AOI-clipped land-use classification raster
Domain / Land Use AI
Land-use and land-cover classification for planning workflows
Classify water, vegetation, built-up, and bare soil patterns from multispectral imagery with AI-ready raster workflows.
Open Modelling PanelDecision Outputs
NDVI, NDBI, and MNDWI-derived feature context
Class distribution summaries for planning reports
GeoAI-ready labelled training and inference handoff
Workflow
Step 1
Upload multispectral GeoTIFF imagery and optional AOI boundary.
Step 2
Generate spectral indices and baseline land-cover classes.
Step 3
Upgrade to GeoAI segmentation/classification models for production accuracy.
Algorithm & Module Registry
Landuse, surface temperature, raster uploads, layer visualization, legends, and palette controls.
Recommended Algorithms
GeoAI Raster Tools
- NDVI, NDBI and MNDWI feature generation
- Baseline land-cover classification
- Land surface temperature and emissivity correction
- Raster min/max palette control
- Projection-aware GeoTIFF visualization
Data Sources
Input Stack
Operational Outputs
Decision Products
- Landuse raster
- Surface temperature raster
- Flood/HAND layers
- Interactive map review
Open Linked Modules
GeoAI Toolchain
Recommended AI and geospatial tools for turning this model into a production-grade Nita AI module.
GeoAI / OpenGEOAI
Geospatial AI model training, segmentation, object detection, inference, and imagery workflows.
Optional runtime integration through geoai-py; the site reports availability when installed. Open docsGoogle Earth Engine
Cloud-scale public geospatial datasets, satellite imagery, population rasters, and time-series analysis.
Optional live mode after Earth Engine authentication. Open docsLeaflet + Leaflet Draw
Interactive AOI drawing, map inspection, layer controls, and user-facing spatial workflows.
Enabled in the browser UI. Open docsChart.js
Time-series, indicator, and demographic charts for decision dashboards.
Enabled in the browser UI. Open docs