🚧 Under Development 🚧

Building Segmentation on LR-HR-SR Satellite Imagery

This repository contains code for training and validating segmentation models to perform building delineation on different types of satellite imagery: Low-Resolution (LR), High-Resolution (HR), and Super-Resolution (SR). The goal is to compare the performance of segmentation models across these varying resolutions.

Overview

The project leverages PyTorch Lightning for model training and Weights & Biases (W&B) for experiment tracking. It includes scripts to train segmentation models and validate them by calculating relevant metrics.

Project Structure

• train.py: Script to train the segmentation models using configurations specified in YAML files.
• validate.py: Script to validate the trained models and calculate segmentation metrics.
• configs/: Directory containing YAML configuration files for different training setups.
• model_files/: Contains model definitions and utilities.

Models

The following segmentation models are implemented and can be selected through the configuration files in the configs/ directory:

Model Name	Number of Bands	Pretrained Status
UNet	4	Scratch
UNet++	4	Scratch
DeepLabV3	4	Scratch
DeepLabV3+	4	Scratch
TorchGeo ResNet18	3	Backbone pretrained on S2 RGB
TorchGeo FarSeg	3	Backbone pretrained on S2 RGB

These models are customizable via YAML configurations and are compatible with LR, HR, and SR imagery workflows. Important settings when changing models:

• Set number of bands in both model and data section
• Set appropriate loss, define wether sigmoid needs to be applied

Usage

To train a segmentation model:

1. Update Configuration: Modify the configuration files in the configs/ directory to set your training parameters. Things to consider:

• Model Selection: Currently implemented are DeepLabV3, UNet and UNet++
• Training parameters: Optimizers, Schedulers, LRs etc
• Set the LR-SR-HR paramter
• if using dataloaders from this project, make sure to change the data information like path and interpolation setttings

2. Run Training: Run train.py to start training, adjust which config to use. Either pass config file as argument from CL, or hardcode.

python train.py configs/config_hr.yaml

3. Validate: Run validate.py

• Give models and loaded weights + dataloaders to opensr-usecases package to get validation metrics, hardcoded in script.
• output in package format, as both text and mAP curve image.
• optionally, send output to txt file

python validate.py >> logs/output.txt

Training Status

Check the training status at WandB

Example Validation Output

The validation is based on an external package. It outputs a numerical caluclation of the improvement of SR basic imagery over LR, as well as mAP curves for all data types