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Yayınlar
- ArticleYalnız künye2025G-ABORE: regularizing complex geometry building boundaries from pre-processed building LiDAR points using Bézier curve
Karsli, Fevzi; Ozdemir, Emirhan; Bahadir, Murat
The increasing use of Light Detection and Ranging (LiDAR) point clouds in vector map production highlights the need for a generalized approach to represent complex (linear and curvilinear) building boundary geometries, considering both technological advancements and architectural diversity. In this paper, the aim is to automatically regularize the complex geometric building boundaries extracted from LiDAR point clouds. In order to achieve this aim, building corner points were first detected with the Automatic Building Outline Regularization (ABORE) method. Then, a distance-based statistical method was implemented to determine the edge types representing the building boundary. If the building edges are linear, ABORE corner points are used. Conversely, if they are curvilinear, the edges are regularized using Bézier curves based on Bernstein polynomials. Thus, by generalizing the proposed ABORE method for regularizing only linear building boundaries, a novel method called Generalized Automatic Building Outline Regularization (G-ABORE) is developed to regularize building boundaries in complex geometry. The proposed method was implemented on the open-source Toronto, São Paulo, Minnesota, and locally produced Ordu LiDAR datasets. Regarding accuracy assessments, the overall performance of the novel method for building boundary regularization is 98.76%, 98.87%, 98.11%, and 98.99% for Completeness (Cp), Correctness (Cr), Quality (Q), and F-score metrics, respectively. In addition, RMSE and PoLiS positional accuracy values were calculated as 0.27 m and 0.24 m, respectively. The results demonstrate that the G-ABORE method effectively regularizes complex building boundaries with high accuracy. As a result, it is proven that the developed method can regularize building layers with an accuracy suitable for representation in vector maps, regardless of the structure of building boundary geometries extracted from point cloud data in fields such as architectural design, urban planning, and more.
RDL Kalıcı Kaydıİncele - ArticleYalnız künye2024FwSVM-Net: A novel deep learning-based automatic building extraction from aerial images
Yildirim, Feride Secil; Karsli, Fevzi; Bahadir, Murat; Yildirim, Merve
The building layer, one of the fundamental elements of mapping, features various layouts and architectures and is used in many mapping activities, such as urban planning, property management, illegal building detection, and disaster investigation. Therefore, accessing building information from remotely sensed images in a fast and automated manner is a popular topic. This paper aims to design a new Convolutional Neural Network (CNN) architecture called FwSVM-Net (Fast with Support Vector Machine Network) for building extraction. The FwSVM-Net, with 16 convolutional layers, employs a fusion mechanism to reduce the semantic gap between the encoder and decoder sections. In the architecture's classification layer, a Support Vector Machine (SVM) is used to increase segmentation accuracy while reducing parameter density. The Massachusetts Building Dataset was used for the automatic building extraction from aerial images. The performance of the FwSVM-Net on the test data was calculated at 97 %, 91 %, 87 %, 89 %, and 86 % for Overall Accuracy (OA), Precision (Pr), Recall (Rc), F1-Score (F1), and Intersection over Union (IoU), respectively. FwSVM-Net shows approximately ±2 % similarity in accuracy with the U-Net architecture, which is trained and evaluated with the same dataset. Despite this, the fact that the FwSVM-Net completed the training process approximately twice as fast as the U-Net architecture provides a significant time advantage. As a result, it is predicted that the FwSVM-Net will offer speed and convenience in terms of usability because of its performance, which is equivalent to the performance of the U-Net architecture for segmentation, and its time advantage.
RDL Kalıcı Kaydıİncele