Mapping 12 Million Hectares: AI Land-Use Intelligence for Water-Stressed Regions

1. 1

   The need

   A US-based environmental analytics firm serving state water agencies and agricultural districts across the American Southwest, with parallel land-monitoring work for Australian government clients.

   Land-use maps were still produced through semi-manual workflows, with analysts classifying imagery scene by scene. A single regional assessment took six weeks, while clients needed change detection at a seasonal, not annual, cadence.

2. 2

   The work

   • Satellite Imagery Analysis Pipeline: automated ingestion from 20+ satellite sources, multi-spectral and SAR.
   • AI Land-Use & Land-Cover Detection: deep-learning classification with change-detection alerting.
   • Spatial Analytics Platform: raster and vector processing at scale on cloud-native infrastructure.
   • Interactive Mapping Portal: client dashboards with time-series sliders and exportable reports.
   • Drone Data Processing: photogrammetry workflows integrated with satellite basemaps.
   • Geospatial APIs: feeding classification outputs into clients' water-rights and compliance systems.
3. 3

   The outcome

   • 96%+ classification accuracy against ground truth.
   • 6 wks → 4 days analysis cycle per region.
   • 80% lower cost per km² analysed.
   • 12M ha under continuous monitoring.

Based on a client testimonial; details beyond the client's account have not been assumed.