Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives

Costabile, Pierfranco; Costanzo, Carmelina; Lorenzo, G. De; Santis, Rosa De; Penna, Nadia; Macchione, Francesco

doi:10.1016/j.envsoft.2020.104889

Cited by 42 publications

(26 citation statements)

References 87 publications

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“…Data collected by MLMS has been used for extracting a wide range of road features such as pavement surfaces, lane markings, road edges, traffic signs, and roadside objects. It also facilitated applications including cross-section extraction [27,28], pavement condition monitoring [17], sight distance assessment [20,21], vertical clearance evaluation [22,29], and flood modeling in urban areas [7,8]. When compared with airborne LiDAR, ground systems provide a higher horizontal accuracy owing to their smaller laser footprint size.…”

Section: Mobile Lidar For Transportation Applicationsmentioning

confidence: 99%

“…While some studies proposed participatory assessment methods (i.e., utilizing data collected by citizen scientists) [5,6], the high-quality topographic data acquired by remote-sensing techniques provides an alternative for automated condition evaluation of roadside ditches. In addition to roadway maintenance, high-quality remote sensing data is essential for the investigation of the hydrological effects of roadside ditches and ultimately benefits flood risk assessment [7,8]. Despite ditch networks being increasingly incorporated in distributed hydrologic modeling, the ability to accurately extract drainage networks from remote sensing data remains challenging [9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

et al. 2021

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Maintenance of roadside ditches is important to avoid localized flooding and premature failure of pavements. Scheduling effective preventative maintenance requires a reasonably detailed mapping of the ditch profile to identify areas in need of excavation to remove long-term sediment accumulation. This study utilizes high-resolution, high-quality point clouds collected by mobile LiDAR mapping systems (MLMS) for mapping roadside ditches and performing hydrological analyses. The performance of alternative MLMS units, including an unmanned aerial vehicle, an unmanned ground vehicle, a portable backpack system along with its vehicle-mounted version, a medium-grade wheel-based system, and a high-grade wheel-based system, is evaluated. Point clouds from all the MLMS units are in agreement within the ±3 cm range for solid surfaces and ±7 cm range for vegetated areas along the vertical direction. The portable backpack system that could be carried by a surveyor or mounted on a vehicle is found to be the most cost-effective method for mapping roadside ditches, followed by the medium-grade wheel-based system. Furthermore, a framework for ditch line characterization is proposed and tested using datasets acquired by the medium-grade wheel-based and vehicle-mounted portable systems over a state highway. An existing ground-filtering approach—cloth simulation—is modified to handle variations in point density of mobile LiDAR data. Hydrological analyses, including flow direction and flow accumulation, are applied to extract the drainage network from the digital terrain model (DTM). Cross-sectional/longitudinal profiles of the ditch are automatically extracted from the LiDAR data and visualized in 3D point clouds and 2D images. The slope derived from the LiDAR data turned out to be very close to the highway cross slope design standards of 2% on driving lanes, 4% on shoulders, and a 6-by-1 slope for ditch lines.

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Section: Mobile Lidar For Transportation Applicationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

et al. 2021

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“…For this purpose, various numerical models such as Finite Difference Methods (FDMs) and Finite Element Methods (FEMs) have been widely applied to solve governing equations (one of which is known as the Saint-Venante equation) on the flood events in 1-D, 2-D, and 3-D [5][6][7]. The accuracy level of both FDMs and FEMs is completely dependent on various factors such as the hydrodynamic conditions of the flood, the availability of boundary conditions for solving the governing equations, the availability of recorded information from gauged basins, and the motion of sediments [8][9][10][11]. Furthermore, the suitability of typical numerical schemes (explicit or implicit) and grids size for solving flood equations will affect the performance of FEMs and FDMs, respectively.…”

Section: Introductionmentioning

confidence: 99%

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

Farhadi

Najafzadeh

2021

Water

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Detecting effective parameters in flood occurrence is one of the most important issues that has drawn more attention in recent years. Remote Sensing (RS) and Geographical Information System (GIS) are two efficient ways to spatially predict Flood Risk Mapping (FRM). In this study, a web-based platform called the Google Earth Engine (GEE) (Google Company, Mountain View, CA, USA) was used to obtain flood risk indices for the Galikesh River basin, Northern Iran. With the aid of Landsat 8 satellite imagery and the Shuttle Radar Topography Mission (SRTM) Digital Elevation Model (DEM), 11 risk indices (Elevation (El), Slope (Sl), Slope Aspect (SA), Land Use (LU), Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Topographic Wetness Index (TWI), River Distance (RD), Waterway and River Density (WRD), Soil Texture (ST]), and Maximum One-Day Precipitation (M1DP)) were provided. In the next step, all of these indices were imported into ArcMap 10.8 (Esri, West Redlands, CA, USA) software for index normalization and to better visualize the graphical output. Afterward, an intelligent learning machine (Random Forest (RF)), which is a robust data mining technique, was used to compute the importance degree of each index and to obtain the flood hazard map. According to the results, the indices of WRD, RD, M1DP, and El accounted for about 68.27 percent of the total flood risk. Among these indices, the WRD index containing about 23.8 percent of the total risk has the greatest impact on floods. According to FRM mapping, about 21 and 18 percent of the total areas stood at the higher and highest risk areas, respectively.

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“…There are almost no point clouds on the walls or on the projection screen. Generally, a terrestrial 3D laser scanner or an electronic total station is used for the measurement or modeling of large-scale scenes with weak texture [7,8]. With the former, the measurement time is long, and the subsequent measurement data must be spliced.…”

Section: Introductionmentioning

confidence: 99%

A Practical 3D Reconstruction Method for Weak Texture Scenes

Yang

Jiang

2021

Remote Sensing

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In recent years, there has been a growing demand for 3D reconstructions of tunnel pits, underground pipe networks, and building interiors. For such scenarios, weak textures, repeated textures, or even no textures are common. To reconstruct these scenes, we propose covering the lighting sources with films of spark patterns to “add” textures to the scenes. We use a calibrated camera to take pictures from multiple views and then utilize structure from motion (SFM) and multi-view stereo (MVS) algorithms to carry out a high-precision 3D reconstruction. To improve the effectiveness of our reconstruction, we combine deep learning algorithms with traditional methods to extract and match feature points. Our experiments have verified the feasibility and efficiency of the proposed method.

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Terrestrial and airborne laser scanning and 2-D modelling for 3-D flood hazard maps in urban areas: new opportunities and perspectives

Cited by 42 publications

References 87 publications

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

Comparative Analysis of Different Mobile LiDAR Mapping Systems for Ditch Line Characterization

Flood Risk Mapping by Remote Sensing Data and Random Forest Technique

A Practical 3D Reconstruction Method for Weak Texture Scenes

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