For years, detecting illegal roads in remote areas has remained a challenging and labor-intensive task. More often than not, it requires poring over satellite images to identify thin lines cut through the dense green of forests and fragile ecosystems. Enter artificial intelligence. A new study published in the journal Remote Sensing describes how scientists have automated the process of mapping illegal roads that are wreaking havoc on the environment. The model was trained to detect roads from satellite images captured from rural and semiforested areas in Papua New Guinea, Malaysia and Indonesia. According to the study, the model identified roads with an accuracy of up to 81%. “The forests in those areas are very dense, and oftentimes, there might be a logging road or some illegal road that you wouldn’t be able to clearly see,” study co-author Bill Laurance, a distinguished research professor at James Cook University in Australia, told Mongabay in a video interview. “But artificial intelligence and computer models can be trained to do that.” Illegal roads, like the one above in the Republic of the Congo, often cut through dense forests and cause harm to the biodiversity living in fragile ecosystems. Image by Bill Laurance. There’s been an unprecedented increase in road building in the past few decades. Twenty-five million kilometers (15.5 million miles) of paved roads are expected to be built by 2050, with previous research showing that nine-tenths of all road construction is happening in countries in the Global South. “We are living in the…This article was originally published on Mongabay
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