Task : Water Body Segmentation using DeepLabV3plus

This project implements water body segmentation using the DeepLabV3+ model. Mainly focus on the importance of ASPP in Deeplab V3+ for extracting the water bodies. It includes code for training the model, evaluating its performance, and metrics calculations.

Files Required:

deeplabv3Ex.py: DeepLabV3+ model
train2.py: Training the model code
eval.py: Model evaluation code
metrics.py: Evaluation metrics code

Installation:

git clone https://github.com/sunandhini96/Water_body_segmentation-DeepLabV3plus.git

cd Water_body_segmentation-DeepLabV3plus

Usage:

Run the training script to train the model:

python train2.py

To evaluate the trained model:

python eval.py

Dataset:

The project uses RGB satellite images and corresponding masks from Sentinel-2 A/B satellite. You can obtain the dataset https://www.kaggle.com/datasets/franciscoescobar/satellite-images-of-water-bodies

Methodology: Deeplab V3+ Architecture

Output:

RGB image, True mask image, predicted mask image without ASPP, Predicted mask image with ASPP

Citation:

If you use this code in your research, please cite our paper for more details.

More Details:

For a detailed explanation of the project and results, refer to our paper.