GIScience & Remote Sensing

Multimodal Remote Sensing Data Fusion for Geospatial Intelligence

Contemporary Earth observation and GIScience bring together satellite, airborne, nighttime light, in situ, mobile, social, and model-generated geospatial data. However, heterogeneities in scale, geometry, resolution, uncertainty and timeliness complicate joint analysis and pose critical open challenges, while data limitations such as cloud contamination, mismatched spatial and temporal sampling, missing observations, and domain shift remain ubiquitous.

There is therefore an urgent community need to harness the full potential of heterogeneous geospatial datasets ranging from satellite remote sensing to crowdsourced and ground sensor data. Integrating spectral, spatial, temporal, semantic and contextual cues via fusion unlocks geographical patterns invisible to single data source, and multi-modal data fusion approaches have the potential to translate disjointed observations into consistent geospatial representations for mapping, monitoring, forecasting and decision-making.

The convergence of multi-level fusion strategies, cross-modal learning, geospatial foundation models and interpretable uncertainty-aware methods enables comprehensive extraction of geophysical and semantic information inaccessible to single-modality analysis. Standardised benchmarks, transparent experimental reporting and reproducible evaluation protocols can overcome inconsistent comparison practices within the field, while research that couples data-driven models with physical process constraints further improves the reliability of fusion results for environmental monitoring and spatial decision-making. Bringing together these contributions establishes a coherent research roadmap and facilitates the transition of cutting-edge fusion algorithms towards scalable, trustworthy operational earth observation systems.

This Article Collection centres on multimodal geospatial data fusion for Earth observation and GIScience. It welcomes research demonstrating how fusion enhances the accuracy, spatial detail, transferability or timeliness of geospatial information; bridging geospatial theory, reproducible algorithms and practical applications such as land use mapping, urban infrastructure monitoring, agriculture, ecosystem assessment, climate surveillance, disaster risk management, public health and geospatial digital twins.

Relevant subtopics cover multi-level fusion frameworks, cross-modal machine learning, foundation model-enabled fusion, standardised benchmarks and uncertainty evaluation. We encourage the submission of original research articles, methodological papers, benchmark studies and critical review articles.

Contributions must feature robust methods, explicit validation and demonstrated scientific or societal value across diverse contexts. Contributions integrating remote sensing, GIScience and computer vision are particularly welcomed. Authors should make the data and materials supporting the results or analyses presented in their paper freely available according to Taylor and Francis’ open + FAIR data policy, and be prepared to consider model limitations, ethics and responsible geospatial intelligence to advance reliable, practical multimodal fusion workflows.

Guest Advisor Biographies

Hongjie He is an Associate Professor at the School of Geospatial Artificial Intelligence, East China Normal University. He received his Ph.D. in Geography from the University of Waterloo, Canada, with solid academic training in geospatial science and remote sensing. His research primarily focuses on AI-enabled multimodal and multi-source remote sensing data fusion, covering optical, SAR, hyperspectral, LiDAR and geophysical satellite data, to tackle critical challenges including sensor heterogeneity, scale inconsistency and missing observations. He has published extensively in top remote sensing and GIScience journals and accumulated rich experience in academic community service, including conference session chairing, frequent peer reviewing, and leading special issue organization. Dr. He is dedicated to advancing robust, reproducible, and trustworthy geospatial fusion methodologies and promoting international academic collaboration in Earth observation and GIScience.

Prof. Kyle (Yilin) Gao is a Tenure Track Assistant Professor of Geoinformatics at Aalto University and a Visiting Scholar at the University of Cambridge with the Tessera project. Previously, he was a Postdoctoral Fellow at the University of Waterloo, where he held an NSERC CPRA Fellowship and was awarded a Schmidt AI in Science Fellowship. He received his Ph.D. from the University of Waterloo in 2025. His research focuses on 3D reconstruction and geospatial foundation models for studying the natural and built environment. He has authored over 40 peer-reviewed papers in top AI conferences and remote sensing journals, and reviewed over 80 manuscripts. He has chaired over 10 sessions at leading conferences and serves as Co-Chair of the ISPRS Working Group on Foundations in AI for Photogrammetry and Remote Sensing.

Linlin (Lincoln) Xu is a Tenure-Track Assistant Professor in the Department of Geomatics Engineering at the University of Calgary. He received his Ph.D. in Geography from the University of Waterloo in 2014 and subsequently held postdoctoral and research faculty appointments there. His work focuses on environmental mapping, particularly Arctic, land, and hyperspectral applications, by integrating artificial intelligence with SAR, hyperspectral, multispectral, and LiDAR observations. His expertise supports rigorous multimodal methods for remote-sensing analysis and geospatial monitoring.

Jing Li is Associate Professor at the School of Geospatial Artificial Intelligence, East China Normal University, Shanghai, China. His research focuses on computer vision, with an emphasis on multimodal image fusion and remote sensing image processing, including infrared and visible image fusion, remote sensing image fusion, LLM-based degradation removal and fusion, as well as LLM-based remote sensing image reasoning segmentation, and visual question answering (VQA). He has authored over 30 academic papers, including top-tier conferences and journals such as CVPR, AAAI, IJCAI, TIP, and TMM with three of them recognized as ESI Highly Cited Papers. He serves as a reviewer for top journals including TIP, TNNLS, TGRS, TMM, PR, JAG.

Further Information

­­All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is 31 May 2027.

Please contact Alex Johnson at alex.johnson@tandf.co.uk for more information about this Collection or to enquire about discount codes.

lease be sure to select the appropriate Article Collection from the drop-down menu in the submission system.