Article
Environmental Sciences
Xiao Li, Fang Qiu, Fan Shi, Yunwei Tang
Summary: This paper presents an efficient and automated workflow for generating building footprints from pre-classified LiDAR data. The workflow includes clustering LiDAR points, extracting outermost points, applying recursive convex hull algorithm, and developing a signal-based regularization algorithm. The proposed workflow achieves satisfying performance in generating building footprints even for building with complex structures.
Article
Environmental Sciences
Zhenyang Hui, Zhuoxuan Li, Penggen Cheng, Yao Yevenyo Ziggah, JunLin Fan
Summary: This study proposed a building extraction method from airborne LiDAR data based on multi-constraints graph segmentation, which converted point-based building extraction into object-based building extraction, and introduced a multi-scale progressive growth optimization method, achieving superior results.
Article
Environmental Sciences
Marko Bizjak, Domen Mongus, Borut Zalik, Niko Lukac
Summary: This paper presents a novel automatic building reconstruction methodology based on half-spaces and height jump analysis. The methodology includes three stages: preprocessing, sub-building division, and reconstruction with half-spaces. The performance of the methodology was demonstrated on a large scale and validated on an ISPRS benchmark dataset.
Article
Environmental Sciences
Marko Bizjak, Borut Zalik, Niko Lukac
Summary: This paper introduces a new approach based on half-spaces for automatically reconstructing 3D building models on a large scale, without assuming building layouts and minimizing the number of input parameters. Experimental results show accurate reconstruction of buildings with different layouts, but also identified limitations for large-scale applications.
Article
Engineering, Multidisciplinary
Yongji Yan, Hongyuan Wang, Zhiwei Dong, Zhaodong Chen, Rongwei Fan
Summary: In this paper, a novel method for extracting suburban residential building zone from airborne STIL data is proposed. The method uses a single data source and includes two correction algorithms, with one having a better extraction effect, demonstrating the feasibility of the proposed method.
Article
Geochemistry & Geophysics
Renato Cesar dos Santos, Guilherme Gomes Pessoa, Andre Caceres Carrilho, Mauricio Galo
Summary: This letter proposes a new approach that combines five estimation strategies to improve the robustness of boundary extraction from airborne LiDAR data.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Environmental Sciences
Spencer Dakin Kuiper, Nicholas C. Coops, Piotr Tompalski, Scott G. Hinch, Alyssa Nonis, Joanne C. White, Jeffery Hamilton, Donald J. Davis
Summary: Understanding changes in salmonid populations and their habitat is crucial due to climate change and their importance as a keystone species. Airborne Laser Scanning (ALS) data can be used to assess the quality and quantity of salmonid habitat, as well as characterize detailed stream attributes. ALS data provides detailed Digital Elevation Models (DEMs) and can be utilized for sustainable forest management decision making and advanced salmonid habitat modeling.
REMOTE SENSING OF ENVIRONMENT
(2022)
Article
Environmental Sciences
Jin Huang, Jantien Stoter, Ravi Peters, Liangliang Nan
Summary: This paper presents a fully automatic approach for reconstructing compact 3D building models from large-scale airborne point clouds. The approach addresses the challenge of missing vertical walls by inferring them directly from the data. The method outperforms state-of-the-art methods in terms of reconstruction accuracy and robustness, as demonstrated in experiments on various large-scale airborne LiDAR point clouds. Additionally, the authors have generated a new dataset with their method, which can stimulate research in urban reconstruction and the use of 3D city models in urban applications.
Article
Remote Sensing
Wangshan Yang, Xinyi Liu, Yongjun Zhang, Yi Wan, Zheng Ji
Summary: Building instance segmentation is crucial for parallel reconstruction, management, and analysis of building instances. Existing studies have mainly focused on building scenes with large building spacing, leading to low accuracy in complex building scenes and building point clouds. To address this, we propose a novel object-based building instance segmentation method using airborne LiDAR point clouds. The method divides point clouds into objects, classifies them based on roof plane, roof accessory, and building facade characteristics, and merges objects to obtain building instances.
INTERNATIONAL JOURNAL OF REMOTE SENSING
(2022)
Article
Environmental Sciences
Sani Success Ojogbane, Shattri Mansor, Bahareh Kalantar, Zailani Bin Khuzaimah, Helmi Zulhaidi Mohd Shafri, Naonori Ueda
Summary: A novel network based on an end-to-end deep learning framework is proposed for detecting and classifying urban building features, achieving an overall accuracy of over 80%. Morphological operations applied to extracted building footprints have improved the uniformity of building boundaries for increased accuracy in detecting buildings.
Article
Chemistry, Multidisciplinary
Young-Ha Shin, Kyung-Wahn Son, Dong-Cheon Lee
Summary: This paper focuses on utilizing the multiple returns in LiDAR data for building extraction using PointNet++. The experimental results show improved performance in building extraction. However, the method is limited by the lower point density in new data.
APPLIED SCIENCES-BASEL
(2022)
Article
Geochemistry & Geophysics
Juntao Yang, Zhizhong Kang, Perpetual Hope Akwensi
Summary: A new method is developed for detecting building roofs using airborne LiDAR point clouds and multispectral images, incorporating label-constraint approach for improved accuracy and efficiency in building roof region detection.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2021)
Article
Environmental Sciences
Lele Zhang, Jinhu Wang, Yueqian Shen, Jian Liang, Yuyu Chen, Linsheng Chen, Mei Zhou
Summary: This study proposes a workflow for automatically and accurately reconstructing the overhead wires of railways using deep learning and the RANSAC algorithm. Data augmentation and point cloud downsampling are used to address data issues, and a network is developed to segment wires, pylons, and ground points. The proposed method achieves better performance in wire identification and overall reconstruction accuracy compared to existing methods.
Article
Forestry
Shangshu Cai, Xinlian Liang, Sisi Yu
Summary: In this study, a progressive plane detection filtering (PPDF) method is proposed for processing airborne LiDAR point clouds for forestry applications. The method uses multi-scale planes to characterize terrain and detects planes in local point clouds by the random sample consensus method. Ground points are then extracted based on the reference terrain. PPDF was found to be more accurate and robust compared to classic filtering methods, with a minimal average total error and standard deviation of 3.42% and 2.45% respectively across all sites.
Article
Environmental Sciences
Zhan Cai, Hongchao Ma, Liang Zhang
Summary: Airborne LiDAR is an Earth observing system that directly acquires high-accuracy building roof data. This paper proposes a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, which can extract reliable and accurate building feature lines.