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  • Cartography and geographic base data  (2)
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  • Cartography and geographic base data  (2)
  • 1
    Online Resource
    Online Resource
    Augmented Multi-Component Recurrent Graph Convolutional Network for Traffic Flow Forecasting
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 2 ( 2022-01-26), p. 88-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 2 ( 2022-01-26), p. 88-
    Abstract: Due to the periodic and dynamic changes of traffic flow and the spatial–temporal coupling interaction of complex road networks, traffic flow forecasting is highly challenging and rarely yields satisfactory prediction results. In this paper, we propose a novel methodology named the Augmented Multi-component Recurrent Graph Convolutional Network (AM-RGCN) for traffic flow forecasting by addressing the problems above. We first introduce the augmented multi-component module to the traffic forecasting model to tackle the problem of periodic temporal shift emerging in traffic series. Then, we propose an encoder–decoder architecture for spatial–temporal prediction. Specifically, we propose the Temporal Correlation Learner (TCL) which incorporates one-dimensional convolution into LSTM to utilize the intrinsic temporal characteristics of traffic flow. Moreover, we combine TCL with the graph convolutional network to handle the spatial–temporal coupling interaction of the road network. Similarly, the decoder consists of TCL and convolutional neural networks to obtain high-dimensional representations from multi-step predictions based on spatial–temporal sequences. Extensive experiments on two real-world road traffic datasets, PEMSD4 and PEMSD8, demonstrate that our AM-RGCN achieves the best results.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11020088
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Accurate Reconstruction of the LoD3 Building Model by Integrating Multi-Source Point Clouds and Oblique Remote Sensing Imagery
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 3 ( 2019-03-08), p. 135-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 3 ( 2019-03-08), p. 135-
    Abstract: 3D urban building models, which provide 3D information services for urban planning, management and operational decision-making, are essential for constructing digital cities. Unfortunately, the existing reconstruction approaches for LoD3 building models are insufficient in model details and are associated with a heavy workload, and accordingly they could not satisfy urgent requirements of realistic applications. In this paper, we propose an accurate LoD3 building reconstruction method by integrating multi-source laser point clouds and oblique remote sensing imagery. By combing high-precision plane features extracted from point clouds and accurate boundary constraint features from oblique images, the building mainframe model, which provides an accurate reference for further editing, is quickly and automatically constructed. Experimental results show that the proposed reconstruction method outperforms existing manual and automatic reconstruction methods using both point clouds and oblique images in terms of reconstruction efficiency and spatial accuracy.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8030135
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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