Update README.md

README.md

@@ -7,4 +7,100 @@ tags:
 - earth-observation
 - remote-sensing
 - foundation-model
----
+---
+
+# GeoSANE: Learning Geospatial Representations From Models, Not Data
+
+GeoSANE is a geospatial model foundry that operates directly in model-weight space. Instead of training a downstream model from scratch, GeoSANE learns a shared latent representation over a population of pretrained remote sensing models and uses that representation to generate new model candidates for a target architecture. These generated models can then be evaluated and fine-tuned on downstream tasks.
+
+This repository provides a demo project for running GeoSANE on a downstream remote sensing benchmark. The included notebook walks through the full evaluation pipeline for TIMM backbones on the Sen1Floods11 segmentation task: preparing the downstream dataset, loading a trained GeoSANE checkpoint, generating model candidates, fine-tuning and saving the resulting checkpoint.
+
+## Project Contents
+
+- `geosane-demo.ipynb`: end-to-end demo notebook
+- `shrp/`: the core SHRP library used by GeoSANE for weight tokenization, latent sampling, model reconstruction, evaluation, and fine-tuning
+- `downstream_datasets/`: downstream benchmark loaders, including Sen1Floods11, SpaceNet, EuroSAT, DIOR, fMoW, and others
+- `requirements.txt`: broad dependency list for setting up an environment
+- `requirements-lock.txt`: pinned versions from a working environment
+- `anchor_tokenized/`: cached tokenized anchor-model datasets generated during evaluation
+- `checkpoints/`: fine-tuned model checkpoints written during notebook runs
+
+## Quick Start
+
+Create an environment and install dependencies:
+
+```bash
+python -m venv .venv
+source .venv/bin/activate
+pip install --upgrade pip
+pip install -r requirements.txt
+```
+
+If you want to reproduce the exact environment used for this demo as closely as possible, use:
+
+```bash
+pip install -r requirements-lock.txt
+```
+
+Note: the lock file includes environment-specific PyTorch builds. You may need to install a compatible `torch` / `torchvision` pair first and then install the remaining dependencies.
+
+## Downstream Task
+
+[A Jupyter notebook](https://github.com/HSG-AIML/GeoSANE/blob/main/geosane-demo.ipynb) is provided in [GeoSANE GitHub page](https://github.com/HSG-AIML/GeoSANE) to show in detail how to use pretrained model weights. The details of GeoSANE are described in our paper, available on [arXiv](https://arxiv.org/abs/2603.23408).
+The demo notebook is currently configured for:
+
+- task: segmentation
+- downstream dataset: Sen1Floods11
+- generated backbone prompt: TIMM backbones such as `swin_s3_base_224.ms_in1k`
+
+The downstream dataset file created by the notebook is:
+
+- `data/sen1flood11_preprocessed.pt`
+
+This file is constructed from:
+
+```python
+trainset = Sen1Floods11HandLabeledDataset(split="train", resize_to=(224, 224))
+testset = Sen1Floods11HandLabeledDataset(split="val", resize_to=(224, 224))
+```
+
+
+## GeoSANE Checkpoints
+
+The notebook expects a trained GeoSANE run directory. The configured `model_path` must point to a directory that contains at least:
+
+- `params.json`
+- `checkpoint_000300/state.pt`
+
+In the notebook, this is configured through:
+
+```python
+EXPERIMENT = TimmExperimentConfig(
+    model_path=Path("/path/to/your/geosane_run"),
+    checkpoint_rel_path=Path("checkpoint_000300/state.pt"),
+    ...
+)
+```
+
+After downloading or copying the checkpoint directory, update `model_path` in `geosane-demo.ipynb` to your local path.
+
+## Outputs
+
+Running the notebook produces several artifacts:
+
+- `data/sen1flood11_preprocessed.pt`: preprocessed downstream dataset
+- `anchor_tokenized/`: tokenized anchor-model datasets used during sampling and evaluation
+- `checkpoints/`: fine-tuned model checkpoints saved during evaluation
+- `model_path/notebook_eval_results/`: JSON evaluation outputs
+
+
+## Acknowledging this work
+
+If you would like to cite our work, please use the following reference:
+
+* Hanna, Joelle, Damian Falk, Stella X. Yu and Damian Borth. *GeoSANE: Learning Geospatial Representations from Models, Not Data.*, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026.
+
+
+## Code
+This repository incorporates code from the following source:
+* [SANE](https://github.com/HSG-AIML/SANE)