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Book ; Online: Remote Sensing based Building Extraction

Awrangjeb, Mohammad / Hu, Xiangyun / Yang, Bisheng / Tian, Jiaojiao

2020

Abstract: Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for ... ...

Abstract	Building extraction from remote sensing data plays an important role in urban planning, disaster management, navigation, updating geographic databases, and several other geospatial applications. Even though significant research has been carried out for more than two decades, the success of automatic building extraction and modeling is still largely impeded by scene complexity, incomplete cue extraction, and sensor dependency of data. Most recently, deep neural networks (DNN) have been widely applied for high classification accuracy in various areas including land-cover and land-use classification. Therefore, intelligent and innovative algorithms are needed for the success of automatic building extraction and modeling. This Special Issue focuses on newly developed methods for classification and feature extraction from remote sensing data for automatic building extraction and 3D
Keywords	Technology (General) ; Engineering (General). Civil engineering (General) ; Building construction
Size	1 electronic resource (442 pages)
Publisher	MDPI - Multidisciplinary Digital Publishing Institute
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020480984
ISBN	9783039283828 ; 9783039283835 ; 3039283820 ; 3039283839
DOI	10.3390/books978-3-03928-383-5
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Position-attitude calculation of panoramic image based on point-line feature combination

ZHU Ningning / YANG Bisheng / CHEN Chi / DONG Zhen

Acta Geodaetica et Cartographica Sinica, Vol 52, Iss 2, Pp 218-

2023 Volume 229

Abstract: At present, the position and attitude parameters of panoramic images are mostly solved by point features, while the line features commonly existing in the scene have not been fully utilized. In this paper, a solution method based on point-line feature ... ...

Abstract	At present, the position and attitude parameters of panoramic images are mostly solved by point features, while the line features commonly existing in the scene have not been fully utilized. In this paper, a solution method based on point-line feature combination is proposed, which can not only be used to solve the position and attitude of panoramic image in the scene with missing point features, but also improve the accuracy and robustness in the scene with sufficient point features. The line feature in this method is represented by any two points on the line, which does not require the corresponding relationship between the panoramic image and the 3D scene, so it is easy to select and has great practicability. Firstly, the direct linear transform (DLT) is used to construct the point-line feature combination model of panoramic image, and the simplified model is obtained for horizontal and vertical lines; Then, using the simulated road scene, the applicability of the model is analyzed from the two aspects: different combinations of feature points and lines, large attitude angle, and the tolerance is analyzed by manually introducing different types and magnitude of point-line errors; Finally, this method is applied to the fusion of panoramic image and LiDAR points. It is proved that this method of point-line feature combination is better than the simple point method in accuracy, robustness and tolerance.
Keywords	panoramic image ; point-line feature combination ; position and attitude calculation ; simulation analysis ; panoramic image and lidar points fusion ; Mathematical geography. Cartography ; GA1-1776
Subject code	004
Language	Chinese
Publishing date	2023-02-01T00:00:00Z
Publisher	Surveying and Mapping Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A hierarchical approach for refining point cloud quality of a low cost UAV LiDAR system in the urban environment

Yang, Bisheng / Li, Jianping

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS journal of photogrammetry and remote sensing. 2022 Jan., v. 183

2022

Abstract: Insufficient accuracies of the low end position and orientation system (POS) used in low cost UAV LiDAR systems (ULSs) cause the direct georeferencing data to lead to poor point cloud quality. Trajectory correction and scan-to-map matching are two ... ...

Abstract	Insufficient accuracies of the low end position and orientation system (POS) used in low cost UAV LiDAR systems (ULSs) cause the direct georeferencing data to lead to poor point cloud quality. Trajectory correction and scan-to-map matching are two commonly used strategies for point cloud quality refinement. The existing trajectory correction strategies work with the assumption that POS errors can be modeled as a time-variant function, which cannot be applied to the low end POS. The existing scan-to-map matching methods have difficulty refining the ULS point clouds due to the large gaps between scan lines. This paper proposes HR-ULS, hierarchical refinement for low cost ULS point cloud quality in the urban environment, to solve these challenges. HR-ULS separated the raw laser scanning point clouds into a set of scan-blocks and refined the point cloud quality with a hierarchical strategy, resulting in local and global optimization, respectively. First, the internal scan-block matching (ISBM) estimated multiscale distributions for each laser frame and calculated relative motions iteratively to achieve local map consistency in each scan-block. Second, the multiview scan-block matching (MSBM) took inertial, Global Navigation Satellite System (GNSS), and laser measurements into a unified adjustment framework to correct the trajectory, achieving global map consistency between scan-blocks. Comprehensive experiments evaluated the proposed HR-ULS with the point clouds captured by a low-cost ULS in three typical urban areas. They showed that the average plane fitting RMSE of the ULS point clouds was improved from 0.34 m to 0.09 m, and the average checkpoint offset was improved from 1.86 m to 0.21 m, achieving an identical level of accuracy with that of direct georeferencing using a high end POS, APX-15-UAV.
Keywords	data collection ; georeferencing ; global positioning systems ; lidar ; photogrammetry ; urban areas
Language	English
Dates of publication	2022-01
Size	p. 403-421.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2021.11.022
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: 3D geospatial information extraction of urban objects for smart surveying and mapping

YANG Bisheng / CHEN Chi / DONG Zhen

Acta Geodaetica et Cartographica Sinica, Vol 51, Iss 7, Pp 1476-

2022 Volume 1484

Abstract: 3D spatial-temporal information is indispensable for the major national needs of new infrastructure construction, digital twin cities, natural resource management and monitoring. The rapid development of surveying and mapping equipment is beneficial to ... ...

Abstract	3D spatial-temporal information is indispensable for the major national needs of new infrastructure construction, digital twin cities, natural resource management and monitoring. The rapid development of surveying and mapping equipment is beneficial to improve the convenience of point cloud and image acquisition, providing a new technical means for smart surveying and mapping. However, how to intelligently extract 3D spatial-temporal information from point cloud and image is still facing challenges. Surrounding the core of the smart surveying and mapping, this paper focuses on the key technologies and research progress of point cloud location accuracy improvement, point cloud and panoramic image fusion, component-level urban objects extraction, software development and practice.
Keywords	3d geospatial information extraction ; point cloud ; feature fusion ; smart surveying and mapping ; artificial intelligence ; Mathematical geography. Cartography ; GA1-1776
Language	Chinese
Publishing date	2022-07-01T00:00:00Z
Publisher	Surveying and Mapping Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Multi-level self-adaptive individual tree detection for coniferous forest using airborne LiDAR

Hui, Zhenyang / Cheng, Penggen / Yang, Bisheng / Zhou, Guoqing

International journal of applied earth observation and geoinformation. 2022 Nov., v. 114

2022

Abstract: To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. ...

Abstract	To obtain satisfying results of individual tree detection from LiDAR points, parameters using traditional methods usually need to be adjusted by trials and errors. When encountering complex forest environments, the detection accuracy cannot be satisfied. To resolve this, a multi-level self-adaptive individual tree detection method was presented in this paper. The proposed method can be seen as a hybrid model, which combined the strength of both raster-based and point-based methods. Raster-based strategy was first used for achieving initial trees detection results, while the point-based strategy was adopted for optimizing the clustered trees. In the proposed method, crown width scales were estimated automatically. Meanwhile, multi-scales segmented results were fused together to take advantage of segmented results of both larger and small scales. Six different coniferous forests were adopted for testing. Experimental result shows that this study achieved the lowest omission and commission errors comparing with other three classical approaches. Meanwhile, the average F1 score in this paper is 0.84, which is much highest out of other methods.
Keywords	coniferous forests ; lidar ; models ; spatial data ; trees
Language	English
Dates of publication	2022-11
Publishing place	Elsevier B.V.
Document type	Article
ISSN	1569-8432
DOI	10.1016/j.jag.2022.103028
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Progress and perspective of point cloud intelligence

YANG Bisheng / DONG Zhen

Acta Geodaetica et Cartographica Sinica, Vol 48, Iss 12, Pp 1575-

2019 Volume 1585

Abstract: With the rapid development of the reality capture, such as laser scanning and oblique photogrammetry, point cloud has become the third important data source following vector maps and imagery, and also plays an increasingly important role in scientific ... ...

Abstract	With the rapid development of the reality capture, such as laser scanning and oblique photogrammetry, point cloud has become the third important data source following vector maps and imagery, and also plays an increasingly important role in scientific research and engineering in the fields of earth science, spatial cognition, and smart city, and so on. However, how to acquire valid and accurate three-dimensional geospatial information from point clouds has become the scientific frontier and the urgent demand in the field of surveying and mapping as well as the geoscience applications. To address the challenges mentioned above, point cloud intelligence came into being. This paper summarizes the state-of-the art of point cloud intelligence in acquisition equipment, the intelligent processing, scientific research and the major engineering applications, focusing on its three important areas:the theoretical methods, the key techniques of intelligent processing and the major engineering applications. Finally, the promising development tendency of the point cloud intelligence is summarized.
Keywords	point cloud big data ; point cloud intelligence ; semantic labeling ; structured modelling ; deep learning ; ubiquitous point cloud ; Mathematical geography. Cartography ; GA1-1776
Language	Chinese
Publishing date	2019-12-01T00:00:00Z
Publisher	Surveying and Mapping Press
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Object-based 3D building change detection using point-level change indicators [Corrigendum: June 2023, v. 120, p. 103356]

Zhang, Luqi / Zhang, Zhihua / Zhang, Jiuyan / Qiao, Xin / Zhang, Zhenchao / Yang, Bisheng / Dong, Zhen

International Journal of Applied Earth Observation and Geoinformation. 2023 Apr., v. 118, p. 103293

2023 , Page(s) 103293

Abstract: With the rapid expansion of urban areas in both horizontal and vertical directions, the complicated building structural changes challenge the existing 3D change detection methods. The existing 3D change detection methods are mainly based on local ... ...

Abstract	With the rapid expansion of urban areas in both horizontal and vertical directions, the complicated building structural changes challenge the existing 3D change detection methods. The existing 3D change detection methods are mainly based on local differences and rely on setting thresholds and rules, and face difficulties when determining complex change types. In this paper, to solve these problems, we present a building object extraction method using change indicators and an object-based change type determination approach. The key steps are as follows: (1) point-level change indicators are generated using the local geometric differences between the point clouds from two epochs; (2) change indicators are used to guide the process of region growing and graph cuts for building object extraction; and (3) the object-based change types are determined by a random forest classifier, relying on the elaborate features of the building objects. Experiments were carried out on a simulated dataset and a real airborne laser scanning (ALS) dataset. The proposed method achieved the best performance on the simulated dataset, and the averageprecision and recall on the real ALS dataset reached 91.1% and 85.3% respectively, which demonstrates the effectiveness of the proposed method. This work enables the 3D updating of urban building maps and can be applied to building safety monitoring and identification of potential illegal structures.
Keywords	data collection ; geometry ; spatial data ; Change detection ; Building object extraction ; Graph cuts ; Change type determination
Language	English
Dates of publication	2023-04
Size	p. 103293
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Pre-press version ; Use and reproduction
ISSN	1569-8432
DOI	10.1016/j.jag.2023.103293
Database	NAL-Catalogue (AGRICOLA)

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Article: Autonomous Vehicle Localization with Prior Visual Point Cloud Map Constraints in GNSS-Challenged Environments

Lin, Xiaohu / Wang, Fuhong / Yang, Bisheng / Zhang, Wanwei

Remote Sensing. 2021 Jan. 31, v. 13, no. 3

2021

Abstract: Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to ... ...

Abstract	Accurate vehicle ego-localization is key for autonomous vehicles to complete high-level navigation tasks. The state-of-the-art localization methods adopt visual and light detection and ranging (LiDAR) simultaneous localization and mapping (SLAM) to estimate the position of the vehicle. However, both of them may suffer from error accumulation due to long-term running without loop optimization or prior constraints. Actually, the vehicle cannot always return to the revisited location, which will cause errors to accumulate in Global Navigation Satellite System (GNSS)-challenged environments. To solve this problem, we proposed a novel localization method with prior dense visual point cloud map constraints generated by a stereo camera. Firstly, the semi-global-block-matching (SGBM) algorithm is adopted to estimate the visual point cloud of each frame and stereo visual odometry is used to provide the initial position for the current visual point cloud. Secondly, multiple filtering and adaptive prior map segmentation are performed on the prior dense visual point cloud map for fast matching and localization. Then, the current visual point cloud is matched with the candidate sub-map by normal distribution transformation (NDT). Finally, the matching result is used to update pose prediction based on the last frame for accurate localization. Comprehensive experiments were undertaken to validate the proposed method, showing that the root mean square errors (RMSEs) of translation and rotation are less than 5.59 m and 0.08°, respectively.
Keywords	algorithms ; cameras ; data collection ; global positioning systems ; lidar ; normal distribution ; prediction
Language	English
Dates of publication	2021-0131
Publishing place	Multidisciplinary Digital Publishing Institute
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	2513863-7
ISSN	2072-4292
ISSN	2072-4292
DOI	10.3390/rs13030506
Database	NAL-Catalogue (AGRICOLA)

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Article: A point-based deep learning network for semantic segmentation of MLS point clouds

Han, Xu / Dong, Zhen / Yang, Bisheng

International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) ISPRS journal of photogrammetry and remote sensing. 2021 May, v. 175

2021

Abstract: Semantic segmentation of point cloud is critical to 3D scene understanding and also a challenging problem in point cloud processing. Although an increasing number of deep learning based methods are proposed in recent years for semantic segmentation of ... ...

Abstract	Semantic segmentation of point cloud is critical to 3D scene understanding and also a challenging problem in point cloud processing. Although an increasing number of deep learning based methods are proposed in recent years for semantic segmentation of point clouds, few deep learning networks can be directly used for large-scale outdoor point cloud segmentation which is essential for urban scene understanding. Given both the challenges of outdoor large-scale scenes and the properties of the 3D point clouds, this paper proposes an end-to-end network for semantic segmentation of urban scenes. Three key components are encompassed in the proposed point clouds deep learning network: (1) an efficient and effective sampling strategy for point cloud spatial downsampling; (2) a point-based feature abstraction module for effectively encoding the local features through spatial aggregating; (3) a loss function to address the imbalance of different categories, resulting in the overall performance improvement. To validate the proposed point clouds deep learning network, two datasets were used to check the effectiveness, showing the state-of-the-art performance in most of the testing data, which achieves mean IoU of 70.8% and 73.9% in Toronto-3D and Shanghai MLS dataset, respectively.
Keywords	data collection ; photogrammetry ; China
Language	English
Dates of publication	2021-05
Size	p. 199-214.
Publishing place	Elsevier B.V.
Document type	Article
Note	NAL-AP-2-clean
ZDB-ID	1007774-1
ISSN	0924-2716
ISSN	0924-2716
DOI	10.1016/j.isprsjprs.2021.03.001
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: A Strip Adjustment Method of UAV-Borne LiDAR Point Cloud Based on DEM Features for Mountainous Area.

Chen, Zequan / Li, Jianping / Yang, Bisheng

Sensors (Basel, Switzerland)

2021 Volume 21, Issue 8

Abstract: Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective ...

Abstract	Due to the trajectory error of the low-precision position and orientation system (POS) used in unmanned aerial laser scanning (ULS), discrepancies usually exist between adjacent LiDAR (Light Detection and Ranging) strips. Strip adjustment is an effective way to eliminate these discrepancies. However, it is difficult to apply existing strip adjustment methods in mountainous areas with few artificial objects. Thus, digital elevation model-iterative closest point (DEM-ICP), a pair-wise registration method that takes topography features into account, is proposed in this paper. First, DEM-ICP filters the point clouds to remove the non-ground points. Second, the ground points are interpolated to generate continuous DEMs. Finally, a point-to-plane ICP algorithm is performed to register the adjacent DEMs with the overlapping area. A graph-based optimization is utilized following DEM-ICP to estimate the correction parameters and achieve global consistency between all strips. Experiments were carried out using eight strips collected by ULS in mountainous areas to evaluate the proposed method. The average root-mean-square error (RMSE) of all data was less than 0.4 m after the proposed strip adjustment, which was only 0.015 m higher than the result of manual registration (ground truth). In addition, the plane fitting accuracy of lateral point clouds was improved 4.2-fold, from 1.565 to 0.375 m, demonstrating the robustness and accuracy of the proposed method.
Language	English
Publishing date	2021-04-15
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2052857-7
ISSN	1424-8220 ; 1424-8220
ISSN (online)	1424-8220
ISSN	1424-8220
DOI	10.3390/s21082782
Database	MEDical Literature Analysis and Retrieval System OnLINE

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