Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS)

The ISPRS Journal of Photogrammetry and Remote Sensing (P&RS) is the official journal of the International Society for Photogrammetry and Remote Sensing (ISPRS). The Journal provides a channel of communication for scientists and professionals in all countries working in the many disciplines that employ photogrammetry, remote sensing, spatial information systems, computer vision, and related fields. The Journal is designed to serve as a source reference and archive of advancements in these disciplines.

The P&RS objective is to publish high quality, peer-reviewed, preferably previously unpublished papers of a scientific/research, technological development or application/practical nature. P&RS will publish papers, including those based on ISPRS meeting presentations*, which are regarded as significant contributions in the above-mentioned fields. We especially encourage papers: of broad scientific interest; on innovative applications, particularly in new fields; of an interdisciplinary nature; on topics that have not been dealt with (or to a small degree) by P&RS or related journals; and on topics related to new possible scientific/professional directions. Preferably, theoretical papers should include applications, and papers dealing with systems and applications should include theoretical background.

The scope of the journal is extensive and covers sensors, theory and algorithms, systems, experiments, developments and applications. Topics of interest include but are not limited to:

Sensors

• Airborne and spaceborne multispectral and hyperspectral imaging systems
• Airborne and terrestrial cameras
• Airborne, terrestrial and mobile laser scanning
• Range imaging
• Active and passive imaging sensor characterisation
• Sensor calibration and standardisation
• Geosensor networks
• Internet of Things

Methods and procedures

• Spatial data handling technologies
• Integrated sensor calibration and orientation
• Surface and object reconstruction, modelling and interpretation
• GIS data modelling, representation and structures
• Database systems
• Mapping from imagery
• Visualisation, animation and virtual reality
• Physical measurements and spectral signatures
• Point cloud processing
• Extraction of geometric and semantic information from SAR
• SAR Interferometry
• Image classification algorithms
• Image segmentation and GEOBIA
• Image time series analysis
• Hyperspectral data analysis
• Big image data analysis
• Crowd sourcing
• Feature extraction

Applications and products

• 3D urban GIS
• Close-range imaging and metrology
• Architectural & archaeological photogrammetry
• Determination of forest metrics
• Medical image analysis and biometry
• Resource and environmental modelling
• Global monitoring and assessment
• Thematic applications
• DTM and orthoimage generation
• Location based services

Submitted articles may be:

• Papers (detailed discussions involving new research, technological developments or applications);
• Review Papers (extensive state-of-the-art surveys of established or emerging topics or application areas); or
• Contributions for special Journal columns (letters to the Editor about the journal or to authors commenting on previously published papers; new useful ideas and brief pertinent comments of a technical nature; editorials, mainly after an invitation by the Editorial Advisory Board).

NOTE: All articles, except correspondence, will be peer-reviewed and should be of high scientific level.

* Journal paper submissions based on past conference publications are only allowed if the submitted manuscript is significantly extended or otherwise enhanced. That is, the journal article will only be considered if more extensive experiments and discussion are presented (even if the same methods are used) and/or the methods have been significantly improved and/or advanced (even if the same datasets have been used). The burden of explicitly describing the differences between these publications rests with the author(s). In case of a strong overlap with another publication, the manuscript should be rejected.

P&RS issues may be devoted to specific scientific topics of high or increasing importance (Theme Issues), especially in relation to one or more ISPRS Working Groups, as well as to special events, geographic regions, professional activities etc. The number and topics of Theme Issues will be such that the P&RS still remains of interest to the majority of its subscribers.

For more details on the International Society for Photogrammetry and Remote Sensing, visit http://www.isprs.org.

Special Issue on Interpretability and explainability in GeoAI for Geospatial Science and Earth Observations
Submission Date: 2026-05-31

In recent years, Geospatial Artificial Intelligence (GeoAI), has demonstrated significant value in social, urban, environmental and Earth sciences. GeoAI integrates AI with geospatial science and technology, uniquely modeling spatial effects to solve complex geographical problems, revealing hidden patterns, and advancing theories. Its dual role bridges scientific discovery and real-world impact. However, the "black-box" nature and inherent complexity of GeoAI models present substantial challenges. In GeoAI for science, interpretability is not merely desirable but indispensable. The task of facilitating scientific discovery and advancing knowledge in this field directly relies on the ability to understand and interpret model decisions and behaviors. To meet this demand, GeoAI models must either be designed with inherent interpretability—achieved by explicitly embedding spatial effects, mechanism, and rules into their design—or be supported by a robust suite of explainability methods capable of explaining model behaviors and outputs.

Meanwhile, we have seen significant developments in XAI methods. For instance, integrating physical models or empirical laws directly into the model design to make them inherently interpretable; and also, employing post-hoc explainability methods to interpret the behavior of complex models. Notable XAI techniques, such as LIME, SHAP, Grad-CAM, Integrated Gradients, and GNN Explainer, have been developed and applied to various data types—including images, text, graphs, and tabular data—and have seen exponential growth in geospatial applications.

However, current applications of XAI in geospatial science and earth observations are still primarily centered on post-hoc explainability methods. While these approaches can faithfully reflect model behavior, they often fall short in uncovering the internal mechanisms of models or the underlying spatial data-generating processes. Although some efforts have been made toward enhancing model interpretability, particularly by integrating location information or domain knowledge, such approaches remain scarce and underdeveloped. These limitations underscore the urgent need for further innovation to advance both the explainability and interpretability of GeoAI models, thereby fully realizing their potential for scientific discovery and practical applications.

Advancing explainable GeoAI (XGeoAI) methods is thus critical for enhancing the quality, transparency, and scientific value of research in geospatial science and earth observations. We invite contributions that address these challenges, including but not limited to the following areas:

    Explainability and interpretability in geospatial foundation models
    Spatially explicit XAI methods for GeoAI
    Intrinsically interpretable GeoAI model design
    Physics-aware machine learning in GeoAI
    Causal inference and discovery in GeoAI
    Ethical concerns, fairness, bias detection and uncertainty quantification in GeoAI
    XAI for earth observation imagery and spatial big data
    XAI applications in urban, environmental and ecosystems
    Scalability and computational efficiency of XAI in geospatial contexts
    Geovisual analytics, people sensors, and human-centered XGeoAI
    Large language models (LLMs) and vision language models (VLMs) in XGeoAI
    Evaluation frameworks for XGeoAI

This special issue aims to advance the application of XGeoAI in geospatial science and earth observations, fostering cross-disciplinary innovation and knowledge exchange, and enhancing the interpretability and societal impact of research outcomes.

Papers must be original contributions, not previously published or submitted to other journals. Conference papers may be considered provided that they are considerably extended and improved. Substantive research and relevant-for-practice papers will be preferred. Review articles around the topics are also encouraged. Papers must follow the instructions for authors at http://www.elsevier.com/journals/isprs-journal-of-photogrammetry-and-remote-sensing/0924-2716/guide-for-authors.

For any inquiry, please contact Dr. Fan Zhang (fanzhanggis@pku.edu.cn).

Guest editors:

Dr. Fan Zhang
Peking University, Beijing, China
fanzhanggis@pku.edu.cn 

Dr. Ziqi Li
Florida State University, Tallahassee, FL, USA
ziqi.li@fsu.edu 

Dr. Xiao Huang
Emory University, Atlanta, GA, USA
xiao.huang2@emory.edu 

Dr. Xiaoxiang Zhu
Technical University of Munich, Munich, Germany
xiaoxiang.zhu@tum.de 

Manuscript submission information:

To submit your manuscript please go to  https://www.editorialmanager.com/photo/default.aspx  and follow the procedures for manuscript submission by selecting our Special Issue Article type as "VSI: XGeoAI”.

Submission Deadline: 31 May 2026

Author Guidelines and Manuscript Submission can be found at: https://www.sciencedirect.com/journal/isprs-journal-of-photogrammetry-and-remote-sensing/publish/guide-for-authors

Keywords:
GeoAI; XAI; Interpretable Models; AI for Geosciences; AI Explainability