Spacenet buildinglabels example12/28/2023 ![]() We trained a UNet semantic segmentation model with a ResNet18 backbone, using the fastai/PyTorch plugin for Raster Vision for each dataset. (Light blue are ground truth, orange are shifted outlines) Training the models The figure below shows examples of scenes from a few of these noisy datasets.Įxamples of randomly dropped (top) and shifted (bottom) buildings. There are six datasets, each with a different level of random shifting in pixel units: 0, 10, 20, 30, 40, 50.įor each dataset, each individual polygon was shifted independently by two random numbers (for the x and y axes), each drawn independently from a uniform distribution between -k and k. The second series of noisy datasets contains randomly shifted buildings.There are six datasets, each generated with a different probability of dropping each building: 0.0, 0.1, 0.2, 0.3, 0.4, and 0.5. The first series of noisy datasets we generated contain randomly dropped (ie.The SpaceNet dataset contains a set of images, where for each image, there is a set of polygons in vector format, each representing the outline of a building. We generated a series of noisy datasets, trained a model on each one, and recorded the accuracy of the predictions against a held-out, uncorrupted set of scenes. Rather than making the errors completely random, we constrained them to approximate the types of mistakes we’ve observed in OSM - missing and shifted building outlines. We used the SpaceNet Vegas buildings dataset, which contains ~30k buildings labeled over 30cm DigitalGlobe WorldView-3 imagery. To do this, we took an off-the-shelf dataset, and systematically introduced errors into the labels. In order to measure the relationship between label noise and model accuracy, we needed a way to vary the amount of label noise, while keeping other variables constant. These lessons will inform the viability of machine learning as a component of disaster mapping.A training chip generated from OSM, showing labeling errors. We will discuss the dataset, the barriers to applying machine learning algorithms to off-nadir images, and the lessons learned about the limits of current state-of-the-art algorithms through this challenge. In this challenge, participants competed to develop the best building footprint extraction algorithm from the many different look angles provided. These data, provided to the public free of charge on Amazon Web Services (AWS) S3 with a CC-BY 4.0 license, were used in the SpaceNet Challenge Round 4 run on TopCoder. To enable exploration of look angle’s impact on automated foundational mapping, we open sourced a dataset of 27 views of Atlanta, GA taken from 7 to 54 degrees off-nadir during a single pass of a WorldView-2 satellite, along with high-quality, geographically accurate building footprint labels of the imaged area. buildings) in images taken at a significant angle? No existing public datasets contain many images acquired significantly off-nadir angles, and those datasets generally do not contain multiple looks of single geographies to evaluate the effect of look angle. To acquire images more quickly, early satellite imagery after disasters is often taken at a substantial angle away from directly overhead - often over 40 degrees “off-nadir” - but past studies have not answered a fundamental question for using AI for disaster response mapping: can algorithms identify features (e.g. Automation or partial automation of mapping using artificial intelligence presents an exciting opportunity to accelerate the process. Current efforts are too slow to aid immediate response efforts: for example, re-mapping of Puerto Rico by the Humanitarian Open Street Maps team and 5,300 volunteers took approximately two months. At present, re-mapping involves time-intensive, manual approaches where mappers spend significant time labeling buildings and roads in imagery. Creating new maps is thus is a central element of any disaster response effort. Re-mapping after natural disasters is critical for assessing damage, optimizing aid distribution, and more.
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