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Assessed by researchers at James Cook University, the automated approach to large-scale road mapping uses convolutional neural networks trained on road data.
According to the scientists, many roads in developing countries, both legal and illegal, are unmapped, with road-mapping studies in theA sampled image at full extent and for a smaller inset area featuring clearly discernible land covers and road infrastructure. Previous studies, on the other hand, have shown that earth is experiencing an unprecedented wave of road building, with some 25 million kilometres of new paved roads expected by mid-century.
“Traditionally, road mapping meant tracing road features by hand, using satellite imagery. This is incredibly slow, making it almost impossible to stay on top of the global road tsunami,” Bill Laurance, senior author ofLaurance explained that he and his colleagues trained three machine-learning models to automatically map road features from high-resolution satellite imagery covering rural, generally“This study shows the remarkable potential of AI for large-scale tasks like global road-mapping.
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