You can deploy this model and start earning money today!

HSG-AIML/
GeoSANE

safetensors

weight-space-learning

openai-community/gpt2

finetune

apache-2.0

Views

No views yet

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:

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:

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

HSG-AIML/GeoSANE