1/13/2024 0 Comments Spacenet parkinsense![]() The 8-band raster image, at roughly 2 m ground sampling distance, contains both visible spectrum channels and near infrared channels with 16-bit values.The 3-band raster image, at roughly 0.5 m ground sampling distance, contains Red, Green, and Blue color channels with 8-bit values.For each sub-region, there are two images (GeoTIFFs) and one label (geoJSON): ![]() More information on SpaceNet is available here. The code snippets in this blog are shared under the following license: We implement the metric derived from Intersection-over-Union to compute an F1-score for the machine learning algorithm.We post-process the output to generate non-rectangular region proposals and output the proposed regions into geoJSON files.We train a weighted fully convolutional neural network to label the output the weights give feedback on the importance of bands and layers.We pre-process the labeled imagery using a distance transform to create a richer labeled data set.The classifier uses 11 bands within the satellite imagery.There are several properties of this challenge that distinguish it from other computer vision problems: Similar to many other computer vision problems, we desire to find objects (in particular, building footprints) in images (satellite imagery from SpaceNet). ![]() Sample Python code is included to demonstrate basic GIS functionality within Python and neural network design in TensorFlow. The strategy employed is far from optimal but rather is meant to illustrate a complete workflow based on a previous post. This post presents a walk through of an object detection process applied to SpaceNet imagery.
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