Geospatial AI Solutions

Geospatial AI processes satellite, aerial, and sensor-based spatial data to extract actionable intelligence at global scale. The data volume problem is distinctive: commercial satellite constellations (Planet Labs, Maxar, Sentinel) generate petabytes of imagery per day — far beyond human analyst capacity to review. ML models provide the first-pass classification layer that makes this data operationally useful. Applications span agriculture (crop monitoring), defense and intelligence (change detection), climate (emissions monitoring, deforestation), infrastructure (asset mapping), and disaster response (damage assessment).

Solution Areas

Satellite Data Analysis — Classify land cover, detect change over time, and extract features from multispectral and synthetic aperture radar (SAR) imagery. Convolutional neural networks and vision transformers achieve state-of-the-art performance on standard remote sensing benchmarks (SpaceNet, xView, DOTA). Transfer learning from pretrained geospatial foundation models (IBM-NASA Prithvi, Google SatVision) reduces the labeled data required for new applications.

GIS AI Architecture — Architect data pipelines that ingest, preprocess, and serve geospatial datasets for ML training and inference. Covers coordinate reference system handling, tiling strategies for large rasters, vector-raster integration, and deployment patterns for spatial inference at scale. Integration with STAC (SpatioTemporal Asset Catalog) for cloud-native geospatial data access.

Emerging applications include natural language interfaces to spatial data (query a GIS database in plain English), multi-agent workflows that orchestrate satellite tasking and analysis pipelines, and real-time fusion of satellite, IoT sensor, and ground truth data for environmental monitoring.