A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION

This repository contains the code for the paper A MULTI-TASK DEEP LEARNING FRAMEWORK FOR BUILDING FOOTPRINT SEGMENTATION

Framework

Outputs

How to use it?

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.

The code was implemented in Python(3.8) and PyTroch(1.14.0) on Windows OS. The segmentation models pytorh 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.

Citation

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.

Contact Information:

If you encounter bugs while using this code, please do not hesitate to contact me.

Burak Ekim: burak.ekim@unibw.de