12/25/2023 0 Comments Spacenet datasetSection 1 – Tiramisu and Manipulating the Truth Think of this like Bohemian Rhapsody for deep learning (minus the Grammy Hall of Fame). Finally, we take creative liberties to think about how we might apply these types of deep learning solutions in a broader operational sense using conditional random fields, percolation theory, and reinforcement learning. We next look at how we might exploit the material properties of the road surface itself by using the spectral aspect of the data to create a deep learning solution tailored for a specific spectral signature. The first part of this blog describes how to directly leverage the full 8-band imagery and manipulate ground truth labels to obtain excellent road networks with relative ease and excellent performance. In this post, we approach the current SpaceNet challenge from distinct perspectives. For more details, check out the SpaceNet data repository on AWS and see our previous NVIDIA Developer Blog post on past SpaceNet challenges to extract building footprints. This move towards automated extraction of road networks will help bring innovation to computer vision methodologies applied to high-resolution satellite imagery and ultimately help create better maps where they are needed most such as humanitarian efforts, disaster response, and operations. In the third SpaceNet challenge, competitors were tasked with finding automated methods for extracting map-ready road networks from high-resolution satellite imagery. Today, map features such as roads, building footprints, and points of interest are primarily created through manual techniques. It’s that time again - SpaceNet raised the bar in their third challenge to detect road-networks in overhead imagery around the world.
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