GeoPROV: A Semantic Provenance Profile for Coastal EO-Derived Products Informed by a Systematic Review of Spatial Data Supply Chains

Emerging remote sensing technologies increasingly support coastal observation through Earth observation (EO) derivatives such as satellite-derived shorelines, unmanned aerial vehicle (UAV)- and light detection and ranging (LiDAR)-based elevation models, coastal land-use/land-cover products, bathymetric grids, turbidity and chlorophyll-a indicators, habitat maps, flood extents, and artificial intelligence (AI)-generated segmentation outputs. These EO-derived products are central to shoreline monitoring, coastal vulnerability assessment, flood-response planning, marine ecosystem management, and land–sea administration. Yet their evidential value depends on transparent provenance: users must know which sensor, acquisition condition, preprocessing chain, algorithm, model version, validation dataset, and uncertainty estimate produced each coastal product. The review synthesises 156 eligible publications identified through a systematic review process. Of these, 89 studies containing sufficient implementation and methodological detail were selected for detailed analytical coding. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol, the review analyses application domains, provenance capture modes, representation strategies, semantic web technologies, and interoperability practices. A coastal EO thematic refinement translates the review findings into provenance requirements for satellite, UAV, LiDAR, Internet of Things (IoT), open-water, and AI-enabled coastal observation workflows. The review identifies persistent gaps in feature-level lineage, alignment between the World Wide Web Consortium Provenance Ontology (PROV-O) and International Organization for Standardization (ISO) geospatial metadata standards, spatial semantics, AI workflow transparency, uncertainty traceability, and scalable querying. To address these limitations, the paper introduces GeoPROV, a lightweight semantic profile of PROV-O designed for spatially explicit, feature-level provenance. GeoPROV aligns PROV-O with ISO 19115/19157, GeoSPARQL, Shapes Constraint Language (SHACL), and SpatioTemporal Asset Catalogue (STAC)-compatible catalogue practices. The framework enables EO-derived coastal products to be traced from raw observation through preprocessing, AI/geoprocessing, validation, catalogue publication, and coastal-administrative decision-making. The resulting GeoPROV profile integrates PROV-O, GeoSPARQL, SHACL, ISO 19115/19157 lineage concepts, and STAC metadata within a lightweight semantic framework demonstrated through a coastal EO product workflow and machine-actionable provenance implementation.