Land Use and Land Cover Mapping Using Deep Learning Based Segmentation Approaches and VHR Worldview-3 images
This repository contains the code for the paper A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION.
- This work constructs a multi-task learning framework in which the main segmentation task is coupled with the auxilliary image reconstruction and edge extraction tasks. A homoscedastic uncertainty aware-objective function is formed where individual loss contributions are learned throughout the training procedure, along with the default weights.
Sample outputs, from left to right:
- Input Image,
- Segmentation Annotation,
- Predicted Segmentation Map,
- Edge Annotation,
- Predicted Edge Map,
- Reconstructed Input Image.
Simply download the repository and follow the main_notebook.ipynb after modifying the paths and the parameters in the params.py script.
The Spacenet6 dataset needs to be downloaded prior to running the main notebook (or use your own custom Dataset instance).
The code was implemented in Python(3.8) and PyTroch(1.14.0) on Windows OS. The segmentation models pytorch library is used as a baseline for implementation. Apart from main data science libraries, RS-specific libraries such as GDAL, rasterio, and tifffile are also required.
B. Ekim and E. Sertel, "A Multi-Task Deep Learning Framework for Building Footprint Segmentation," 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 2021, pp. 2500-2503, doi: 10.1109/IGARSS47720.2021.9554766.
If you encounter bugs while using this code, please do not hesitate to contact me.
Burak Ekim: burak.ekim@unibw.de