Presto
NASA HarvestPresto, the Pretrained Remote Sensing Transformer, is a lightweight self-supervised model from NASA Harvest for remote-sensing pixel timeseries. It consumes between one and twenty-four monthly timesteps of Sentinel-1 SAR, Sentinel-2 optical, ERA5 meteorology, SRTM topography, Dynamic World classes, and latitude and longitude, and exposes an encoder whose pooled embeddings are fine-tuned for downstream classification and regression.
The pretrained weights ship inside the MIT repository as data/default_model.pt rather than on a model hub, so the adoption band is read from GitHub stars. Verified 2026-09-10 via the repository README, LICENSE, and contents listings.
Openness
5 high confidence- license
- MIT(OSI)
- weights
- open(data/default_model.pt shipped in the MIT repository and loaded by the package's load_pretrained helper)
- data
- open(scripts/generate_data.py rebuilds the corpus end to end from Earth Engine, with the tile list, tile statistics and a sample shard shipped in the repository)
- code
- open(self-supervised train.py, eval.py, configs, and the end-to-end data-generation pipeline)
The weights, the self-supervised pretraining script, and the pipeline that rebuilds the corpus all ship under a plain MIT license with no added attribution or use clause, which is the ladder's top rung.
- https://raw.githubusercontent.com/nasaharvest/presto/main/README.md recorded 2026-09-10
Documents Presto.load_pretrained() reading the state dict from data/default_models.pt, a train.py self-supervised training entry point with a runnable local command, and scripts/generate_data.py generating the training tars from Earth Engine exports, reading the resulting webdataset shards through an authenticated `pipe:gcloud storage cat`. The bucket is not anonymously readable — the object returns 403 and listing returns 401 — so the data dimension rests on the reconstruction scripts rather than on a public download.
- https://raw.githubusercontent.com/nasaharvest/presto/main/LICENSE recorded 2026-09-10
Unmodified MIT License text, Copyright (c) 2024 Presto Authors, with no attribution or field-of-use clause added.
- https://api.github.com/repos/nasaharvest/presto/contents/data recorded 2026-09-10
Lists default_model.pt at 3.3 MB alongside dw_144_mini_shard_44.tar, dynamic_world_samples.geojson, tile_processing.txt, tile_stats.yaml, and v1_dw_tile_metadata_for_public_release.xlsx.
- https://api.github.com/repos/nasaharvest/presto/contents recorded 2026-09-10
Repository root carries train.py, eval.py, mosaiks.py, config, scripts, and the presto package, not inference code alone.
- https://api.github.com/repos/nasaharvest/presto recorded 2026-09-10
license: MIT; archived: false; fork: false.
Adoption
1 medium confidenceThe weights ship inside the repository rather than on a model hub, so no download count exists and 280 GitHub stars are used as a last-resort reach signal.
- https://api.github.com/repos/nasaharvest/presto recorded 2026-09-10
stargazers_count: 280; archived: false; fork: false.
Capability
3 high confidenceTwo attributes are stated directly. The encoder-decoder reconstructs masked inputs, which the category does not count as generative, and a variable count of monthly timesteps is one temporal scale rather than several.
- https://raw.githubusercontent.com/nasaharvest/presto/main/README.md recorded 2026-09-10
Shows construct_finetuning_model adding a linear head for finetuning and a jupyter notebook demonstrating finetuning on different downstream tasks, and specifies the input tensor as the S1, S2, ERA5, and SRTM bands plus Dynamic World classes and latitude and longitude.
Verified 2026-09-10