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  • Zhang, Yun  (2)
  • Cartography and geographic base data  (2)
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  • Cartography and geographic base data  (2)
  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 2 ( 2020-02-01), p. 84-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 2 ( 2020-02-01), p. 84-
    Abstract: The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way of solving the existing copyright management problem or other problems in spatial data infrastructure for data sharing. An Ethereum smart contract is used in this model to realize spatial data digital rights management function. The InterPlanetary File System is utilized as external data storage for storing spatial data in the decentralized file system to avoid data destruction that is caused by a single point of failure. There is no central server in the model architecture, which has a completely decentralized nature and it makes spatial data rights management not dependent on third-party trust institutions. We designed three spatial data copyright management algorithms, developed a prototype system to implement and test the model, used the smart contract security verification tool to check code vulnerabilities, and, finally, discussed the usability, scalability, efficiency, performance, and security of the proposed model. The result indicates that the proposed model not only has diversified functions of copyright management compared with previous studies on the blockchain-based digital rights management, but it can also solve the existing problems in traditional spatial data infrastructure for data sharing due to its characteristics of complete decentralization, mass orientation, immediacy, and high security.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 11 ( 2023-11-18), p. 467-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 11 ( 2023-11-18), p. 467-
    Abstract: Lane-level road information is especially crucial now that high-precision navigation maps are in more demand. Road information may be obtained rapidly and affordably by mining floating vehicle data (FCD). A method is proposed to extract the number of lanes on urban roads by combining deep learning and low-frequency FCD. Initially, the FCD is cleaned using the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering technique. Then, the FCD is split into three categories based on the typical urban road types: one-way one-lane, one-way two-lane, and one-way three-lane, and the deep learning sample data is created using segmentation, rotation, and gridding. Lastly, the number of urban road lanes is obtained by training and predicting the sample data using the LeNet-5 model. The number of urban road lanes was effectively identified from the low-frequency FCD with a detection accuracy of 92.7% through the cleaning and classification of Wuhan FCD. Urban roads can be efficiently covered by the FCD on a regular basis, and lane information can be efficiently collected using deep learning techniques. This method can be used to generate and update lane number information for high-precision navigation maps.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
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