Getting Started With SpaceNet Data

The first SpaceNet challenge is complete, but the data remains available for download and analysis on AWS. 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. 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. Further motivating the study of SpaceNet data is the release of a new

• December 2017 update: updated code is also available here.

1. Data Access

After creating an AWS account, download the data at the SpaceNet AWS portal

aws s3api get-object --bucket spacenet-dataset \
    --key AOI_1_Rio/processedData/processedBuildingLabels.tar.gz \
    --request-payer requester processedBuildingLabels.tar.gz
 

For this post, we will focus on the TopCoder challenge dataset. Upon downloading and expanding the tarballs, the TopCoder training directory structure should appear as follows:

In this post we will focus on the high-resolution 3-band imagery as well as the vector data.

2. Data Inspection

Image cutouts for the pan-sharpened 3-band imagery are 438–439 pixels in width, and 406–407 pixels in height. 8-band images have not been pan-sharpened and so have 1/4 the resolution of the 3-band imagery at 110 x 102 pixels. For each unique image ID we find a corresponding entry in the vectordata/geoJson directory with image footprints.

2. Ground Truth Transform

Computer vision algorithms tend to operate in pixel space, where locations are reported on the matrix of pixel positions rather than latitude and longitude. After the initial data download, or extraction, the second step in the extract-transform-load (ETL) process is to transform the latitude-longitude coordinates in the GeoJSON label files to pixel coordinates. We describe three methods of transforming the GeoJSON label files into pixel coordinates in various formats.

2.1 Building Outline Coordinates

The GeoJSON file lists building polygon vertices in latitude and longitude. Transforming these vertices into pixel coordinates requires knowledge of the image extent and precise geometric coordinate transform. This information (along with much more) can be extracted with the GDAL code suite. A number of sophisticated functions using GDAL and other geospatial libraries are available in the SpaceNet utilities repository on GitHub. The code below takes the GeoJSON label file and corresponding image and returns two coordinate arrays, one in geospatial coordinates (latitude and longitude) and one in pixel coordinates.

We can inspect our transform by overlaying the ground truth polygons on the input image using matplotlib.