Version 1
: Received: 8 September 2023 / Approved: 12 September 2023 / Online: 12 September 2023 (08:36:45 CEST)
How to cite:
Kenzhebay, M.; Zhao, W.; Koeva, M.; Persello, C. Roof Structure Extraction from Aerial Images and nDSM using Deep Learning. Preprints2023, 2023090762. https://doi.org/10.20944/preprints202309.0762.v1
Kenzhebay, M.; Zhao, W.; Koeva, M.; Persello, C. Roof Structure Extraction from Aerial Images and nDSM using Deep Learning. Preprints 2023, 2023090762. https://doi.org/10.20944/preprints202309.0762.v1
Kenzhebay, M.; Zhao, W.; Koeva, M.; Persello, C. Roof Structure Extraction from Aerial Images and nDSM using Deep Learning. Preprints2023, 2023090762. https://doi.org/10.20944/preprints202309.0762.v1
APA Style
Kenzhebay, M., Zhao, W., Koeva, M., & Persello, C. (2023). Roof Structure Extraction from Aerial Images and nDSM using Deep Learning. Preprints. https://doi.org/10.20944/preprints202309.0762.v1
Chicago/Turabian Style
Kenzhebay, M., Mila Koeva and Claudio Persello. 2023 "Roof Structure Extraction from Aerial Images and nDSM using Deep Learning" Preprints. https://doi.org/10.20944/preprints202309.0762.v1
Abstract
A topic of growing interest in urban remote sensing is the automated extraction of geometrical building information for 3D city modeling. Roof geometry information is useful for applications such as urban planning, solar potential estimation and telecommunication installation planning, and wind flow simulations for pollutant diffusion analysis. Recent research has proven that the advance in remote sensing technologies and deep learning methods offer the prospects of deriving the roof structure information accurately and efficiently. In this study, we propose a Vectorized Roof Extractor- method based on Fully Convolutional Networks (FCNs) and advanced polygonization method to extract roof structure from aerial imagery and a normalized Digital Surface Models (nDSM) in a regularized vector format. The roof structure consists of building outlines, external edges of the building roof, inner rooflines, internal intersections of the main roof planes. The methodology is comprised of segmentation, vectorization and post-processing for outer rooflines, external edges of the building roof, and inner rooflines, and internal intersections of the main roof planes. For the comparison, we adapt the Frame field Learning (FFL) method originally designed to extract building polygons [1]. Our experiments are conducted on a custom data set derived for the city of Enschede, The Netherlands, using aerial imagery, nDSM and manually digitized training polygons. The results show that the proposed Vectorized Roof Extractor outperformed adapted FFL on PoLiS distance with values of 3.5 m and 1.2 m for outlines and inner rooflines, respectively. Furthermore, the model surpassed the adapted FFL on PoLiS-thresholded F-score for outlines and inner rooflines, with 0.31 and 0.57, respectively. The Vectorized Roof Extractor produced adequate visual results, with straighter walls and fewer missed inner roofline detections. It can predict buildings with common walls thanks to skeleton graph computation. To summarize, the proposed method is suitable for urban applications and has the potential to be improved further.
Keywords
image processing; image analysis; deep learning; roof structure extraction; roof vectorization; frame field learning
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
