AI can potentially estimate global environmental destruction due to road development for mining

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 the Brazilian Amazon, Asia-Pacific and elsewhere regularly finding up to 13 times more road length than reported in government or road databases.

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 of the study published in the journal Remote Sensing, said in a media statement.

Laurance explained that he and his colleagues trained three machine-learning models to automatically map road features from high-resolution satellite imagery covering rural, generally remote and often forested areas of Papua New Guinea, Indonesia and Malaysia.

“This study shows the remarkable potential of AI for large-scale tasks like global road-mapping. We’re not there yet, but we’re making good progress,” he said. “Proliferating roads are probably the most important direct threat to tropical forests globally. In a few more years, AI might give us the means to map and monitor roads across the world’s most environmentally critical areas.”