The tool sets provided by Cosmiq Works provide useful methods to convert from the line string graph formats into a segmentation mask allowing the user to specify the width of the segmented road. To infer road networks using the SpaceNet data a number of preprocessing steps are required to create segmentation masks for training and evaluation. Given that the feature maps are shared throughout the dense blocks this aids multi-scale supervision and introduces skip connections within and outside of each block, a feature shown to be extremely successful in Residual Networks. The advantages of these modifications include deeper supervision between layers. From a design perspective, Tiramisu extends the U-Net architecture adding Densely Connected Convolutional Networks into the network through conversion into fully convolutional layers to enable upsampling.
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This network was chosen because it can provide highly accurate semantic segmentation masks for a variety of segmentation tasks and easily extends to the full 8-channel data (in contrast to just 3-channel RGB data). Here we use the Semantic Segmentation Suite, written by George Seif, which incorporates many different segmentation methods. In this section the Tiramisu network is used which is readily available in many forms on GitHub. Section 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.
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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.
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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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