Geospatial Machine Learning for Urban Development: The collective mission of mapping the world is never complete: We need to discover and classify roads, settlements, land types, landmarks, and addresses. The recent proliferation of remote sensing data (overhang images, LiDAR, sensors) enabled automatic extraction of such structures to better understand our world. In this talk, we will first mention the motivation and results of DeepGlobe Satellite Image Challenge, for road extraction, building detection, and land cover classification. Then we will go into details of an example approach which proposes a complete system to use deep learning for generating street addresses for unmapped developing countries. The approach applies deep learning to extract road vectors from satellite images, then processes the street network to output linear and hierarchical street addresses, by labeling regions, roads, and blocks; based on addressing schemes around the world and coherent with human cognitive system. We will share and demonstrate the motivation and algorithm behind the scenes, then compare them to current open and industrial solutions, and walk through our open source code to generate the addresses for a given bounding box.