Vectorizing planar roof structure from very high resolution remote sensing images using transformers

ABSTRACT

Accurately predicting the geometric structure of a building's roof as a vectorized representation from a raster image is a challenging task in building reconstruction. In this paper, we propose an efficient and precise parsing method called Roof-Former, based on a vision Transformer. Our method involves three steps: (1) Image encoder and edge node initialization, (2) Image feature fusion with an enhanced segmentation refinement branch, and (3) Edge filtering and structural reasoning. Our method outperforms previous works on the vectorizing world building dataset and the Enschede dataset, with vertex and edge heat map F1-scores increasing from $87.1\text{\%}$, $76.2\text{\%}$ to $89.1\text{\%}$, $78.1\text{\%}$, and from $69.7\text{\%}$, $68.8\text{\%}$ to $71.2\text{\%}$, $69.5\text{\%}$, respectively. Furthermore, our method demonstrates superior performance compared to the current state-of-the-art based on qualitative evaluations, indicating its effectiveness in extracting global image information while maintaining the consistency and topological validity of the roof structure.

KEYWORDS:

• Roof structure extraction
• very high resolution remote sensing
• Transformer
• geometry reconstruction

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The experiments conducted in this paper are based on two publicly available datasets, which can be accessed at Nauata and Furukawa (2020) and Zhao, Persello, and Stein (2022). Any inquiries regarding the datasets should be directed to the original authors.

Notes

1 Key Register Addresses and Buildings https://www.pdok.nl

Additional information

Funding

This work was supported by Foundation of Anhui Province Key Laboratory of Physical Geographic Environment, P.R. China [grant number 2022PGE012].