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license: apache-2.0 |
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## Overview |
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[SatlasPretrain](https://satlas-pretrain.allen.ai) is a large-scale remote sensing image understanding dataset. |
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The models here are Swin Transformer backbones pre-trained on either the high-resolution images or the Sentinel-2 images in SatlasPretrain. |
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- `satlas-model-v1-highres.pth` is applicable for downstream tasks involving 0.5-2.0 m/pixel satellite or aerial imagery. |
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- `satlas-model-v1-lowres.pth` is applicable for downstream tasks involving [Sentinel-2 satellite images](https://sentinel.esa.int/web/sentinel/missions/sentinel-2). |
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The pre-trained backbones are expected to improve performance on a wide range of remote sensing and geospatial tasks, such as planetary and environmental monitoring. |
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They have already been deployed to develop robust models for detecting solar farms, wind turbines, offshore platforms, and tree cover in [Satlas](https://satlas.allen.ai), a platform for global geospatial data generated by AI from satellite imagery. |
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## Usage and Input Normalization |
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The backbones can be loaded for fine-tuning on downstream tasks: |
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import torch |
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import torchvision |
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model = torchvision.models.swin_transformer.swin_v2_b() |
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full_state_dict = torch.load('satlas-model-v1-highres.pth') |
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# Extract just the Swin backbone parameters from the full state dict. |
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swin_prefix = 'backbone.backbone.' |
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swin_state_dict = {k[len(swin_prefix):]: v for k, v in full_state_dict.items() if k.startswith(swin_prefix)} |
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model.load_state_dict(swin_state_dict) |
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The expected input is as follows: |
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- `satlas-model-v1-highres.pth`: inputs 8-bit RGB high-resolution images, with 0-255 RGB values normalized to 0-1 by dividing by 255. |
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- `satlas-model-v1-lowres.pth`: inputs the TCI image from Sentinel-2 L1C scenes, which is an 8-bit image already processed from the B04 (red), B03 (green), and B02 (blue) bands. Normalize the 0-255 RGB values to 0-1 by dividing by 255. |
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Please see [the SatlasPretrain github](https://github.com/allenai/satlas/blob/main/SatlasPretrain.md) for more examples and usage options. |
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Models that use nine Sentinel-2 bands are also available there. |
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## Code |
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The training code and SatlasPretrain dataset are at https://github.com/allenai/satlas/. |
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SatlasPretrain is [a paper](https://arxiv.org/abs/2211.15660) appearing at the International Conference on Computer Vision in October 2023. |
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## Feedback |
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We welcome any feedback about the model or training data. |
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To contact us, please [open an issue on the SatlasPretrain github](https://github.com/allenai/satlas/issues). |
