Explainable and Trustworthy AI for Earth Observation Applications and Geospatial Science
A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".
Deadline for manuscript submissions: 31 December 2026 | Viewed by 1
Special Issue Editors
Interests: remote sensing; machine learning algorithms; deep learning; climate change; data mining; XAI
Special Issues, Collections and Topics in MDPI journals
Interests: remote sensing and applications; digital image processing; machine learning algorithms and applications; deep learning; environmental change analyses
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in artificial intelligence (AI) and machine learning have profoundly transformed the capabilities of Earth observation and remote sensing. However, the growing complexity of modern AI models has raised critical concerns regarding transparency, interpretability, and scientific trustworthiness.
Explainable Artificial Intelligence (XAI) has emerged as a compelling paradigm to address these challenges by providing meaningful insights into model behavior and decision-making processes. When integrated with geospatial data and remote sensing workflows, XAI not only facilitates rigorous model validation but also deepens our scientific understanding of environmental and Earth system processes.
Despite its promise, the integration of XAI into geospatial contexts introduces a distinct set of challenges, including spatial heterogeneity, scale dependency, geospatial ethics, and the absence of standardized evaluation metrics tailored to Earth observation tasks. Furthermore, despite the rapid adoption of deep learning and data-driven approaches in remote sensing, the lack of model interpretability remains a significant limitation, particularly in high-stakes domains such as disaster risk assessment, climate change analysis, and environmental management, where model transparency is not merely desirable but essential.
This Special Issue seeks to bridge the gap between predictive performance and interpretability by promoting AI methods that are not only accurate, but also transparent, physically consistent, and scientifically meaningful. We particularly encourage contributions that incorporate domain knowledge, such as physical laws, geospatial constraints, and expert-driven priors, into explainable AI frameworks, fostering a new generation of models that are both powerful and interpretable. We welcome original research articles and reviews on themes including, but not limited to, the following:
- Explainable AI (XAI) for remote sensing applications;
- Geospatial AI (GeoAI) and explainable spatial modeling (GeoXAI);
- Physics-informed and hybrid AI models in Earth observation;
- Spatiotemporal explainability in environmental monitoring;
- XAI for remote sensing image analysis tasks, including semantic segmentation, image fusion, and change detection;
- Interpretable deep learning for satellite imagery analysis;
- Explainable AI (XAI) for remote sensing applications;
- Geospatial AI (GeoAI) and explainable spatial modeling (GeoXAI);
- Physics-informed and hybrid AI models in Earth observation;
- Spatiotemporal explainability in environmental monitoring;
- XAI for remote sensing image analysis tasks, including semantic segmentation, image fusion, and change detection;
- Interpretable deep learning for satellite imagery analysis;
- Feature attribution and uncertainty quantification in geospatial models;
- Causal inference and explainability in Earth system science;
- Trustworthy AI for environmental decision support;
- Benchmarking and evaluation of XAI methods in remote sensing.
Prof. Dr. Taskin Kavzoglu
Dr. Ismail Colkesen
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
- explainable AI (XAI)
- trustworthy AI
- geospatial AI (GeoAI)
- physics-informed AI
- deep learning
- interpretable deep learning
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.
Further information on MDPI's Special Issue policies can be found here.
