CUTTING-EDGE research led by a James Cook University (JCU) professor has unveiled the crucial role of artificial intelligence (AI) in revealing concealed roads that pose a significant threat to the world’s rainforests.
Using satellite imagery and advanced AI algorithms known as ‘convolutional neural networks,’ the research team, led by Professor Bill Laurance, has developed a method to identify previously unmapped roads, a major contributor to environmental destruction in wilderness areas.
According to Professor Laurance, the Earth is currently witnessing an unprecedented surge in road construction, with an estimated 25 million kilometres of new paved roads projected by mid-century.
Around 90 per cent of this construction is concentrated in developing nations, particularly in tropical and subtropical regions known for their exceptional biodiversity.
“By sharply increasing access to formerly remote natural areas, poorly regulated road development triggers dramatic increases in environmental disruption due to activities such as logging, mining and land-clearing,” Prof Laurance said.
Many of these roads, both legal and illegal, remain uncharted, with studies in regions like the Brazilian Amazon and Asia-Pacific revealing up to 13 times more road length than officially reported by government databases.
Traditionally, road mapping involved manual tracing of road features from satellite images, a painstakingly slow process that struggled to keep pace with the rapid expansion of global road networks.
To address this challenge, the research team trained machine-learning models to automatically detect road features from high-resolution satellite imagery covering remote, 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,” Prof Laurance said.
“We’re not there yet, but we’re making good progress.”
Prof Laurance said that proliferating roads pose one of the most pressing threats to tropical forests worldwide, underscoring the urgent need for advanced tools like AI to map and monitor roads across critical environmental zones.
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