800 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
- artificial intelligence
- machine learning
- deep learning
- remote sensing
- satellite imagery
- computer vision
- Earth observation
- geospatial analytics
- foundation models
- federated learning
- explainable AI
- environmental monitoring
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