An improved minimum bounding rectangle algorithm for regularized building boundary extraction from aerial LiDAR point clouds with partial occlusions

Feng, Maolin; Zhang, Tonggang; Li, Shichao; Jin, Guoqing; Xia, Yanjun

doi:10.1080/01431161.2019.1641245

Cited by 22 publications

(6 citation statements)

References 12 publications

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“…In the case of no façade line, the gap detection would not be performed correctly. An alternative would be to implement a convex hull (Feng et al, 2020;Wang et al, 2017) on the ground binary image, to delimit the study area. However, urban ground is usually delimited by buildings.…”

Section: Discussionmentioning

confidence: 99%

“…In order to obtain a complete model, the point cloud of facades can be combined and completed with pre-generated models called SmartBoxes (Nan et al, 2010). In (Feng et al, 2020), the shape of the buildings is completed on the basis of the topological relationship with superimposed trees. For applications, it is important to classify such interpolated data as it has no properties intensity, and no free line-of-sight between points and the location of the laser emitter.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Balado

González

Verbree

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Occlusions accompany serious problems that reduce the applicability of numerous algorithms. The aim of this work is to detect and characterize urban ground gaps based on occluding object. The point clouds for input have been acquired with Mobile Laser Scanning and have been previously segmented into ground, buildings and objects, which have been classified. The method generates various raster images according to segmented point cloud elements, and detects gaps within the ground based on their connectivity and the application of the hit-or-miss transform. The method has been tested in four real case studies in the cities of Vigo and Paris, and an accuracy of 99.6% has been obtained in occlusion detection and labelling. Cars caused 80.6% of the occlusions. Each car occluded an average ground area of 11.9 m2. The proposed method facilitates knowing the percentage of occluded ground, and if this would be reduced in successive multi-temporal acquisitions based on mobility characteristics of each object class.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Balado

González

Verbree

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…To relieve these problems, refs. [17][18][19][20][21][22][23][24] combined optical imagery with GIS data, digital surface models (DSMs) obtained from light detection and ranging (lidar), or synthetic aperture radar interferometry to distinguish non-buildings that are highly similar to buildings, increasing the robustness of building extraction, are used. However, obtaining a wide range of corresponding multisource data is always costly.…”

Section: Related Workmentioning

confidence: 99%

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Liao

Han

et al. 2021

Remote Sensing

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Most of the existing approaches to the extraction of buildings from high-resolution orthoimages consider the problem as semantic segmentation, which extracts a pixel-wise mask for buildings and trains end-to-end with manually labeled building maps. However, as buildings are highly structured, such a strategy suffers several problems, such as blurred boundaries and the adhesion to close objects. To alleviate the above problems, we proposed a new strategy that also considers the contours of the buildings. Both the contours and structures of the buildings are jointly learned in the same network. The contours are learnable because the boundary of the mask labels of buildings implicitly represents the contours of buildings. We utilized the building contour information embedded in the labels to optimize the representation of building boundaries, then combined the contour information with multi-scale semantic features to enhance the robustness to image spatial resolution. The experimental results showed that the proposed method achieved 91.64%, 81.34%, and 74.51% intersection over union (IoU) on the WHU, Aerial, and Massachusetts building datasets, and outperformed the state-of-the-art (SOTA) methods. It significantly improved the accuracy of building boundaries, especially for the edges of adjacent buildings. The code is made publicly available.

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“…The SSI model will be used as the spatial index identifier in this paper. Remote sensing data slices will no longer be identified by matching the minimum bounding rectangle (MBR) with the grid [18]. The MBR will cause the spatial index of the remote sensing data to overlap and the position to be misaligned.…”

Section: Spatial Segmentation Indexing Modelmentioning

confidence: 99%

Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

et al. 2020

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The arrival of the era of big data for Earth observation (EO) indicates that traditional data management models have been unable to meet the needs of remote sensing data in big data environments. With the launch of the first remote sensing satellite, the volume of remote sensing data has also been increasing, and traditional data storage methods have been unable to ensure the efficient management of large amounts of remote sensing data. Therefore, a professional remote sensing big data integration method is sorely needed. In recent years, the emergence of some new technical methods has provided effective solutions for multi-source remote sensing data integration. This paper proposes a multi-source remote sensing data integration framework based on a distributed management model. In this framework, the multi-source remote sensing data are partitioned by the proposed spatial segmentation indexing (SSI) model through spatial grid segmentation. The designed complete information description system, based on International Organization for Standardization (ISO) 19115, can explain multi-source remote sensing data in detail. Then, the distributed storage method of data based on MongoDB is used to store multi-source remote sensing data. The distributed storage method is physically based on the sharding mechanism of the MongoDB database, and it can provide advantages for the security and performance of the preservation of remote sensing data. Finally, several experiments have been designed to test the performance of this framework in integrating multi-source remote sensing data. The results show that the storage and retrieval performance of the distributed remote sensing data integration framework proposed in this paper is superior. At the same time, the grid level of the SSI model proposed in this paper also has an important impact on the storage efficiency of remote sensing data. Therefore, the remote storage data integration framework, based on distributed storage, can provide new technical support and development prospects for big EO data.

show abstract

An improved minimum bounding rectangle algorithm for regularized building boundary extraction from aerial LiDAR point clouds with partial occlusions

Cited by 22 publications

References 12 publications

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Automatic Detection and Characterization of Ground Occlusions in Urban Point Clouds From Mobile Laser Scanning Data

Joint Learning of Contour and Structure for Boundary-Preserved Building Extraction

Big Earth Observation Data Integration in Remote Sensing Based on a Distributed Spatial Framework

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