Creating Training Datasets for the SpaceNet Road Detection and Routing Challenge
The SpaceNet Road Detection and Routing Challenge aims to automatically extract road networks directly from high-resolution satellite imagery. Such automated processes may help improve a vast array of problems, from the mundane (traffic) to the extreme (mass evacuation). In previous posts [1, 2] we detailed the evaluation metric for this challenge, and more recently we described the SpaceNet Roads
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Remote Sensing: Deep Learning for Land Cover Classification of Satellite Imagery Using Python
