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Abhatta7/building-footprint-segmentation

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Building Footprint Segmentation Models

This repository contains the pretrained U-Net, DeepLabV3+, and SegFormer model weights developed for my MS in Artificial Intelligence capstone project at DePaul University.

The models perform semantic segmentation to automatically extract building footprints from high-resolution satellite imagery using the SpaceNet 2 Paris Buildings dataset. The complete workflow includes preprocessing, binary mask generation, patch-based training, sliding-window inference, and object-level building counting. :contentReference[oaicite:0]{index=0}


Models Included

ModelFilename
U-Netbest_unet.pth
DeepLabV3+best_deeplab.pth
SegFormerbest_segformer.pth

Performance

ModelIoUDice ScoreMean Building Count Error
U-Net0.62560.74382.2087
DeepLabV3+0.72540.82893.2043
SegFormer0.70630.81652.0174

Key Findings

  • —DeepLabV3+ achieved the highest pixel-level segmentation accuracy (IoU and Dice Score).
  • —SegFormer achieved the lowest building count error, demonstrating superior object-level consistency.
  • —U-Net served as a strong baseline while maintaining competitive performance. :contentReference[oaicite:1]{index=1}

Dataset

SpaceNet 2 Paris Buildings

https://www.kaggle.com/datasets/ugorjiir/spacenet-2-paris-buildings

The dataset consists of:

  • —High-resolution RGB satellite images (.tif)
  • —Building footprint annotations (.geojson)
  • —Binary segmentation masks generated during preprocessing. :contentReference[oaicite:2]{index=2}

Training Overview

  • —Framework: PyTorch
  • —Patch Size: 512 × 512
  • —Batch Size: 4
  • —Epochs: 40
  • —Optimizer: Adam
  • —Learning Rate: 0.0001
  • —Loss Function: Binary Cross-Entropy + Dice Loss
  • —Sliding-window inference for full-resolution image prediction. :contentReference[oaicite:3]{index=3}

Repository Structure

text
.
├── best_unet.pth
├── best_deeplab.pth
├── best_segformer.pth
└── README.md

Main Project Repository

The complete source code, notebook, documentation, report, and implementation are available on GitHub:

https://github.com/AryakBhattacharya/building-footprint-extraction


Citation

If you use these pretrained models in your research, please cite:

bibtex
@misc{bhattacharya2026building,
  title={Deep Learning-Based Building Footprint Segmentation from Satellite Imagery},
  author={Aryak Bhattacharya},
  year={2026},
  school={DePaul University},
  type={MS Capstone Project}
}

Author

Aryak Bhattacharya

MS in Artificial Intelligence DePaul University

GitHub: https://github.com/AryakBhattacharya