Towards General-Purpose Representation Learning of Polygonal Geometries

Code for recreting the results in our GeoInformatica 2023 paper

Related Link

1. Springr Paper
2. Arxiv Paper

Award

1. This paper won AAG 2023 J. Warren Nystrom Award (1 award recipient every year)
2. This paper won AAG 2022 William L. Garrison Award for Best Dissertation in Computational Geography (1 award recipient every other year)

Our Model Overview

Dependencies

• Python 3.7+
• Torch 1.7.1+
• Other required packages are summarized in requirements.txt.

Data

Download the required dbtopo datasets from here and put them in ./data_proprocessing/dbtopo/output/ folder. The folder has two datasets:

1. DBSR-46K: the pgon_triples_geom_300_norm_df.pklfile, a GeoDataFrame contain the DBSR-46K spatial relation prediction dataset created from DBpedia and OpenStreetMap. Each row indicates a triple from DBpedia and its subject and object are presented as a simple polygon with 300 vertices.
2. DBSR-cplx46K: the pgon_triples_geom_300_norm_df_complex.pkl file, a GeoDataFrame contain the spatial relation prediction dataset. The only difference is each row's subject and object are presented as a complex polygon with 300 vertices.

Train and Evaluation

The main code are located in polygoncode folder

1. 1_pgon_dbtopo.sh do suprevised training on both DBSR-46K and DBSR-cplx46K datasets.

Reference

If you find our work useful in your research please consider citing our GeoInformatica 2023 paper.

@article{mai2023towards,
  title={Towards general-purpose representation learning of polygonal geometries},
  author={Mai, Gengchen and Jiang, Chiyu and Sun, Weiwei and Zhu, Rui and Xuan, Yao and Cai, Ling and Janowicz, Krzysztof and Ermon, Stefano and Lao, Ni},
  journal={GeoInformatica},
  volume={27},
  number={2},
  pages={289--340},
  year={2023},
  publisher={Springer}
}

Please go to Dr. Gengchen Mai's Homepage for more information about Spatially Explicit Machine Learning and Artificial Intelligence.