The elevation field is always zero for this dataset. Here we show the first building label associated with the image note that coordinates are stored as a WKT polygon or multipolygon with coordinates stored as. First entry of the GeoJSON label file associated with Figure 2. Upon downloading and expanding the tarballs, the TopCoder training directory structure should appear as follows:įigure 3. In short, the command to download processed 200m x 200m image tiles with associated building footprints is: aws s3api get-object -bucket spacenet-dataset \ -key AOI_1_Rio/processedData/ \ -request-payer requester įor this post, we will focus on the TopCoder challenge dataset. Detailed descriptions of data formats and download instructions can be found here. December 2017 update: updated code is also available here.Īfter creating an AWS account, download the data at the SpaceNet AWS portal.We include python code for the interested reader, and refer the reader to the SpaceNet Challenge repository for more utilities. Further motivating the study of SpaceNet data is the release of a new SpaceNet point of interest dataset. This post aims to lower the barrier of entry for exploring SpaceNet data by demonstrating methods to transform and visualize the raw SpaceNet GeoJSON labels into formats more conducive for machine learning, namely NumPy arrays and image masks. This dataset contains a massive amount of labeled data in GeoJSON files, a format that may be unfamiliar to many in the computer vision field. The first SpaceNet challenge is complete, but the data remains available for download and analysis on AWS.
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