12/7/2023 0 Comments Spacenet datasetIt’s a large corpus of labeled satellite imagery. SpaceNet launched in August 2016 as an open innovation project offering a repository of freely available imagery with co-registered map features. In this section, we provide more detail about the datasets we use in this post. For the complete code and notebooks of this tutorial, see our GitHub repo. By following our examples, you can train the ML models on AWS, apply the models to other regions where satellite or LiDAR data is available, and experiment with new ideas to improve the performances. This post demonstrates running ML services on AWS to extract features from large-scale geospatial data in the cloud. The notebooks reproduce winning algorithms from the SpaceNet challenges (which only use satellite images). In addition to the SpaceNet satellite images, we compare and combine the USGS 3D Elevation Program (3DEP) LiDAR data to extract the same. We show you how to launch an Amazon SageMaker notebook instance and walk you through the tutorial notebooks at a high level. Both datasets are hosted on the Registry of Open Data on AWS. In this post, we demonstrate how to extract buildings and roads from two large-scale geospatial datasets: SpaceNet satellite images and USGS 3DEP LiDAR data. Additionally, you can use these datasets in machine learning (ML) model development in the cloud. They are well structured and easily accessible. The datasets range from genomics to climate to transportation information. The Registry of Open Data on AWS hosts a large amount of public open data. Open Data on AWS helps you discover and share public open datasets in the cloud. Sharing data and computing in the cloud allows data users to focus on data analysis rather than data access.
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