- #SPACENET CHALLENGE HOW TO#
- #SPACENET CHALLENGE FULL#
- #SPACENET CHALLENGE CODE#
- #SPACENET CHALLENGE SERIES#
The following graph shows the RGB value aggregated histogram of all images after processing.
To improve the visual feature extraction performance, we process the data by white balancing and convert it to 8-bit (0–255) values for ease of postprocessing. Among SpaceNet data categories, we use the 30cm resolution pan-sharpened 3-band RGB geotiff and corresponding building and road labels. Data registrationįor this post, we select the Las Vegas AOI where both SpaceNet satellite images and USGS LiDAR data are available. In this post, we use LiDAR data in the second format. The other format is in LAZ (compressed LAS) with requester-pays access. The following image shows an example of LiDAR visualization in Las Vegas. This format is suitable for online visualization.
#SPACENET CHALLENGE FULL#
The first is a public repository in Entwine Point Tiles (EPT) format, which is a lossless, full resolution, streamable octree structure. The USGS 3DEP LiDAR is presented in two formats.
These light pulses generate precise, 3D information about the shape of the Earth and its surface characteristics. LiDAR is a remote sensing method that emits hundreds of thousands of near-infrared light pulses each second to measure distances to the Earth. The program’s goal is to complete the acquisition of nationwide LiDAR to provide the first-ever national baseline of consistent high-resolution topographic elevation data, collected in a timeframe of less than a decade. Our second dataset comes from the USGS 3D Elevation Program (3DEP) in the form of LiDAR (Light Detection and Ranging) data. The following image is from DigitalGlobe’s SpaceNet Challenge Concludes First Round, Moves to Higher Resolution Challenges. For this post, we use Las Vegas the images in this AOI cover 216km2 areas with 151,367 building polygon labels and 3,685km road labels. The dataset covers 11 areas of interest (AOIs), including Rio de Janeiro, Las Vegas, and Paris.
#SPACENET CHALLENGE SERIES#
The project has also launched a series of competitions ranging from automatic building extraction, road extraction, to recently published multi-temporal urban development analysis. It’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.
#SPACENET CHALLENGE CODE#
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.
#SPACENET CHALLENGE HOW TO#
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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