Mapping illegal waste dumping sites with neural-network classification\n of satellite imagery

Le résumé fourni par la source

Public health and habitat quality are crucial goals of urban planning. In\nrecent years, the severe social and environmental impact of illegal waste\ndumping sites has made them one of the most serious problems faced by cities in\nthe Global South, in a context of scarce information available for decision\nmaking. To help identify the location of dumping sites and track their\nevolution over time we adopt a data-driven model from the machine learning\ndomain, analyzing satellite images. This allows us to take advantage of the\nincreasing availability of geo-spatial open-data, high-resolution satellite\nimagery, and open source tools to train machine learning algorithms with a\nsmall set of known waste dumping sites in Buenos Aires, and then predict the\nlocation of other sites over vast areas at high speed and low cost. This case\nstudy shows the results of a collaboration between Dymaxion Labs and\nFundaci\\'on Bunge y Born to harness this technique in order to create a\ncomprehensive map of potential locations of illegal waste dumping sites in the\nregion.\n

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