Merge branch 'main' of https://github.com/seai-lab/TorchSpatial

.github/workflows/python-package-conda.yml

@@ -0,0 +1,34 @@
+name: Python Package using Conda
+
+on: [push]
+
+jobs:
+  build-linux:
+    runs-on: ubuntu-latest
+    strategy:
+      max-parallel: 5
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python 3.10
+      uses: actions/setup-python@v3
+      with:
+        python-version: '3.10'
+    - name: Add conda to system path
+      run: |
+        # $CONDA is an environment variable pointing to the root of the miniconda directory
+        echo $CONDA/bin >> $GITHUB_PATH
+    - name: Install dependencies
+      run: |
+        conda env update --file environment.yml --name base
+    - name: Lint with flake8
+      run: |
+        conda install flake8
+        # stop the build if there are Python syntax errors or undefined names
+        flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
+        # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
+        flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
+    - name: Test with pytest
+      run: |
+        conda install pytest
+        pytest

README.md

@@ -1,7 +1,19 @@
 # TorchSpatial: A Location Encoding Framework and Benchmark for Spatial Representation Learning
 🚧 Constructing...
 
-![TorchSpatial Overall Framework](figs/TorchSpatial_task4.jpg)
+## 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.
+
+## 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](https://github.com/seai-lab/TorchSpatial).
+
+## Overall Framework
+![TorchSpatial Overall Framework](figs/TorchSpatial4_10regTasks.jpg)
 
 
 
@@ -10,9 +22,11 @@
 
 ## Data Download Instructions
 The data can be downloaded from the following DOI link:
-[Download Data](https://doi.org/10.6084/m9.figshare.26026798)
-
-Data should be organized following the ..
+[Download Data](https://doi.org/10.6084/m9.figshare.26026798
+
+
+
+        )
 
 ## Code Execution
 The example bash files for running the codes can be found in main/run_bash folder
@@ -68,8 +82,15 @@ All our experiments were conducted on a Ubuntu workstation equipped with 4 NVIDI
 
 
 ### Reference
-If you find our work useful in your research please consider citing [our ISPRS PHOTO 2023 paper](https://www.researchgate.net/publication/371964548_Sphere2Vec_A_General-Purpose_Location_Representation_Learning_over_a_Spherical_Surface_for_Large-Scale_Geospatial_Predictions).  
+If you find our work useful in your research please consider citing our TorchSpatial paper and [ISPRS PHOTO 2023 paper](https://www.researchgate.net/publication/371964548_Sphere2Vec_A_General-Purpose_Location_Representation_Learning_over_a_Spherical_Surface_for_Large-Scale_Geospatial_Predictions).  
 ```
+@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},
@@ -111,3 +132,6 @@ If you use the unsupervised learning function, please also cite [our ICML 2023 p
   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.