#SPACENET 7 DOWNLOAD#

#SPACENET 7 DOWNLOAD#

Once you’ve done that, simply run the command below to download the training dataset to your working directory: cd /working_directory/ aws s3 cp s3://spacenet-dataset/spacenet/SN7_buildings/tarballs/SN7_buildings_. All you need is an AWS account and the AWS CLI installed and configured. Data AccessĪs always with SpaceNet, data is freely available as part of the Registry of Open Data on AWS. Accordingly, and unsurprisingly, we use Solaris for the baseline algorithm. Furthermore, the significant effort put into data preprocessing greatly reduces the challenges inherent in using geospatial data for example the oft-annoying task of converting GeoJSON labels of a random coordinate reference system (CRS) into pixel coordinates or a pixel mask is trivial within Solaris. This package allows non-experts in the geospatial realm to build, train, test, and score machine learning models with a simple and well-documented workflow. Solaris is an open source machine learning pipeline for overhead imagery developed by CosmiQ Works. In this post we open source an algorithmic baseline for the SpaceNet 7 Challenge, as a means to both demonstrate that the proposed task is indeed feasible, and to provide a starting point that significantly lowers the barrier of entry for participants in the upcoming competition. As discussed in our SN7 announcement blog, this task is highly relevant to myriad humanitarian, urban planning, and disaster response scenarios. The SpaceNet 7 Challenge has an ambitious goal: identify and track building locations and change (i.e., construction) at the individual building identifier level, all from a deep temporal stack of medium resolution satellite imagery. 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).

Spacenet 7