ForestMonster

ForestMonster is an open-source artifical intelligence model with the goal of creating a one-meter high resolution maps of US and Canadian forests to better understanding of forest health, biodiversity, and carbon sequestration.

We plan to acheive this with fused input data from aerial active remote sensing (3DEP LiDAR data) and satellite passive remote sensing (Sentinel-2 imagery data), and, initially, labelled data from in-situ forest surveys from the Michigan Department of Natural Resources to build and evaluate an AI model capable of classifying forest types. The repository is designed to facilitate data collection, model training, and prediction in a reproducible and scalable manner.

Citation

Please cite us with the below:

@software{Anderson_ForestMonster_-_AI_2024,
  author={Anderson, Reed},
  doi={10.5281/zenodo.13333314},
  month=aug,
  title={{ForestMonster - AI Enables 1-Meter Resolution Forestry Maps Using Aerial and Satellite Remote Sensing Fusion}},
  url={https://github.com/reedanders/ForestMonster},
  version={0.0.1},
  year={2024}
}