- IntroExample: this is an introductory example to rslearn. It shows how to create an rslearn dataset with aligned Sentinel-2 images and WorldCover map data, how to train a model on the dataset, and how to apply the model to map land cover in new regions.
- 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 OlmoEarth 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).
- FinetuneOlmoEarth: fine-tune OlmoEarth to segment Sentinel-2 images for land cover and crop type mapping. In this example, the labels are taken from the USDA Cropland Data Layer.
- OlmoEarthEmbeddings: compute OlmoEarth embeddings on a location and time range of interest.