Skip to content

Deforestation forecasting with deep convolution neural networks for GREEN Meghalaya (PES)

License

Notifications You must be signed in to change notification settings

Rome-1/Meghalaya-PES

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

27 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

DeepFore[st]cast'

License: MIT

Leveraging deep convolutional neural networks to forecast tropical deforestation.

Citation

Please cite:

Thorstenson, R. (2024). Forecasting Deforestation in India with Deep Learning for the GREEN Meghalaya PES Program. Yale University.

Ball, J. G. C., Petrova, K., Coomes, D. A., & Flaxman, S. (2022). Using deep convolutional neural networks to forecast spatial patterns of Amazonian deforestation. Methods in Ecology and Evolution, 13, 2622– 2634. https://doi.org/10.1111/2041-210X.13953

Requirements

  • Python 3.8+
  • scikit-learn
  • torch 1.9.0
  • torchaudio 0.9.0
  • torchvision 0.10.0

See src/requirements/environment.yml for the complete list.

Getting started

First, create the directories and download the appropriate data. See https://github.com/PatBall1/DeepForestcast/tree/master for details. Also see the Makefile in the root, src/bash_scripts/run_data_load.sh, and src/main_data_load.py.

Next, build a config in src/configs.py, then call python3 src/models.py. It can take the parameters below. If preferred, use the bash scripts src/bash_scripts/.

--debug: Enables debugging mode by overriding model settings for a simpler and faster configuration. Disables Weights & Biases integration if --wandb_project is specified.

--skip_wandb: Disables Weights & Biases logging for the run.

--test_only: Skips training and runs only testing. Requires a pretrained model specified via --init_model.

--wandb_project: Specifies a custom W&B project for logging.

--init_model (str): Path to a pretrained model to initialize training or testing. The file should have a .pt extension.

--test_epochs (int): Specifies how often (in terms of epochs) to test the model during training.

--config (str): Path to the configuration file for the model.

--config_object (str): Specifies a Python object within the file provided by --config to load the model configuration.

About

Deforestation forecasting with deep convolution neural networks for GREEN Meghalaya (PES)

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published

Languages

  • Python 97.3%
  • Makefile 1.7%
  • Shell 1.0%