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Abstract
We develop AI tools to monitor how road networks change over time, based on satellite imagery. This supports urban planning and assessing the environmental impact of road networks. While satellite imagery makes this possible at scale, high-resolution imagery is expensive, and licensing often restricts model training. To overcome this, we use freely available Sentinel imagery, which covers the globe every few days. However, this data is low-resolution, and this needs to be accounted for in the algorithms.
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