Rockfall Simulation Based on UAV Photogrammetry Data Obtained during an Emergency Declaration: Application at a Cultural Heritage Site

Unmanned aerial vehicle (UAV) photogrammetry is one of the most effective methods for capturing a terrain in smaller areas. Capturing a steep terrain is more complex than capturing a flat terrain. To fly a mission in steep rugged terrain, a ground control station with a terrain following mode is required, and a quality digital elevation model (DEM) of the terrain is needed. The methods and results of capturing such terrain were analyzed as part of the Belca rockfall surveys. In addition to the national digital terrain model (NDTM), two customized DEMs were developed to optimize the photogrammetric survey of the steep terrain with oblique images. Flight heights and slant distances between camera projection centers and terrain are analyzed in the article. Some issues were identified and discussed, namely the vertical images in steep slopes and the steady decrease of UAV heights above ground level (AGL) with the increase of height above take-off (ATO) at 6%-8% rate. To compensate for the latter issue, the custom DEMs and NDTM were tilted. Based on our experience, the proposed optimal method for capturing the steep terrain is a combination of vertical and oblique UAV images.

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“…Some authors do not mention any specifics of the UAV survey. UAV was used by [19][20][21] to survey landslides and rockfalls.…”

Section: Introductionmentioning

confidence: 99%

Optimization of UAV Flight Missions in Steep Terrain

2020

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“…These two basic products were required for the subsequent sight distance analysis. The application of the SfM-MVS process requires the insertion of a scale, and data orientation with respect to the north and the vertical, to enable the measurements of geometric features and orientations [32]. In this sense, control point coordinates (x, y, z) of surface features were determined from the mosaic of the latest orthophotographs and DTM available from the National Geographic Institute of Spain (IGN) in ArcGIS [33].…”

Section: Methodsmentioning

confidence: 99%

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

et al. 2019

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The safe and efficient operation of highways largely depends on the adequate provision of sight distance. Small unmanned aerial vehicles (UAVs) can be utilized to efficiently complete data acquisition very soon after identifying an issue when searching for potential highway safety risks. A double grid flight is proposed to obtain an adequate three-dimensional (3D) recreation of the road environment, ensuring an unbiased sight distance output. Then, a dense cloud point is derived through a Structure from Motion Multi-View Stereo process. The point cloud is classified to produce both a terrain model, characterized by its resolution, and a 3D-object model, characterized by the maximum edge length of the entities. The resulting road environment model is utilized to calculate sight distance in a geographic information system. The results enabled the detection of accident-prone locations caused by sight distance limitations. Moreover, the impact of the 3D modeling parameters on the results was assessed.

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“…Slope failures are dangerous mass movements that occur frequently in mountainous terrains, causing extensive damage to natural features, as well as to economic and social infrastructure [1]. They can also have direct, long-term, physical impacts on major infrastructure such as roads, bridges, and human habitations, with severe effects on local infrastructure development and land use management [2].…”

Section: Introductionmentioning

confidence: 99%

“…Slope failures are often dangerous, particularly those that occur along a road. Falling masses of soil and rock require a rapid response [1]. Since such mass movements often occur in remote mountainous areas, there can be considerable time pressure to delineate the areas affected so that any victims can be reached in good time and provided with humanitarian support [11].…”

Section: Introductionmentioning

confidence: 99%

“…Enhanced data production workflows allow emergency response teams to obtain the required images in a timely manner, in order to respond to emergency situations. The potential for UAV use in natural hazard and emergency situations is clearly growing, and the latest technological developments have been described in recent publications [1,4,14,15]. Compared to traditional mapping methods, the use of UAVs has the potential to acquire data with higher spatial and temporal resolutions, as well as offering flexible deployment capabilities [16].…”

Section: Introductionmentioning

confidence: 99%

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UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks

et al. 2019

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Slope failures occur when parts of a slope collapse abruptly under the influence of gravity, often triggered by a rainfall event or earthquake. The resulting slope failures often cause problems in mountainous or hilly regions, and the detection of slope failure is therefore an important topic for research. Most of the methods currently used for mapping and modelling slope failures rely on classification algorithms or feature extraction, but the spatial complexity of slope failures, the uncertainties inherent in expert knowledge, and problems in transferability, all combine to inhibit slope failure detection. In an attempt to overcome some of these problems we have analyzed the potential of deep learning convolutional neural networks (CNNs) for slope failure detection, in an area along a road section in the northern Himalayas, India. We used optical data from unmanned aerial vehicles (UAVs) over two separate study areas. Different CNN designs were used to produce eight different slope failure distribution maps, which were then compared with manually extracted slope failure polygons using different accuracy assessment metrics such as the precision, F-score, and mean intersection-over-union (mIOU). A slope failure inventory data set was produced for each of the study areas using a frequency-area distribution (FAD). The CNN approach that was found to perform best (precision accuracy assessment of almost 90% precision, F-score 85%, mIOU 74%) was one that used a window size of 64 × 64 pixels for the sample patches, and included slope data as an additional input layer. The additional information from the slope data helped to discriminate between slope failure areas and roads, which had similar spectral characteristics in the optical imagery. We concluded that the effectiveness of CNNs for slope failure detection was strongly dependent on their design (i.e., the window size selected for the sample patch, the data used, and the training strategies), but that CNNs are currently only designed by trial and error. While CNNs can be powerful tools, such trial and error strategies make it difficult to explain why a particular pooling or layer numbering works better than any other.

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Rockfall Simulation Based on UAV Photogrammetry Data Obtained during an Emergency Declaration: Application at a Cultural Heritage Site

Cited by 75 publications

References 49 publications

Optimization of UAV Flight Missions in Steep Terrain

Optimization of UAV Flight Missions in Steep Terrain

Using Small Unmanned Aerial Vehicle in 3D Modeling of Highways with Tree-Covered Roadsides to Estimate Sight Distance

UAV-Based Slope Failure Detection Using Deep-Learning Convolutional Neural Networks

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