Extracting Topographic Data from Online Sources to Generate a Digital Elevation Model for Highway Preliminary Geometric Design

Chen, Sikai; Tang, Zongxin; Zhou, Hao; Cheng, Jianding

doi:10.1061/jtepbs.0000212

Cited by 7 publications

(4 citation statements)

References 36 publications

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“…Further, the quality of the embedding could be refined by adding other features extracted from imagery, as identified in previous research. These features include elevation data (Chen, Tang, Zhou, & Cheng, 2019) and lane number or width changes (Bar Hillel, Lerner, Levi, & Raz, 2014), which could influence behavior while approaching the intersection. Further, sight distance could be computed using Geographic Information Systems, while accounting for obstacles like trees or buildings (Castro, Iglesias, Sánchez, & Ambrosio, 2011).…”

Section: Discussionmentioning

confidence: 99%

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

Wijnands

Zhao

Nice

et al. 2020

Computer aided Civil Eng

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The World Health Organization has listed the design of safer intersections as a key intervention to reduce global road trauma. This article presents the first study to systematically analyze the design of all intersections in a large country, based on aerial imagery and deep learning. Approximately 900,000 satellite images were downloaded for all intersections in Australia and customized computer vision techniques emphasized the road infrastructure. A deep autoencoder extracted high-level features, including the intersection's type, size, shape, lane markings, and complexity, which were used to cluster similar designs. An Australian telematics data set linked infrastructure design to driving behaviors captured during 66 million kilometers of driving. This showed more frequent hard acceleration events (per vehicle) at four-than three-way intersections, relatively low hard deceleration frequencies at T-intersections, and consistently low average speeds on roundabouts. Overall, domain-specific feature extraction enabled the identification of infrastructure improvements that could result in safer driving behaviors, potentially reducing road trauma. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Discussionmentioning

confidence: 99%

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

Wijnands

Zhao

Nice

et al. 2020

Computer aided Civil Eng

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“…These methods capture elevation data points, building a representation of the Earth's surface (Teng et al, 2015). Mathematical interpolation and modeling techniques then transform these points into a continuous surface, depicting elevations across the landscape (Sanders, 2007;Chen et al, 2019;Chen et al, 2022). Flood-extent maps rely heavily on rainfall data, land use, soil types, and terrain data (including DEMs) simulating rainfall-runoff processes (Egbuchua, 2014;Innocent and Steve, 2015;Teng et al, 2015).…”

Section: Mapping Flood-prone Zones and Exposure Ratementioning

confidence: 99%

Integrated assessment of flood susceptibility and exposure rate in the lower Niger Basin, Onitsha, Southeastern Nigeria

Chinedu,

Ezebube,

Uchegbu

et al. 2024

Front. Earth Sci.

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BackgroundVarious methods have been utilized to investigate and mitigate flood occurrences, yet there is a paucity of literature on factors, such as soil compositions, that contribute to persistent flooding in river basins like the Lower Niger catchment, specifically at Onitsha. Furthermore, the study seeks to furnish essential geospatial data concerning flood vulnerability, flood risks, and exposure rates in the Lower Niger Catchment area, situated in Onitsha, southeastern Nigeria.Materials and methodsSoil samples were collected from 10 specific locations identified through GPS and ground-truthing techniques. Additionally, satellite imagery from the Landsat Enhanced Thematic Mapper (ETM +) was utilized, with supervised classification employed to extract feature classes. Analysis operations were conducted using IDRISI software, resulting in the creation of digital elevation models (DEMs), susceptibility maps, and flood-risk zones.ResultsAnalysis revealed that the predominant soil composition in the study area comprises sandy (84.8%), silt (8.1%), and clayey (7.1%) soils. Utilizing these soil characteristics alongside relevant aerial data, exposure rates were determined at various scales to delineate the most flood-vulnerable zones in the basin. It was found that certain areas, accommodating a population exceeding 79,426 across 2,926.2 ha, were particularly susceptible to flooding. Notably, major markets such as Bridgehead, Textile, and Biafra were identified as highly susceptible, with varying degrees of risk. The prevalence of sandy soil, which facilitates increased rainwater infiltration but is also prone to rapid saturation and runoff, likely contributes to the heightened susceptibility to flooding in these areas.ConclusionGeospatial analysis employing remote sensing data indicates the high susceptibility and exposure to flooding in the lower Niger River Basin around Onitsha. Urgent mitigation efforts are imperative, necessitating the establishment of zoned areas equipped with effective drainage systems to safeguard vulnerable populations.

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“…Another DEM map that can be obtained using software is by using Autodesk Infraworks [30] by using the Model Builder. It provides publicly available data sources and creates a 3D model for a few minutes, which provides not only terrain and imagery data but also road, building, and water data for an area of up to 200 square kilometers.…”

Section: Dem Model From Online Datamentioning

confidence: 99%

Comparison of UAV Drone and Online Terrain Model for Railway Route Planning

Adi

Aghastya

Imron

et al. 2022

jrtt

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This research tried to compare Digital Elevation Model (DEM) results from UAV Drone Survey with online DEM with a study case a railway route planning from Semarang to Demak in Central Jawa. The terrain model was resulted by Agisoft Metashape software and was compared to DEM from GoogleEarth, BING, SRTM, ASTERGDEM and DEMNAS by using Global Mapper sofware. The comparison was conducted by generating contour drawing for each model, along with graphical and statistical analysis. The results shown that the DEM Model from UAV Drone Survey result can meet the standard accurcy needed. The result of comparison from other the online DEM resulted that DEMNAS and ASTER GDEM have the stronges correlation among other DEM resources. The finding suggested for a carefull consideration to use open source DEM Data for perelyminary design odr Detail Engineering Design, especially for railway infrastructure project.

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Extracting Topographic Data from Online Sources to Generate a Digital Elevation Model for Highway Preliminary Geometric Design

Cited by 7 publications

References 36 publications

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

Identifying safe intersection design through unsupervised feature extraction from satellite imagery

Integrated assessment of flood susceptibility and exposure rate in the lower Niger Basin, Onitsha, Southeastern Nigeria

Comparison of UAV Drone and Online Terrain Model for Railway Route Planning

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