EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

Asal, Fahmy F. F.

doi:10.5194/isprs-annals-iii-2-91-2016

Cited by 4 publications

(6 citation statements)

References 13 publications

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“…The MAD and SD for the UAV point cloud (0.11 m and 0.39 m, respectively) were significantly higher than for the LiDAR data (0.002 m and 0.03 m, respectively). Those results are in line with the findings presented in the work of [19] and with results obtained comparing both point clouds using the DoD method (previous section).…”

Section: Suitability Of the Proposed Methods To Produce Dem From Uav Asupporting

confidence: 92%

“…Reduction of point cloud density decreases the data acquisition and data processing costs, but can affect the accuracy of the generated model. Alas, the author of [19] reported a 50% reduction of LiDAR point density without big deteriorations in the visual and statistical characteristics of the generated DEMs. The results in [20] showed that data with 50% reduction provided compatible surface estimation, but significantly reduced half of the processing time and storage space.…”

Section: Dem Generation From Point Cloudsmentioning

confidence: 99%

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Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

et al. 2019

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Digital elevation model (DEM) has been frequently used for the reduction and management of flood risk. Various classification methods have been developed to extract DEM from point clouds. However, the accuracy and computational efficiency need to be improved. The objectives of this study were as follows: (1) to determine the suitability of a new method to produce DEM from unmanned aerial vehicle (UAV) and light detection and ranging (LiDAR) data, using a raw point cloud classification and ground point filtering based on deep learning and neural networks (NN); (2) to test the convenience of rebalancing datasets for point cloud classification; (3) to evaluate the effect of the land cover class on the algorithm performance and the elevation accuracy; and (4) to assess the usability of the LiDAR and UAV structure from motion (SfM) DEM in flood risk mapping. In this paper, a new method of raw point cloud classification and ground point filtering based on deep learning using NN is proposed and tested on LiDAR and UAV data. The NN was trained on approximately 6 million points from which local and global geometric features and intensity data were extracted. Pixel-by-pixel accuracy assessment and visual inspection confirmed that filtering point clouds based on deep learning using NN is an appropriate technique for ground classification and producing DEM, as for the test and validation areas, both ground and non-ground classes achieved high recall (>0.70) and high precision values (>0.85), which showed that the two classes were well handled by the model. The type of method used for balancing the original dataset did not have a significant influence in the algorithm accuracy, and it was suggested not to use any of them unless the distribution of the generated and real data set will remain the same. Furthermore, the comparisons between true data and LiDAR and a UAV structure from motion (UAV SfM) point clouds were analyzed, as well as the derived DEM. The root mean square error (RMSE) and the mean average error (MAE) of the DEM were 0.25 m and 0.05 m, respectively, for LiDAR data, and 0.59 m and -0.28 m, respectively, for UAV data. For all land cover classes, the UAV DEM overestimated the elevation, whereas the LIDAR DEM underestimated it. The accuracy was not significantly different in the LiDAR DEM for the different vegetation classes, while for the UAV DEM, the RMSE increased with the height of the vegetation class. The comparison of the inundation areas derived from true LiDAR and UAV data for different water levels showed that in all cases, the largest differences were obtained for the lowest water level tested, while they performed best for very high water levels. Overall, the approach presented in this work produced DEM from LiDAR and UAV data with the required accuracy for flood mapping according to European Flood Directive standards. Although LiDAR is the recommended technology for point cloud acquisition, a suitable alternative is also UAV SfM in hilly areas.

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Section: Suitability Of the Proposed Methods To Produce Dem From Uav Asupporting

confidence: 92%

Section: Dem Generation From Point Cloudsmentioning

confidence: 99%

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

et al. 2019

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“…All these increases are lower than those reported by Anderson et al [18] and Liu et al [19]. Nevertheless, Asal [2] reported an increase of RMSE of approximately 260% when the point cloud was reduced to 6%. For each density value studied, the RMSE values derived from the four interpolators were ordered from lowest to highest as follows: IDW, KR, TLI, and the worst values were found for RBF.…”

Section: Discussionmentioning

confidence: 55%

“…It is usually used for describing terrains, giving its elevations without considering the vegetation or man-made features. It plays an important role in applications related to terrain modeling, hydrological modeling, or landscape evolution due erosion process [1][2][3]. When a high scale is required, it is very important to count with a point cloud which yields a high resolution and high quality DEM.…”

Section: Introductionmentioning

confidence: 99%

“…For a large area, Singh et al [17] evaluated the effects of LiDAR point cloud density on the biomass estimation of remnant forest in a rapidly urbanizing region, concluding that data with an average point spacing of 0.70 to 1.50 m could derive in cost-effective acquisition and data processing. Asal [2] evaluated the effects of reduction in airborne LiDAR data on the visual and statistical characteristics of the created DEM and concluded that the DEM accuracy decreases only 4.83% when the point cloud was reduced 50%.…”

Section: Introductionmentioning

confidence: 99%

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Effects of Structure from Motion Data Density, Interpolation Method and Grid Size on Micro Topography Digital Terrain Model Accuracy

Agüera-Vega¹,

Agüera-Puntas²,

Mancini³

et al. 2019

Preprint

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The objective of this study is to evaluate the effects of the 3D point cloud density derived from unmanned aerial vehicle (UAV) photogrammetry and structure from motion (SfM) and multi-view stereopsis (MVS) techniques, the interpolation method for generating a digital terrain model (DTM), and the resolution (grid size) of the derived DTM on the accuracy of estimated heights in small areas, where a very accurate high spatial resolution is required. A UAV-photogrammetry project was carried out on a bare soil of 13 × 13 m with a rotatory wing UAV at 10 m flight altitude (equivalent ground sample distance = 0.4 cm). The 3D point cloud was derived, and five sample replications representing 1, 2, 3, 4, 5, 10, 15, 20, 30, 40, 50, 60, 70, 80 and 90% of the original cloud were extracted to analyze the effect of cloud density on DTM accuracy. For each of these samples, DTMs were derived using four different interpolation methods (Inverse Distance Weighted (IDW), Multiquadric Radial Basis Function (MRBF), Kriging (KR), and Triangulation with Linear Interpolation (TLI)) and 15 DTM grid size (GS) values (20, 15, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0.67, 0.5, and 0.4 cm). Then, 675 DTMs were analyzed. The results showed, for each interpolation method and each density, an optimal GS value (most of the cases equal to 1 cm) for which the Root Mean Square Error (RMSE) is minimum. IDW was the interpolator which yielded best accuracies for all combination of densities and GS. Its RMSE, considering the raw cloud, was 1.054 cm. The RMSE increased 3% when a point cloud with 80% extracted from the raw cloud was used to generate the DTM. When the point cloud included the 40% of the raw cloud, RMSE increased 5%. For densities lower than 15%, RMSE increased exponentially (45% for 1% of raw cloud). The grid size minimizing RMSE for densities of 20% or higher was 1 cm, which represents 2.5 times the ground sample distance of the pictures used for developing the photogrammetry project.

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Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

Chen

Yang

et al. 2023

ISPRS Journal of Photogrammetry and Remote Sensing

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EVALUATING THE EFFECTS OF REDUCTIONS IN LiDAR DATA ON THE VISUAL AND STATISTICAL CHARACTERISTICS OF THE CREATED DIGITAL ELEVATION MODELS

Cited by 4 publications

References 13 publications

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Accuracy Assessment of Deep Learning Based Classification of LiDAR and UAV Points Clouds for DTM Creation and Flood Risk Mapping

Effects of Structure from Motion Data Density, Interpolation Method and Grid Size on Micro Topography Digital Terrain Model Accuracy

Adaptive coarse-to-fine clustering and terrain feature-aware-based method for reducing LiDAR terrain point clouds

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