NeimanLab – “Satellite 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)…”