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Spacenet satellite imagery12/17/2023 The proposed footprint is a “true positive” if the proposal is the closest (measured by the IoU) proposal to a labeled polygon AND the IoU between the proposal and the label is about the prescribed threshold of 0.5.Each proposed building footprint is either a “true positive” or a “false positive”. A SpaceNet entry will generate polygons to represent proposed building footprints. For each building there is a geospatially defined polygon label to represent the footprint of the building. The evaluation metric for this competition is an F1 score with the matching algorithm inspired by Algorithm 2 in the ILSVRC paper applied to the detection of building footprints. Conda is a simple way to install everything and their dependencies Several packages require binaries to be installed before pip installing the other packages. This is version 3.0 and has been updated with more capabilities to allow for computer vision applications using remote sensing data Download Instructionsįurther download instructions for the SpaceNet Dataset can be found here Installation Instructions The labelTools package assists in transfering geoJson labels into common label schemes for machine learning frameworks The evalTools package is used to evaluate the effectiveness of object detection algorithms using ground truth. The geoTools packages is intended to assist in the preprocessing of SpaceNet satellite imagery data corpus hosted on SpaceNet on AWS to a format that is consumable by machine learning algorithms. This repository has three python packages, geoTools and evalTools and labelTools. Future development of code tools for geospatial machine learning analysis will be done at. This repository is no longer being updated.
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