Earth Index<\/a> allows users to scan satellite imagery by visual similarity. A user can highlight an example\u2014a patch of deforestation, a mining site, a trawler, or an airstrip\u2014and instruct the system to find comparable patterns elsewhere. The underlying approach relies on \u201cfoundation models\u201d trained on vast archives of Earth observation data, enabling the system to recognize features across geography and time.<\/p>\n Until recently, such analysis required specialized teams, bespoke models, and significant computing resources. Even well-funded investigations could take months to develop. Tools like Earth Index reduce that burden. In one Mongabay-specific case<\/a>, our journalists used it to identify previously unreported narcotrafficking airstrips<\/a> in the Peruvian Amazon, combining automated detection with on-the-ground reporting (Spanish<\/a>)<\/p>\n Earth Index is now available without a waitlist<\/a>, through an \u201cOpen\u201d tier that provides global access and core features to any user. More advanced capabilities\u2014such as higher usage limits, API access, and a more computationally intensive \u201cDeep Search\u201d\u2014sit behind a separate tier, though the developers say they intend to keep access free for high-impact users.<\/p>\n This expansion reflects a shift in how geospatial AI is being deployed. Satellite data has been publicly available for decades, yet much of it remains underused because of its complexity. Foundation models alter that dynamic by allowing users to interact with imagery in more intuitive ways, translating raw pixels into searchable patterns.<\/p>\n Earth Index\u2019s design emphasizes accessibility. Non-technical users can upload their own reference data, generate training labels, and share results publicly. The system supports iterative workflows in which human judgment refines machine output, enabling datasets that once took months to assemble to be built in days.<\/p>\n Early applications suggest a wide range of uses. Investigations have mapped illegal gold mining in the Amazon<\/a>, quarry operations in the Balkans<\/a>, watersheds in California\u2019s farming heartland<\/a>, and methane emissions from cattle<\/a>. Others have used the tool to catalogue industrial livestock facilities or track changes in land use across regions.<\/p>\n Much of the underlying imagery comes from public satellites with moderate resolution and update frequency, making the system less suited to real-time monitoring. Interpretation still depends on user judgment, and false positives remain a risk. The availability of such tools also raises questions about oversight and misuse, even if current data constraints reduce the likelihood of surveillance at a granular level.<\/p>\n Its main impact, for now, is that access extends beyond specialists. By lowering the technical threshold, it extends capabilities once confined to governments and specialists to a wider set of actors: journalists, researchers, advocacy groups, and local communities. Whether it improves environmental outcomes will depend on who uses it, and to what ends.<\/p>\n Disclosure: Mongabay has a partnership with Earth Genome. Earth Genome did not have editorial influence over this piece.<\/p>\n ![\"Amazon-Mining-Watch\"](\"https:\/\/www.newsbeep.com\/nz\/wp-content\/uploads\/2026\/04\/amazon-mining-watch-banner-1200x675.jpg\")
Earth Genome worked with Amazon Conservation and Pulitzer Center to build Amazon Mining Watch.<\/p>\n![\"\"](\"https:\/\/www.newsbeep.com\/nz\/wp-content\/uploads\/2026\/05\/a81409e64d34c22fc5eb136dcb9eb756ce6393a217056782ac4dec4b7f76f1f2.png\")
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