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Timely and accurate land-cover information is critical for environmental monitoring, sustainable development, and spatial planning. However, existing datasets are often outdated, lack sufficient spatial resolution, or remain inaccessible to non-expert users, limiting their practical value. This study presents the land observation and classification interface (LOCI), a prototype web-based platform developed as a proof of concept for near-real-time land-cover classification and vegetation monitoring. LOCI integrates Sentinel-2 imagery, Google Earth Engine, and a random forest classifier to deliver scalable and interactive land-cover and normalized difference vegetation index products directly through a browser interface. Rather than replicating full spatial digital twin (SDT) systems, LOCI functions as a modular component that enhances SDT ecosystems by providing near-real-time environmental intelligence. The platform's usability and performance are demonstrated through three use cases: (1) identifying cropland preparation and forest clearing as indicators of potential hydrological risk, (2) monitoring land-cover changes, and (3) tracking wetland vegetation dynamics. LOCI achieves high classification accuracy (overall accuracy 92%–93%, Kappa 0.88–0.89) and effectively quantifies relevant environmental changes. These findings demonstrate LOCI's potential to democratise access to remote sensing data and support evidence-based environmental planning. Furthermore, it strengthens integration with SDT frameworks that align with the UN SDGs 6, 11, 13, and 15.

From remote sensing to decision-making: a web-based platform for near-real-time land cover classification