| Autor: |
Carlos Campoverde, Mila Koeva, Claudio Persello, Konstantin Maslov, Weiqin Jiao, Dessislava Petrova-Antonova |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
Remote Sensing, Vol 16, Iss 8, p 1386 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2072-4292 |
| DOI: |
10.3390/rs16081386 |
| Popis: |
Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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