Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China

He, Hongbo; Ye, Huping; Xu, Chenchen; Liu, Xiaohan

doi:10.3390/ijgi11080419

Cited by 4 publications

(4 citation statements)

References 47 publications

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“…The point cloud refined by the octree algorithm was quantified by point-to-point, point-to-surface, and body-to-body change errors to enable quantitative analysis of 3D digital model change detection of UAV at the crater rim of a highland crater ring. The main methods include: (1) C2C [21]; (2) Distance between the point cloud and the reference 3D grid or theoretical model which is used to obtain the surface displacement (cloud-to-mesh distance or cloud-to-model distance, C2M) [29], and (3) M3C2 [30]. To be specific, C2C calculates the distance difference of point cloud by calculating the distance between the point in P1 point cloud and the nearest point in P2 point cloud, as shown in Figure 6a.…”

Section: Uav Technical Framework For Quantification Of Repeated Obser...mentioning

confidence: 99%

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Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

Luo

Gan

Yuan

et al. 2023

Drones

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As UAV technology has been leaping forward, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring high-resolution images, which show bright prospects in a wide variety of terrains and different fields. First, the crater rim landscape of the Dinosaur Valley ring formation located on the central Yunnan Plateau served as the object of the surface change detection experiment, and two repetitive UAV ground observations of the study area were performed at the same altitude of 180 m with DJI Phantom 4 RTK in the rainy season (P1) and the dry season (P2). Subsequently, the UAV-SfM digital three-dimensional (3D) modeling method was adopted to build digital models of the study area at two points in time, which comprised the Digital Surface Model (DSM), Digital Orthomosaic Model (DOM), and Dense Image Matching (DIM) point cloud. Lastly, a quantitative analysis of the surface changes at the pit edge was performed using the point-surface-body surface morphological characterization method based on the digital model. As indicated by the results, (1) the elevation detection of the corresponding check points of the two DSM periods yielded a maximum positive difference of 0.2650 m and a maximum negative value of −0.2279 m in the first period, as well as a maximum positive difference of 0.2470 m and a maximum negative value of −0.2589 m in the second period. (2) In the change detection of the two DOM periods, the vegetation was 9.99% higher in the wet season than in the dry season in terms of coverage, whereas the bare soil was 10.54% more covered than the wet season. (3) In general, the M3C2-PM distances of the P1 point cloud and the P2 point cloud were concentrated in the interval (−0.2,0.2), whereas the percentage of the interval (−0.1,0) accounted for 26.69% of all intervals. The numerical model of UAV-SfM was employed for comprehensive change detection analysis. As revealed by the result of the point elevation difference in the constant area, the technique can conform to the requirements of earth observation with certain accuracy. The change area suggested that the test area can be affected by natural conditions to a certain extent, such that the multi-source data can be integrated to conduct more comprehensive detection analysis.

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Section: Uav Technical Framework For Quantification Of Repeated Obser...mentioning

confidence: 99%

“…As unmanned aerial vehicle (UAV) technology has been advancing rapidly, small consumer-grade UAVs equipped with optical sensors are capable of easily acquiring highresolution images, which are promising in different fields (e.g., geology [1], physical geography [2], and topographic and geomorphological [3] analysis).…”

Section: Introductionmentioning

confidence: 99%

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

Luo

Gan

Yuan

et al. 2023

Drones

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“…This segment is typically constrained by issues such as traffic congestion, delivery delays, and high labor costs [2], urgently requiring an innovative solution. As an important branch of intelligent logistics, UAV logistics utilizes its advantages of flexibility, efficiency, safety, and convenience to solve complex terrain and end transportation problems [3]. However, cargo UAVs face various complex situations during operation, such as urban environment, geographic climate, and extreme weather conditions, which are very likely to induce various types of failures, seriously threatening the safety of cargo distribution.…”

Section: Introductionmentioning

confidence: 99%

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

Xiao,

Yan,

Zhang

et al. 2024

Auton. Intell. Syst.

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The fault diagnosis of cargo UAVs (Unmanned Aerial Vehicles) is crucial to ensure the safety of logistics distribution. In the context of smart logistics, the new trend of utilizing knowledge graph (KG) for fault diagnosis is gradually emerging, bringing new opportunities to improve the efficiency and accuracy of fault diagnosis in the era of Industry 4.0. The operating environment of cargo UAVs is complex, and their faults are typically closely related to it. However, the available data only considers faults and maintenance data, making it difficult to diagnose faults accurately. Moreover, the existing KG suffers from the problem of confusing entity boundaries during the extraction process, which leads to lower extraction efficiency. Therefore, a fault diagnosis knowledge graph (FDKG) for cargo UAVs constructed based on multi-domain fusion and incorporating an attention mechanism is proposed. Firstly, the multi-domain ontology modeling is realized based on the multi-domain fault diagnosis concept analysis expression model and multi-dimensional similarity calculation method for cargo UAVs. Secondly, a multi-head attention mechanism is added to the BERT-BILSTM-CRF network model for entity extraction, relationship extraction is performed through ERNIE, and the extracted triples are stored in the Neo4j graph database. Finally, the DJI cargo UAV failure is taken as an example for validation, and the results show that the new model based on multi-domain fusion data is better than the traditional model, and the precision rate, recall rate, and F1 value can reach 87.52%, 90.47%, and 88.97%, respectively.

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“…At present, the visualization of the air route is mainly based on the 2D or 3D GIS platform developed by the general graphics library. For example, He et al [12] used ArcGIS software to visualize the spatial distribution of the UAV logistics air route network in the 2D map. For users to understand the 2D visual expression, a strong spatial abstraction ability is often required, which makes it unsuitable for non-professional users.…”

Section: Introductionmentioning

confidence: 99%

SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

Tan,

Ye,

et al. 2023

Drones

Self Cite

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Augmented Reality (AR) technology visualizes virtual objects in the real environment, offering users an immersive experience that enhances their spatial perception of virtual objects. This makes AR an important tool for visualization in engineering, education, and gaming. The Unmanned Aerial Vehicles’ (UAVs’) low-altitude public air route (Skyroad) is a forward-looking virtual transportation infrastructure flying over complex terrain, presenting challenges for user perception due to its invisibility. In order to achieve a 3D and intuitive visualization of Skyroad, this paper proposes an AR visualization framework based on a physical sandbox. The framework consists of four processes: reconstructing and 3D-printing a sandbox model, producing virtual scenes for UAVs Skyroad, implementing a markerless registration and tracking method, and displaying Skyroad scenes on the sandbox with GPU-based occlusion handling. With the support of the framework, a mobile application called SkyroadAR was developed. System performance tests and user questionnaires were conducted on SkyroadAR; the results showed that our approachs to tracking and occlusion provided an efficient and stable AR effect for Skyroad. This intuitive visualization is recognized by both professional and non-professional users.

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Exploring the Spatial Heterogeneity and Driving Factors of UAV Logistics Network: Case Study of Hangzhou, China

Cited by 4 publications

References 47 publications

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

Repeated UAV Observations and Digital Modeling for Surface Change Detection in Ring Structure Crater Margin in Plateau

Multi-domain fusion for cargo UAV fault diagnosis knowledge graph construction

SkyroadAR: An Augmented Reality System for UAVs Low-Altitude Public Air Route Visualization

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