#Spacenet challenge code#
#Spacenet challenge download#

#Spacenet challenge download#

Once you’ve done that, simply run the command below to download the training dataset to your working directory: aws s3 cp s3://spacenet-dataset/spacenet/SN7_buildings/tarballs/SN7_buildings_. SpaceNet 6) all you need is an AWS account and the AWS CLI installed and configured. Figure 1 links to an interactive map that allows interested readers to explore the dataset locations.Īs always, the SpaceNet data is freely available on AWS. The remainder of the data cubes are reserved for final testing purposes. 60 of the data cubes are released as training examples (with both imagery and attendant labels), and 20 are released as test_public examples (imagery only). Many of the locations were selected to highlight signifiant change, though some were selected to overlap with previous SpaceNet locales, and some were selected due to geopolitical interest. The SpaceNet 7 dataset contains ~100 locations, spread out across the globe. In the sections below we discuss the many facets of this unique open source dataset, distributed under a permissive CC BY-SA 4.0 license. The dynamic nature of the data cubes permits tracking of urban development, specifically: building footprint evolution as well as address propagation.

The selected locations boast significant change over the two year data collection timeline. This is the first time that SpaceNet has used Planet imagery at ~4 meter resolution, as well as the first time that the temporal dimension will be explicitly incorporated into a challenge. Up-to-date high-fidelity maps are crucial for many applications (such as disaster response or humanitarian efforts, discussed in great detail in our previous blog), and this dataset will aid efforts to improve automated mapping and overhead change detection methods. The SpaceNet 7 Multi-Temporal Urban Development Challenge dataset features a deep temporal stack of imagery and labels in over 100 locations across all six inhabited continents. The SpaceNet partners are proud to announce the release of the SpaceNet 7 dataset. SpaceNet is run in collaboration by co-founder and managing partner CosmiQ Works, co-founder and co-chair Maxar Technologies, and our partners including Amazon Web Services (AWS), Capella Space, Topcoder, IEEE GRSS, the National Geospatial-Intelligence Agency and Planet. Preface: SpaceNet LLC is a nonprofit organization dedicated to accelerating open source, artificial intelligence applied research for geospatial applications, specifically foundational mapping (i.e., building footprint & road network detection). Total training and inference times are calculated based upon an AWS p3.The SpaceNet 7 Multi-Temporal Urban Development Challenge: Dataset Release Note that the total contribution to the total NN’s ensembled is listed in parentheses in the Architectures column. The model architectures, ensemble and pre-training schemes, as well as training and inference time for each of the winning solutions. We also report model precision (ratio of false predictions) and recall (ratio of missed ground truth polygons): The overall score represents the SpaceNet Metric (x 100) for the entire scoring set. See the blog post on CosmiQ Works' blog The DownlinQ for an additional summary.Ĭompetitors’ scores in the SpaceNet 6: Multi-Sensor All Weather Mapping Challenge compared to the baseline model. Each subdirectory contains the competitors' written descriptions of their solution to the challenge.

#Spacenet challenge code#

The five subdirectories in this repository comprise the code for the winning solutions of SpaceNet 6: Multi-Sensor All Weather Mapping Challenge hosted by TopCoder. SpaceNet 6: Multi-Sensor All Weather Mapping Competitor Solutions