Development and Assessment of a Data Set Containing Frame Images and Dense Airborne Laser Scanning Point Clouds

Tommaselli, Antônio Maria Garcia; Galo, Maurício; Reis, Thiago Tiedtke dos; Ruy, Roberto Da Silva; Moraes, Marcus Vinícius Antunes de; Matricardi, Wander Vieira

doi:10.1109/lgrs.2017.2779559

Cited by 19 publications

(12 citation statements)

References 5 publications

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“…The selected study area shown in Figure 2 consists of a small portion of the urban region of Presidente Prudente/Brazil. The LiDAR dataset was acquired with a RIEGL LMS Q680i in 2014 by Sensormap Geotecnologia, a subsidiary company of the Engemap Group (Tommaselli et al, 2018). The point cloud has 1.65 million points and covers an area of 428 m by 351 m, resulting in a average point density of 10.9 pts/m 2 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The point cloud has 1.65 million points and covers an area of 428 m by 351 m, resulting in a average point density of 10.9 pts/m 2 . According to Tommaselli et al (2018), the average flying height was 550 m, and the point cloud has a 12 cm vertical root mean square error (RMSE), which was assessed using ground control points (GCPs) tracked with double frequency GNSS receivers. This point cloud has a considerable ground elevation difference of about 30 m. In addition, the region contains several objects that are easily mistaken as positive outliers, such as light poles, power transmission lines, and low density sampling on building walls.…”

Section: Experiments and Resultsmentioning

confidence: 99%

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Statistical Outlier Detection Method for Airborne Lidar Data

Carrilho

Galo

Santos

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Sampling the Earth’s surface using airborne LASER scanning (ALS) systems suffers from several factors inherent to the LASER system itself as well as external factors, such as the presence of particles in the atmosphere, and/or multi-path returns due to reflections. The resulting point cloud may therefore contain some outliers and removing them is an important (and difficult) step for all subsequent processes that use this kind of data as input. In the literature, there are several approaches for outlier removal, some of which require external information, such as spatial frequency characteristics or presume parametric mathematical models for surface fitting. A limitation on the height histogram filtering approach was identified from the literature review: outliers that lie within the ground elevation difference might not be detected. To overcome such a limitation, this paper proposes an adaptive alternative based on point cloud cell subdivision. Instead of computing a single histogram for the whole dataset, the method applies the filtering to smaller patches, in which the ground elevation difference can be ignored. A study area was filtered, and the results were compared quantitatively with two other methods implemented in both free and commercial software packages. The reference data was generated manually in order to provide useful quality measures. The experiment shows that none of the tested filters was able to reach a level of complete automation, therefore manual corrections by the operator are still necessary.

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Section: Experiments and Resultsmentioning

confidence: 99%

Section: Experiments and Resultsmentioning

confidence: 99%

Statistical Outlier Detection Method for Airborne Lidar Data

Carrilho

Galo

Santos

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…This section presents the experiments and results obtained using the proposed method. The experiments were performed considering a LiDAR dataset acquired over the city of Presidente Prudente/Brazil by the company Sensormap Geotecnologia, which belongs to the Engemap Group, as can be seen in Tommaselli et al (2018). The data set was acquired from a flight height of 900 m with an average density of 5 points/m 2 .…”

Section: Experiments and Resultsmentioning

confidence: 99%

Building Boundary Extraction From Lidar Data Using a Local Estimated Parameter for Alpha Shape Algorithm

Santos¹,

Galo²,

Carrilho³

2018

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The &alpha;-shape algorithm is a very common option to extract building boundaries from LiDAR data. This algorithm is normally executed in 2D space considering a parameter &alpha; as a binary classifier which controls the distinctiveness of points whether or not they belong to the object boundary. For point cloud data, this parameter is directly related to the local point density and the level of detail of building boundaries. Studies that have explored this concept usually consider a unique parameter &alpha; to extract all buildings in the dataset. However, the point density can have a considerable variation along the point cloud and, in this case, the use a global parameter may not be the best choice. Alternatively, this paper proposes a data-driven method that estimates a local parameter for each building. The method evaluation considered six test areas with different levels of complexity, selected from a LiDAR dataset acquired over the city of Presidente Prudente/Brazil. From the qualitative and quantitative analysis, it could be seen that the proposed method generated better results than when a global parameter is used. The proposed method was also able to withstand density variation among the LiDAR data, having a positional accuracy around 0.22m, against 0.40m of global parameter.

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“…The experiments were performed considering two LiDAR datasets. The first dataset is part of the Unesp Photogrammetric Data Set and was generated from three different flying heights over Presidente Prudente/Brazil (Tommaselli et al, 2018). This paper considers only the lowest flight.…”

Section: Lidar Datasetmentioning

confidence: 99%

Building Detection From Lidar Data Using Entropy and the K-Means Concept

Santos

Pessoa

Carrilho

et al. 2019

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Information obtained from LiDAR data processing is considered in a variety of applications, among them urban planning. In this context, buildings play a substantial role, since a high percentage of the urban landscape is occupied by them. In the literature, many methodologies have been developed aiming at the detection of building using remote sensing data. The approaches can be developed by applying different ideas: regularity of cluster boundary, plane fitting, radiometric data and also in geometric attribute derived from LiDAR. This paper proposes a method of building detection based on the use of the entropy concept and the K-means algorithm in which the training step is dispensed with. The experiments were performed considering two LiDAR datasets with different densities (12.5 pts/m 2 and 4 pts/m 2 ). Visual and qualitative analysis enabled verification of the potential of the proposed method, which presented satisfactory results for both datasets.* Corresponding author adjusting planes to the points of each cluster . In this case, it is assumed that the building is represented by a plane, or set of inclined planes, whereas vegetation is formed by several small planes with different orientations. Despite being robust to noise, this approach can present problems in identifying buildings composed of curved roofs.

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Development and Assessment of a Data Set Containing Frame Images and Dense Airborne Laser Scanning Point Clouds

Cited by 19 publications

References 5 publications

Statistical Outlier Detection Method for Airborne Lidar Data

Statistical Outlier Detection Method for Airborne Lidar Data

Building Boundary Extraction From Lidar Data Using a Local Estimated Parameter for Alpha Shape Algorithm

Building Detection From Lidar Data Using Entropy and the K-Means Concept

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