- WindowsFromGeojson: create windows based on a GeoJSON file of point features and acquire Sentinel-2 images. Then, train a model to predict the point positions.
- ProgrammaticWindows: a simple example of creating
windows programmatically, in case the
dataset add_windowscommand is insufficient for your use case. This example also shows how to programmatically add raster and vector data into your dataset. - NaipSentinel2: create windows based on the timestamp that NAIP is available. Then, acquire NAIP images at each window, along with Sentinel-2 images captured within one month of the NAIP image. This dataset could be used e.g. for super-resolution training.
- BitemporalSentinel2: acquire Sentinel-2 images from 2016 and 2024, and train a model to predict which is earlier. This example shows how to specify more complex model architectures (it applies SatlasPretrain independently on the two images and then concatenates the feature maps), and also how to add custom augmentations (to randomize the order of the images).