Welcome to the main repository for the "Green Eye" project! This platform is designed to monitor global forest changes in real-time, leveraging state-of-the-art AI tools and satellite datasets. Our project detects, evaluates, and predicts forest landscape anomalies, facilitating swift interventions for forest conservation.
Our model utilizes the following configurations:
- Data Source: Continental US Harmonised Landsat Sentinel 2 (HLS) data.
- Secondary Data Source: Pre-processed CDL (Cropland Data Layer) dataset, distinguishing between forest and non-forest classes.
- AI Tool: NASA's and IBM's pre-trained temporal vision transformer.
For detailed model architecture and configurations, please refer to the model_config.md file in the repository.
- APP Repo(UI): Green Eye UI Repository
- Tools Repo: Green Eye Tools Repository
- Change Analysis: Change Analysis Repository
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Clone the Repository:
git clone https://github.com/green-eye/main-repo.git
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Setup Dataset: We utilize the multi-temporal crop classification dataset available on HuggingFace for this project.
- Download and set up the dataset by following the instructions on its HuggingFace page.
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Data Transformation (From 12 classes to 2 classes): With the utilities from our Tools Repository, transform the 12-class geotiff files to 2 classes, representing forest and non-forest.
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Setup and Train Model: For setting up the environment and training the model, adhere to the instructions detailed in the NASA-IMPACT's hls-foundation-os repository. Follow the steps provided there to finalize the environment setup and initiate the training process for our model.