Spacenet classifier decision2/3/2024 ![]() A traditional implementation strategy may look similar to this:Ĭhipping and clipping SpaceNet imagery into smaller areas to allow for deep learning consumption (create_spacenet_AOI.py) SpaceNet utilites helps to achieve this transformation. The SpaceNet imagery provided for this challenge must be processed and transformed into a deep-learning compatible format. Using SpaceNet Utilities to Process Imagery and Vector Data resultsOutputFile /path/to/SpaceNetResults.csv Python python/evaluateScene.py /path/to/SpaceNetTruthFile.csv \ To run the metric you can use the following command. Use the metric implementation code to self evaluate.All proposed polygons should be legitimate (they should have an area, they should have points that at least make a triangle instead of a point or a line, etc).The images provided could contain anywhere from zero to multiple buildings.The F1 score is between 0 and 1, where larger numbers are better scores. Let tp denote the number of true positives of the M proposed polygons. ![]() For this competition, the number of true positives and false positives are aggregated over all of the test imagery and the F1 score is computed from the aggregated counts.įor example, suppose there are N polygon labels for building footprints that are considered ground truth and suppose there are M proposed polygons by an entry in the SpaceNet competition. The F1 score is the harmonic mean of precision and recall, combining the accuracy in the precision measure and the completeness in the recall measure. The value of IoU is between 0 and 1, where closer polygons have higher IoU values. The measure of proximity between labeled polygons and proposed polygons is the Jaccard similarity or the “Intersection over Union (IoU)”, defined as: There is at most one “true positive” per labeled polygon. Otherwise, the proposed footprint is a “false positive”.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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