GEO-Bench: Toward Foundation Models for Earth Monitoring

GEO-Bench is a ServiceNow Research project.

GEO-Bench is a General Earth Observation benchmark for evaluating the performances of large pre-trained models on geospatial data. Read the full paper for usage details and evaluation of existing pre-trained vision models.

Installation

GEO-Bench requires Python 3.12 or newer and is installed from PyPI:

pip install geobench

The base install is enough to download and load the benchmark. The plotting helpers in geobench.plot_tools additionally need the plot extra (matplotlib, pandas, seaborn), and the PyTorch Lightning data module in geobench.torch_toolbox needs the torch extra:

pip install "geobench[plot,torch]"

Release 1.0.0 capped Python below 3.13 and pinned outdated upper bounds on its dependencies. Release 1.1.0 removes both, so installing from main is no longer necessary.

Downloading the data

Set $GEO_BENCH_DIR to your preferred location. If not set, it will be stored in $HOME/dataset/geobench.

Next, use the download script. This will automatically download from Hugging Face

Run the command:

geobench-download

You need ~65 GB of free disk space for download and unzip (once all .zip are deleted it takes 57GB). If some files are already downloaded, it will verify the md5 checksum. Feel free to restart the downloader if it is interrupted.

Using data you already have

If the benchmark is already on disk, either from an earlier download or from a copy shared with you, there are two ways to point geobench at it.

The first is $GEO_BENCH_DIR, which is read when geobench is imported and therefore has to be set in the environment before Python starts:

export GEO_BENCH_DIR=/path/to/geobench

Setting os.environ["GEO_BENCH_DIR"] or reassigning geobench.GEO_BENCH_DIR after the import has no effect.

The second is benchmark_dir, which reads a benchmark from elsewhere without changing that default. The tasks task_iterator yields read their datasets from the directory given here too.

import geobench

for task in geobench.task_iterator(benchmark_dir="/shared/geobench/classification_v1.0"):
    dataset = task.get_dataset(split="train")

benchmark_dir is the path to a single benchmark rather than to the directory holding several of them, and it takes the place of both $GEO_BENCH_DIR and benchmark_name.

Test installation

You can run tests. Note: Make sure the benchmark is downloaded before launching tests.

pip install pytest

geobench-test

Loading Datasets

See example_load_dataset.py for how to iterate over datasets.

import geobench

for task in geobench.task_iterator(benchmark_name="classification_v1.0"):
    dataset = task.get_dataset(split="train")
    sample = dataset[0]
    for band in sample.bands:
        print(f"{band.band_info.name}: {band.data.shape}")

Known issues

The m-eurosat and m-brick-kiln datasets in classification_v1.0 record the wrong Sentinel-2 band for most of their channels. The pixel data is unaffected; only the band name and wavelength stored for each channel are wrong, so selecting channels by band name returns the wrong channel. Reported by @gabrieltseng in #28 and #29.

The 13 channels are actually in this order:

Channel	m-eurosat	m-brick-kiln
0–4	B01–B05	B01–B05
5	B06	B07
6	B07	B8A
7	B08	B08
8	B09	B11
9	B10	B12
10	B11	TCI_R
11	B12	TCI_G
12	B8A	TCI_B

The stored metadata instead labels all 13 channels, in both datasets, as B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B10, B11, B12. In m-brick-kiln the source pipeline (mliu356/kiln-scaling) does not export B06, B09 or B10, and its last three channels are 8-bit true-colour composites rather than reflectance bands.

The converters in make_benchmark/dataset_converters/ now record the order above; the data on Hugging Face is not regenerated, so apply this mapping when loading it. Loading either dataset through GeobenchDataset emits a warning to this effect.

Fine-tuning and reproducing experiments

See the code for reproducing experiments as a starting point for fine-tuning:

geo-bench-experiments

Visualizing Results

See the notebook baseline_results.ipynb for an example of how to visualize the results.