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license: apache-2.0 |
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language: |
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- en |
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## Wherobots MLM Models |
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This is a collection of example models implementing the Machine Learning Model Extension to the SpatioTemporal Asset Catalog (STAC) spec. Each metadata json describes a corresponding model asset and the requirements to run that model. |
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These examples, and [APIs built on top of them](https://wherobots.com/wherobotsai-for-raster-inference/), show the utility of describing data linead and runtime requirements of ML models. |
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The eventual goal is that most geospatial ML models are represented by MLM metadata, making it easy to run them on STAC datasets and derive more value. |
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See the MLM spec description if you want to learn more about the MLM description fields: https://github.com/crim-ca/mlm-extension |
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And also check out the stac-model package for creating and validating MLM metadata: https://github.com/crim-ca/mlm-extension/blob/main/README_STAC_MODEL.md |
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Each of these models is hosted and deployed in [WherobotsAI Raster Inference](https://wherobots.com/wherobotsai-for-raster-inference/), a tool for scaling ML models to planet-scale inference tasks. |
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Big thanks to [SATLAS](https://satlas.allen.ai/) and [TorchGeo](https://github.com/microsoft/torchgeo) for distributing open source code and weights for these models. |
