Detection and Classification of Changes in Buildings from Airborne Laser Scanning Data

Xu, Sudan; Vosselman, G.; Elberink, Sander Oude

doi:10.3390/rs71215867

Cited by 42 publications

(36 citation statements)

References 14 publications

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“…(4) Data gaps exist in both data types. The data gaps in laser points mainly occur due to occlusion or pulse absorption by the surface material (e.g., water [11]), while data gaps occur in dense matching points mainly due to poor contrast. (5) Point distribution on trees requires comparing the point clouds distributed on trees, where the laser points are distributed over the canopy, branches, and the ground below, while for dense matching, usually only points on the canopy are generated.…”

Section: Introductionmentioning

confidence: 99%

Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data

et al. 2019

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Detecting topographic changes in an urban environment and keeping city-level point clouds up-to-date are important tasks for urban planning and monitoring. In practice, remote sensing data are often available only in different modalities for two epochs. Change detection between airborne laser scanning data and photogrammetric data is challenging due to the multi-modality of the input data and dense matching errors. This paper proposes a method to detect building changes between multimodal acquisitions. The multimodal inputs are converted and fed into a light-weighted pseudo-Siamese convolutional neural network (PSI-CNN) for change detection. Different network configurations and fusion strategies are compared. Our experiments on a large urban data set demonstrate the effectiveness of the proposed method. Our change map achieves a recall rate of 86.17%, a precision rate of 68.16%, and an F1-score of 76.13%. The comparison between Siamese architecture and feed-forward architecture brings many interesting findings and suggestions to the design of networks for multimodal data processing.

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Section: Introductionmentioning

confidence: 99%

Detecting Building Changes between Airborne Laser Scanning and Photogrammetric Data

et al. 2019

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“…The lack of building wall on ALS obstacle model results directly from the geometry of the air scanner measurement. As a result of such location of the scanner in the resulting cloud there is a high density of points on the roofs and relatively small density on the walls [9]. The number of wall points depends on the direction and speed of the flight, as well as the scanning density.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

Application of ALS data for GNSS terrain obstacles inventory

2018

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Current constellation of global navigation satellite system (GNSS) ensures signal availability even in severe observational conditions like urban canyon or under tree canopy. However, positioning in such environment remains a challenge because obstacles can block, reflect and diffract GNSS signals which significantly affects accuracy. Those errors are strongly sight dependent and cannot be mitigated in differential positioning that is why, knowledge of the shape and spatial distribution of terrain obstacles is essential. In this paper using of airborne laser scanning (ALS) data for terrain obstacles inventory is presented and evaluated. In proposed method terrain obstacle models have been derived from ASCII ALS data file using open source QGIS with LAStools software suite and dedicated ALSObstModel plugin. Test models were developed for three geodetic control points with different environmental characteristics. For each point reference model from direct tachometry measurements have been obtained. An average error in determining the elevation of the terrain obstacles from ALS based models was 0.6° to 1.7°. This distance corresponds to 3 to 6 minutes of satellite in orbit.

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“…Vosselman et al (2004) classify ALS data as bare-earth, building and vegetation, and then compare with a topographical database for map updating. Xu et al (2013) detect and classify changes in buildings after classification of ALS data into urban objects.…”

Section: Change Detection From Remote Sensing and Airborne Lidar Datamentioning

confidence: 99%

“…Airborne laser scanning (ALS) data is also used for similar applications with high geometric precision due to accurate 3D acquisition (Xu et al, 2013;Hebel et al, 2013;Yu et al, 2004). In recent years, 3D maps and virtual city models have been under fast development, therefore many studies have focused on street environment monitoring and city model updating (Früh and Zakhor, 2004;Kang et al, 2013).…”

Section: Introductionmentioning

confidence: 99%