GeoSANE

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:

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:

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 is provided in GeoSANE GitHub page to show in detail how to use pretrained model weights. The details of GeoSANE are described in our paper, available on arXiv. 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:

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:

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