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" # 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 evaluate_results/model_evaluation.ipynb and evaluate_results/ts_effect.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 th …