Journalists are pairing satellite and AI to expose illegal mining in the Amazon

In 2018, Joseph Poliszuk fled Venezuela. That year, after exposing corruption in then-President Nicolas Maduro’s administration, he had become the target of lawsuits by wealthy Maduro loyalists. He and several of his colleagues at the independent outlet Armando.info packed up their lives and fled the country under threat of imprisonment. For years, Poliszuk had published stories on Southern Venezuela, which is made up of sparsely populated states that cover large swaths of the Amazon Basin and the Orinoco River Basin. Through field reporting, Poliszuk had exposed illegal gold mines, narcotrafficking operations, and crimes against indigenous groups scattered throughout the region’s rainforests. Now in exile — first working from Colombia, then Mexico — Poliszuk was forced to reimagine how to do his work from thousands of miles away. He began experimenting with satellite-based investigations. RELATED ARTICLECollaboration helps keep independent journalism alive in VenezuelaHanaa' TameezSeptember 5, 2024Satellite imagery has long helped investigative journalists gather intelligence on conflict zones and track changes in remote landscapes. Now, in a new wave of satellite-based investigations, reporters are leaning on machine learning models to automate parts of this work and scale up their analysis to an unprecedented degree. This innovation is most visible in environmental journalism. Poliszuk is just one in a cohort of South American investigative reporters who have used geospatial data and AI-powered pattern recognition to track illegal mining, large-scale logging operations, and cattle ranching across the Amazon. As illegal gold mining spiked during the COVID-19 pandemic, Poliszuk knew there was a story in documenting the growth of these mines across Venezuela’s rainforests. But manually combing through the satellite images for over 50 million hectares of rainforest wasn’t practical. Poliszuk wondered if he could train a machine learning model to detect the scars of mining pits in these images, as well as the neighboring airstrips that are cut into dense vegetation and used to transport minerals. With financial and editorial support from the Pulitzer Center’s first Rainforest Investigations Network (RIN) fellowship and technical support from the nonprofit Earth Genome, Poliszuk was able to do just that. In January 2022, he co-published his first article using the custom machine learning model in a series in El Pais titled “Corredor Furtivo [Clandestine Corridor].” Poliszuk was able to identify 3,718 gold mining locations in the Venezuelan states of Amazonas and Bolívar. Some of those mines were operating inside protected indigenous lands and Canaima National Park, which is home to Angel Falls, the world’s tallest waterfall. By crosschecking maps identifying mining activity with crime data from Venezuelan authorities, Poliszuk was also able to determine whether the mines were run by Venezuelan syndicates, Colombian guerilla groups, or Brazilian garimpeiro (prospectors). The week after Poliszuk published one of his first stories in the El Pais series, the Venezuelan military announced that it had bombed several illegal airstrips operating in the region. “I have 20 years’ experience covering [illegal mining]…thanks to this technology I can show people the dimension of this phenomenon,” Poliszuk told me. “Thanks to this movement, we have understood that we can track by the air what we cannot prove on foot.” The view from above Even for…

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