This application allows users to select a custom area of interest and date range to analyze how environmental land cover has shifted. Categories such as dense forest canopy, agricultural cropland, built-up urban structures, and surface water are classified and visualised seamlessly across temporal sequences.
Traditional satellite analysis requires heavy spatial software, licenses, and dedicated workstations. This project makes land cover monitoring accessible to urban planners, students, and community stakeholders by delivering interactive temporal maps inside standard web browsers.
From Open Imagery to Browser Canvas
Open Satellite Sourcing
Land Cover Classification
WebGIS Tile Serving
Ingested multi-spectral imagery from Sentinel-2 and Landsat archives covering targeted monitoring regions across specified observation windows.
Processed and categorized spectral signatures into core environmental classes — forest, cropland, built-up area, and water — utilizing `[GeoAI Classifier Model]`.
Published optimized raster map tiles and vector boundaries through a lightweight web mapping engine (`[Web Mapping Library]`) tuned for responsive client-side rendering.
Key Portal Features
Engineered to minimize friction and maximize insight for non-specialist spatial analysts.
Temporal Comparison UI
Land Cover Layer Toggling
Zero-Install Web Execution
Select date ranges to inspect land cover transitions across `[Observation Window]` with synchronized side-by-side split map viewports.
Isolate or stack specific environmental categories — forest, cropland, built-up urban area, or water bodies — with instant opacity toggles.
Runs directly within standard modern web browsers with zero desktop GIS dependencies or server-side GIS plugins required.
Explore additional GeoAI research, publications, and web spatial architecture projects.