Land Delineation Training
Deep learning model training pipeline for delineating land from satellite imagery. Features preprocessing, BsiNet model implementation, and custom loss functions.
in-progress
private
Project Highlights
Developed a deep learning pipeline for land delineation using BsiNet architecture.
Implemented preprocessing modules using GDAL for satellite imagery (GeoTIFF) handling, including normalization, mask binarization, and resampling.
Created a custom PyTorch dataset loader handling images, masks, contours, and distance maps.
Defined and implemented custom loss functions including Dice Loss, Focal Loss, and a combined LossBsiNet.
Set up a training loop with TensorBoard logging for monitoring model performance.
Built a testing script to generate binary masks from model predictions and save them as GeoTIFFs.
Tech Stack
PythonPyTorchGDALOpenCVTensorBoard
This is a private project. Some details and links might be restricted.