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https://github.com/aiandglobaldevelopmentlab/temporal-eo-wealth

Official code repository for IJCAI 2023 paper "Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in Africa"

https://github.com/aiandglobaldevelopmentlab/temporal-eo-wealth

Science Score: 13.0%

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Repository

Official code repository for IJCAI 2023 paper "Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in Africa"

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  • Host: GitHub
  • Owner: AIandGlobalDevelopmentLab
  • Language: Jupyter Notebook
  • Default Branch: main
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  • Size: 66.3 MB
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Created about 3 years ago · Last pushed over 2 years ago
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README.md

Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in Africa (IJCAI 2023)

Lab | Paper | Appendix | Video | Generated maps

This is the official repository for the IJCAI 2023 paper "Time series of satellite imagery improve deep learning estimates of neighborhood-level poverty in Africa".

Authors: Markus Pettersson, Mohammad Kakooei, Julia Ortheden, Fredrik D. Johansson, Adel Daoud.

Apptainer environment

In order to improve reproducability, we ran all of our code using a single Apptainer (previously known as Singularity) container. This container can be built using the included recipe file apptainer_recipe.def as described in the apptainer documentation. Make sure you include the image path you select, e.g. path/to/image/location.sif, in your version of the configuration file config.ini.

To execute a .py script, simply run

bash $ apptainer run path/to/image/location.sif -nv path/to/script/file.py --script_args

in order to run one of the jupyter notebooks, you can start a jupyter lab session by running

bash $ apptainer exec path/to/image/location.sif -nv jupyter

Running trained single- and multi-frame models

Steps:

  1. Set up your local paths and other environment variables in the config.ini file.

  2. Download the satellite data, calculate the dataset variables and prepare the cross-validation folds as outlined in the preprocessing directory.

  3. Make predictions for the different pretrained models by running inference_model.py. In case your system is equipped with Slurm, you can simply run the inference_model.sh script

  4. Generate the figures as presented in the paper by running the evaluateresults/modelevaluation.ipynb and evaluateresults/tseffect.ipynb notebooks.

Acknowledgements

Preprocessing and evaluation code in this repository takes a lot of inspiration from the work by Yeh et al., creators of the architecture we call "single-frame model". You can find their codebase here.

Citation

Please cite our paper as

Markus B. Pettersson, Mohammad Kakooei, Julia Ortheden, Fredrik D. Johansson, & Adel Daoud (2023). Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, IJCAI-23 (pp. 6165–6173).

Or use the follwoing BibTex entry

@inproceedings{pettersson2023time, author = {Markus B. Pettersson and Mohammad Kakooei and Julia Ortheden and Fredrik D. Johansson and Adel Daoud}, title = {Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa}, booktitle = {Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI-23}}, pages = {6165--6173}, publisher = {International Joint Conferences on Artificial Intelligence Organization}, year = {2023}, month = {8} url = {https://doi.org/10.24963/ijcai.2023/684}, doi = {10.24963/ijcai.2023/684} }

Owner

  • Name: AIandGlobalDevelopmentLab
  • Login: AIandGlobalDevelopmentLab
  • Kind: organization

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