International Journal of Digital Earth

Multi‑Source Earth Observation and GeoAI for Maritime Situational Awareness

Maritime situational awareness has become an essential component of Digital Earth research, supporting the observation, understanding, and prediction of human activities and environmental changes across vast and dynamic ocean spaces. Recent advances in multi-source Earth observation, including optical and SAR satellite imagery, thermal infrared sensing, nighttime light data, UAV observations, coastal radar, AIS, meteorological-oceanographic data, and other geo-referenced information, are creating new opportunities to monitor vessels, ports, waterways, fisheries, offshore infrastructure, and coastal environments. At the same time, GeoAI provides powerful methods for extracting knowledge from heterogeneous, high-volume, and spatiotemporally complex maritime data. By integrating multi-source observation with intelligent geospatial analytics, maritime situational awareness is evolving from fragmented monitoring toward more comprehensive, adaptive, and predictive representations of ocean and maritime systems.

Improving maritime situational awareness is important for global trade, navigation safety, ocean governance, environmental protection, and sustainable development. Maritime transportation carries the majority of international trade, yet ocean spaces remain difficult to observe continuously because of their scale, dynamic conditions, limited infrastructure, and the presence of non-cooperative or “dark” vessels. Critical challenges such as ship collision risk, illegal fishing, port congestion, search and rescue, Arctic navigation, oil spills, marine pollution, shipping emissions, and coastal hazards require timely and reliable geospatial intelligence. Traditional single-source monitoring approaches are often constrained by weather, revisit frequency, spatial resolution, data gaps, or limited semantic understanding. Multi-source Earth observation and GeoAI can overcome these limitations by enabling data fusion, vessel detection, behavior interpretation, risk prediction, anomaly discovery, and decision support. These capabilities are central to the Digital Earth vision of transforming geo-referenced data into actionable knowledge for safer, smarter, and more sustainable oceans.

This Article Collection aims to advance Digital Earth technologies for maritime situational awareness by covering theories, methods, datasets, systems, and operational applications based on multi-source Earth observation and GeoAI. Subtopics include:

• Maritime data fusion
• Ship detection, classification, and tracking
• AIS and remote sensing integration
• Non-cooperative vessel monitoring and dark vessel discovery
• Trajectory prediction
• Maritime anomaly detection
• Port and waterway intelligence and smart ports
• Maritime transportation networks
• Digital twins of ships, ports, waterways, and ocean environments
• Arctic and coastal navigation
• Search-and-rescue decision support
• Marine pollution and shipping emission monitoring
• Uncertainty modelling
• Explainable and trustworthy maritime GeoAI
• Web-based platforms for maritime data visualization and sharing

The Collection welcomes Research Articles, Review Articles, Research Letters, Target Articles, and Data Notes, and especially contributions that present conceptual advances, innovative methodologies, reusable datasets, Digital Earth platforms, and transformative maritime applications.

Article Collection Guest Advisors

Maohan Liang received his Ph.D. degree in Traffic Information Engineering and Control from the Wuhan University of Technology, Wuhan, China, in 2023. During his doctoral study, he was a visiting Ph.D. student at Nanyang Technological University, Singapore, from 2021 to 2023. From 2023 to 2025, he was a Research Fellow at the National University of Singapore. He is currently a Professor (Specially Appointed) with the School of Navigation, Wuhan University of Technology. His research interests include intelligent transportation systems, maritime data mining, and multisource data fusion.

Peng Peng is an Associate Researcher at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, China. His research focuses on geographic information science, particularly complex geographical network analysis, maritime spatiotemporal data mining, and knowledge graphs. His work examines the structure, evolution, and robustness of maritime transportation networks using large-scale vessel trajectory data. He has led research projects supported by the National Natural Science Foundation of China and served as a guest editor for ISPRS International Journal of Geo-Information and Sensors.

Feng Lu is a Research Professor at the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, and a Professor at the University of Chinese Academy of Sciences. His research interests include geospatial artificial intelligence, trajectory data mining, knowledge graphs, complex network analysis, and transportation geographic information systems. His work spans spatiotemporal data analysis, intelligent mobility, and maritime transportation networks. He has led research projects supported by China’s National Key Research and Development Program and the National Natural Science Foundation of China. He also serves as Executive Editor-in-Chief of Journal of Geo-information Science.

Further Information

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisors for this Collection will not be handling the manuscripts.

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

The deadline for submitting manuscripts is 31 July 2027.

Please contact Alex Johnson at alex.johnson@tandf.co.uk if you require further information about, or to request a discount code for this Article Collection 

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

Article Collection Key Terms

1. Maritime Situational Awareness
2. Multi-Source Earth Observation
3. GeoAI
4. Smart and Sustainable Oceans
5. Maritime Digital Earth