Laser and Photogrammetric Modeling of Roads Surface Damages

Kuduev, Altynbek; kyzy, Zhypargul Abdykalyk; Shumilov, B. M.

doi:10.1007/978-3-030-80946-1_28

Cited by 1 publication

(4 citation statements)

References 6 publications

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“…(2) Methods targeting road surface vertical dimensions. This category includes methodologies focused on road surface materials, damage conditions, width, and municipal infrastructure information, and professional equipment used includes field measuring devices, LiDAR, and stereovision cameras, along with customized IoT sensor devices [5][6][7][8]10,33,34]. Kuduev et al [10] and Bhatt et al [6] utilized LiDAR to collect point cloud data for the road surface, and detected road surface damages based on the threshold value of the distance difference between the local fitted plane and the point cloud surface.…”

Section: Related Workmentioning

confidence: 99%

“…This category includes methodologies focused on road surface materials, damage conditions, width, and municipal infrastructure information, and professional equipment used includes field measuring devices, LiDAR, and stereovision cameras, along with customized IoT sensor devices [5][6][7][8]10,33,34]. Kuduev et al [10] and Bhatt et al [6] utilized LiDAR to collect point cloud data for the road surface, and detected road surface damages based on the threshold value of the distance difference between the local fitted plane and the point cloud surface. Tan et al [8] collected the road surface information from UAV oblique images, generated the point cloud of road surface based on the photogrammetry theory, and computed the road surface condition using the constructed 3D road surface model.…”

Section: Related Workmentioning

confidence: 99%

“…(1) First, decide which information to discard from the cell state based on the forget gate. It makes this decision based on the current input x t and previous output h t−1 , as denoted by Equation (10), where σ is the sigmoid function and W f and b f are the weights and biases of the forget gate.…”

Section: ) Bfrs Detection From Trajectories Based On the Lstm Neural ...mentioning

confidence: 99%

“…Therefore, sensing the surface condition to gather information like BFRSs is essential for urban transportation [8,9]. Traditional methods of road surface sensing rely on professional equipment and personnel, such as LiDAR, photogrammetry, and remote sensing [8,[10][11][12][13]. These methods are time-consuming and labor-intensive, making it challenging to keep up with the rapidly changing urban road conditions.…”

Section: Introductionmentioning

confidence: 99%

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Urban Road Surface Condition Sensing from Crowd-Sourced Trajectories Based on the Detecting and Clustering Framework

Lyu,

Zhong,

Huang

et al. 2024

Sensors

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Roads play a crucial role in urban transportation by facilitating the movement of materials within a city. The condition of road surfaces, such as damage and road facilities, directly affects traffic flow and influences decisions related to urban transportation maintenance and planning. To gather this information, we propose the Detecting and Clustering Framework for sensing road surface conditions based on crowd-sourced trajectories, utilizing various sensors (GPS, orientation sensors, and accelerometers) found in smartphones. Initially, smartphones are placed randomly during users’ travels on the road to record the road surface conditions. Then, spatial transformations are applied to the accelerometer data based on attitude readings, and heading angles are computed to store movement information. Next, the feature encoding process operates on spatially adjusted accelerations using the wavelet scattering transformation. The resulting encoding results are then input into the designed LSTM neural network to extract bump features of the road surface (BFRSs). Finally, the BFRSs are represented and integrated using the proposed two-stage clustering method, considering distances and directions. Additionally, this procedure is also applied to crowd-sourced trajectories, and the road surface condition is computed and visualized on a map. Moreover, this method can provide valuable insights for urban road maintenance and planning, with significant practical applications.

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