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TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning

Paper Conference

🚧 Constructing...

Overview

TorchSpatial offers a comprehensive framework and benchmark suite designed to advance spatial representation learning (SRL). It supports the development and evaluation of location encoders using extensive benchmarks and innovative evaluation metrics. The documentation is here.

Features

  • Unified Framework: Integrates 15 recognized location encoders to enhance scalability and reproducibility.
  • LocBench Benchmark: Includes 17 datasets for geo-aware image classification and regression, enabling thorough performance assessments across various geographic distributions.
  • Geo-Bias Score: A novel metric to evaluate model performance and geographic bias, promoting spatial fairness in GeoAI applications.

Availability

Access the TorchSpatial framework, LocBench benchmarks, and evaluation metrics on GitHub: TorchSpatial GitHub Repository.

Overall Framework

TorchSpatial Overall Framework

Environment Settings

Please ensure that the package versions used in your environment match those specified in requirements.txt.

Code Execution

Example bash scripts for running the code are available in the main/run_bash folder. A detailed tutorial can be found in the following Jupyter notebook: tutorial.ipynb.

Dataset Download Instructions

  1. You can download the dataset from the following DOI link: Download Data. As we have performed data cleaning and preprocessing, please use the provided dataset to ensure the code functions correctly.
  2. Please update the paths in paths.py to reflect the locations of the datasets on your system accurately.

Results

Table 1: The Top1 classification accuracy of different models on 7 geo-aware image classification datasets in LocBench benchmark. GPT-4V is tested with zero-shot settings, and * indicates that we resample 100 images from each dataset's test/validation set except BirdSnap and Birdsnap† whose whose test sets are used for evaluation. "Avg" column indicates the average performance of each model on all five species recognition datasets. Bold indicates the best models in Group B and C. Top1 classification accuracy

Table2: The R square of different models on 4 geo-aware image regression datasets in LocBench benchmark. Same format as above. R square

The average runtime for BirdSnap, BirdSnap†, NABirds†, and YFCC is within 5 minutes, and 10 to 20 minutes for iNat2017, iNat2018, and fMow. The number of training for each model on each dataset is one. Same as evaluation. All our experiments were conducted on a Ubuntu workstation equipped with 4 NVIDIA RTX A5500 GPUs each of which has 24 GB memory.

Reference

If you find our work useful in your research please consider citing our TorchSpatial paper and ISPRS PHOTO 2023 paper.

@article{wu2024torchspatial,
  title={TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning},
  author={Wu, Nemin and Cao, Qian and Wang, Zhangyu and Liu, Zeping and Qi, Yanlin and Zhang, Jielu and Ni, Joshua and Yao, Xiaobai and Ma, Hongxu and Mu, Lan and Ermon, Stefano and Ganu, Tanuja and Nambi, Akshay and Lao, Ni and Mai, Gengchen},
  journal={arXiv preprint arXiv:2406.15658},
  year={2024}
}

@article{mai2023sphere2vec,
  title={Sphere2Vec: A General-Purpose Location Representation Learning over a Spherical Surface for Large-Scale Geospatial Predictions},
  author={Mai, Gengchen and Xuan, Yao and Zuo, Wenyun and He, Yutong and Song, Jiaming and Ermon, Stefano and Janowicz, Krzysztof and Lao, Ni},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  year={2023},
  vol={202},
  pages={439-462},
  publisher={Elsevier}
}

If you use grid location encoder, please also cite our ICLR 2020 paper and our IJGIS 2022 paper:

@inproceedings{mai2020space2vec,
  title={Multi-Scale Representation Learning for Spatial Feature Distributions using Grid Cells},
  author={Mai, Gengchen and Janowicz, Krzysztof and Yan, Bo and Zhu, Rui and Cai, Ling and Lao, Ni},
  booktitle={International Conference on Learning Representations},
  year={2020},
  organization={openreview}
}

@article{mai2022review,
  title={A review of location encoding for GeoAI: methods and applications},
  author={Mai, Gengchen and Janowicz, Krzysztof and Hu, Yingjie and Gao, Song and Yan, Bo and Zhu, Rui and Cai, Ling and Lao, Ni},
  journal={International Journal of Geographical Information Science},
  volume={36},
  number={4},
  pages={639--673},
  year={2022},
  publisher={Taylor \& Francis}
}

If you use the unsupervised learning function, please also cite our ICML 2023 paper. Please refer to our CSP webite for more detailed information.

@inproceedings{mai2023csp,
  title={CSP: Self-Supervised Contrastive Spatial Pre-Training for Geospatial-Visual Representations},
  author={Mai, Gengchen and Lao, Ni and He, Yutong and Song, Jiaming and Ermon, Stefano},
  booktitle={International Conference on Machine Learning},
  year={2023},
  organization={PMLR}
}

License

Our code is under MIT license. All data products created through our work that are not covered under upstream licensing agreements are available via a CC BY-NC 4.0 license. All upstream data use restrictions take precedence over this license.