Update README.md

README.md

@@ -4,16 +4,40 @@ license: apache-2.0
 
 ## Overview
 
-[SatlasPretrain](https://satlas-pretrain.allen.ai) is a large-scale remote sensing image understanding dataset.
-The models here are Swin Transformer backbones pre-trained on either the high-resolution images or the Sentinel-2 images in SatlasPretrain.
+[SatlasPretrain](https://satlas-pretrain.allen.ai) is a large-scale remote sensing image understanding dataset,
+intended for pre-training powerful foundation models on a variety of types of satellite and aerial images.
+It pairs remote sensing images with hundreds of millions of labels derived from [OpenStreetMap](https://www.openstreetmap.org/), [WorldCover](https://esa-worldcover.org/), other existing datasets, and new manual annotation.
 
-- `satlas-model-v1-highres.pth` is applicable for downstream tasks involving 0.5-2.0 m/pixel satellite or aerial imagery.
-- `satlas-model-v1-lowres.pth` is applicable for downstream tasks involving [Sentinel-2 satellite images](https://sentinel.esa.int/web/sentinel/missions/sentinel-2).
+Quick links:
+
+- [Learn more about the dataset](https://satlas-pretrain.allen.ai)
+- [Download the dataset](https://github.com/allenai/satlas/blob/main/SatlasPretrain.md)
+- [Fine-tune the pre-trained foundation models](https://github.com/allenai/satlaspretrain_models/)
+
+## Dataset
+
+The dataset is contained in the tar files in the `dataset/` folder of this repository.
+Our [Github repository](https://github.com/allenai/satlas/blob/main/SatlasPretrain.md) contains details about the format of the dataset and how to use it, as well as pre-training code.
+
+The dataset is released under [ODC-BY](https://github.com/allenai/satlas/blob/main/DataLicense).
+
+## Models
+
+The models here are Swin Transformer and Resnet backbones pre-trained on the different types of remote sensing images in SatlasPretrain:
+
+- Sentinel-2
+- Sentinel-1
+- Landsat 8/9
+- 0.5 - 2 m/pixel aerial imagery
 
 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.
-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.
+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 accessing global geospatial data generated by AI from satellite imagery.
+
+[See here](https://github.com/allenai/satlaspretrain_models/) for details on how to use the models and the expected inputs.
+
+The model weights are released under [ODC-BY](https://github.com/allenai/satlas/blob/main/DataLicense).
 
-## Usage and Input Normalization
+### Usage and Input Normalization
 
 The backbones can be loaded for fine-tuning on downstream tasks:
 
@@ -26,13 +50,7 @@ The backbones can be loaded for fine-tuning on downstream tasks:
     swin_state_dict = {k[len(swin_prefix):]: v for k, v in full_state_dict.items() if k.startswith(swin_prefix)}
     model.load_state_dict(swin_state_dict)
 
-The expected input is as follows:
-
-- `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.
-- `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.
-
-Please see [the SatlasPretrain github](https://github.com/allenai/satlas/blob/main/SatlasPretrain.md) for more examples and usage options.
-Models that use nine Sentinel-2 bands are also available there.
+They can also be easily initialized using the lightweight [`satlaspretrain_models` package](https://github.com/allenai/satlaspretrain_models/).
 
 ## Code