# Polygonizing Roof Segments from High-Resolution Aerial Images Using Yolov8-Based Edge Detection

arXiv: https://arxiv.org/abs/2503.09187 · HTML: https://mlsanity.com/paper/2503.09187/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 34 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 9 of 9)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Learning to Parse Wireframes in Images of Man-Made Environments](https://mlsanity.com/paper/2007.07527/) (2020)
- [PolyWorld: Polygonal Building Extraction with Graph Neural Networks in Satellite Images](https://mlsanity.com/paper/2111.15491/) (2021)
- [PPGNet: Learning Point-Pair Graph for Line Segment Detection](https://mlsanity.com/paper/1905.03415/) (2019)
- [Conv-MPN: Convolutional Message Passing Neural Network for Structured Outdoor Architecture Reconstruction](https://mlsanity.com/paper/1912.01756/) (2019)
- [Intuitive and Efficient Roof Modeling for Reconstruction and Synthesis](https://mlsanity.com/paper/2109.07683/) (2021)
- [Vectorizing World Buildings: Planar Graph Reconstruction by Primitive Detection and Relationship Inference](https://mlsanity.com/paper/1912.05135/) (2019)
- [Machine-learned 3D Building Vectorization from Satellite Imagery](https://mlsanity.com/paper/2104.06485/) (2021)
- [Optimized Vectorizing of Building Structures with Switch: High-Efficiency Convolutional Channel-Switch Hybridization Strategy](https://mlsanity.com/paper/2306.15035/) (2023)

## Abstract (arXiv)

This study presents a novel approach for roof detail extraction and vectorization using remote sensing images. Unlike previous geometric-primitive-based methods that rely on the detection of corners, our method focuses on edge detection as the primary mechanism for roof reconstruction, while utilizing geometric relationships to define corners and faces. We adapt the YOLOv8 OBB model, originally designed for rotated object detection, to extract roof edges effectively. Our method demonstrates robustness against noise and occlusion, leading to precise vectorized representations of building roofs. Experiments conducted on the SGA and Melville datasets highlight the method's effectiveness. At the raster level, our model outperforms the state-of-the-art foundation segmentation model (SAM), achieving a mIoU between 0.85 and 1 for most samples and an ovIoU close to 0.97. At the vector level, evaluation using the Hausdorff distance, PolyS metric, and our raster-vector-metric demonstrates significant improvements after polygonization, with a close approximation to the reference data. The method successfully handles diverse roof structures and refines edge gaps, even on complex roof structures of new, excluded from training datasets. Our findings underscore the potential of this approach to address challenges in automatic roof structure vectorization, supporting various applications such as urban terrain reconstruction.