Towards open-source LOD2 modelling using convolutional neural networks

Muftah, H.; Rowan, Tom; Butler, Adrian P.

doi:10.1007/s40808-021-01159-8

Cited by 8 publications

(2 citation statements)

References 27 publications

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“…In recent years, there has already been a growing interest in methodologies for the recognition and extraction of roof structures in a raster format using DL algorithms, including the works of [5,18,19]. However, for urban applications, the main interest lies in vectorized output.…”

Section: Related Workmentioning

confidence: 99%

Roof Structure Extraction from Aerial Images and nDSM using Deep Learning

Kenzhebay,

Zhao,

Koeva

et al. 2023

Preprint

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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.

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Section: Related Workmentioning

confidence: 99%

Roof Structure Extraction from Aerial Images and nDSM using Deep Learning

Kenzhebay,

Zhao,

Koeva

et al. 2023

Preprint

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“…The roof structure lines can be used for reconstructing 3D building models. Muftah et al (2021) used a CNN-based method for classifying and segmenting roofs based on aerial imagery for 3D building reconstruction in LoD2. The Deep Roof Definer network proposed by Qian et al, (2022) uses satellite imagery to generate roof structure lines using a detail-oriented DL network.…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Deep Learning for 3d Building Reconstruction: A Review

Buyukdemircioglu

Kocaman

Kada³

2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 3D building reconstruction using Earth Observation (EO) data (aerial and satellite imagery, point clouds, etc.) is an important and active research topic in different fields, such as photogrammetry, remote sensing, computer vision and Geographic Information Systems (GIS). Nowadays 3D city models have become an essential part of 3D GIS environments and they can be used in many applications and analyses in urban areas. The conventional 3D building reconstruction methods depend heavily on the data quality and source; and manual efforts are still needed for generating the object models. Several tasks in photogrammetry and remote sensing have been revolutionized by using deep learning (DL) methods, such as image segmentation, classification, and 3D reconstruction. In this study, we provide a review on the state-of-the-art machine learning and in particular the DL methods for 3D building reconstruction for the purpose of city modelling using EO data. This is the first review with a focus on object model generation based on the DL methods and EO data. A brief overview of the recent building reconstruction studies with DL is also given. We have investigated the different DL architectures, such as convolutional neural networks (CNNs), generative adversarial networks (GANs), and the combinations of conventional approaches with DL in this paper and reported their advantages and disadvantages. An outlook on the future developments of 3D building modelling based on DL is also presented.

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