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  • Cartography and geographic base data  (66)
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  • Cartography and geographic base data  (66)
  • 1
    Online Resource
    Online Resource
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 10 ( 2017-10-18), p. 310-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 10 ( 2017-10-18), p. 310-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 2
    Online Resource
    Online Resource
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 9 ( 2018-09-09), p. 374-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 9 ( 2018-09-09), p. 374-
    Abstract: A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 11 ( 2020-10-25), p. 625-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 11 ( 2020-10-25), p. 625-
    Abstract: Recently, increasing amounts of multi-source geospatial data (raster data of satellites and textual data of meteorological stations) have been generated, which can play a cooperative and important role in many research works. Efficiently storing, organizing and managing these data is essential for their subsequent application. HBase, as a distributed storage database, is increasingly popular for the storage of unstructured data. The design of the row key of HBase is crucial to improving its efficiency, but large numbers of researchers in the geospatial area do not conduct much research on this topic. According the HBase Official Reference Guide, row keys should be kept as short as is reasonable while remaining useful for the required data access. In this paper, we propose a new row key encoding method instead of conventional stereotypes. We adopted an existing hierarchical spatio-temporal grid framework as the row key of the HBase to manage these geospatial data, with the difference that we utilized the obscure but short American Standard Code for Information Interchange (ASCII) to achieve the structure of the grid rather than the original grid code, which can be easily understood by humans but is very long. In order to demonstrate the advantage of the proposed method, we stored the daily meteorological data of 831 meteorological stations in China from 1985 to 2019 in HBase; the experimental result showed that the proposed method can not only maintain an equivalent query speed but can shorten the row key and save storage resources by 20.69% compared with the original grid codes. Meanwhile, we also utilized GF-1 imagery to test whether these improved row keys could support the storage and querying of raster data. We downloaded and stored a part of the GF-1 imagery in Henan province, China from 2017 to 2018; the total data volume reached about 500 GB. Then, we succeeded in calculating the daily normalized difference vegetation index (NDVI) value in Henan province from 2017 to 2018 within 54 min. Therefore, the experiment demonstrated that the improved row keys can also be applied to store raster data when using HBase.
    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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  • 4
    Online Resource
    Online Resource
    Informa UK Limited ; 2020
    In:  Journal of Maps Vol. 16, No. 2 ( 2020-12-09), p. 396-404
    In: Journal of Maps, Informa UK Limited, Vol. 16, No. 2 ( 2020-12-09), p. 396-404
    Type of Medium: Online Resource
    ISSN: 1744-5647
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2176732-4
    SSG: 14,1
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  • 5
    Online Resource
    Online Resource
    Informa UK Limited ; 2020
    In:  Journal of Maps Vol. 16, No. 1 ( 2020-01-01), p. 6-12
    In: Journal of Maps, Informa UK Limited, Vol. 16, No. 1 ( 2020-01-01), p. 6-12
    Type of Medium: Online Resource
    ISSN: 1744-5647
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2020
    detail.hit.zdb_id: 2176732-4
    SSG: 14,1
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  • 6
    Online Resource
    Online Resource
    MDPI AG ; 2015
    In:  ISPRS International Journal of Geo-Information Vol. 4, No. 4 ( 2015-12-08), p. 2742-2768
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 4, No. 4 ( 2015-12-08), p. 2742-2768
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2015
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    Informa UK Limited ; 2018
    In:  Journal of Spatial Science Vol. 63, No. 2 ( 2018-07-03), p. 399-417
    In: Journal of Spatial Science, Informa UK Limited, Vol. 63, No. 2 ( 2018-07-03), p. 399-417
    Type of Medium: Online Resource
    ISSN: 1449-8596 , 1836-5655
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2216491-1
    SSG: 14,1
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  • 8
    Online Resource
    Online Resource
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 12 ( 2021-11-29), p. 798-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 12 ( 2021-11-29), p. 798-
    Abstract: A high-fidelity 3D urban building model requires large quantities of detailed textures, which can be non-tiled or tiled ones. The fast loading and rendering of these models remain challenges in web-based large-scale 3D city visualization. The traditional texture atlas methods compress all the textures of a model into one atlas, which needs extra blank space, and the size of the atlas is uncontrollable. This paper introduces a size-adaptive texture atlas method that can pack all the textures of a model without losing accuracy and increasing extra storage space. Our method includes two major steps: texture atlas generation and texture atlas remapping. First, all the textures of a model are classified into non-tiled and tiled ones. The maximum supported size of the texture is acquired from the graphics hardware card, and all the textures are packed into one or more atlases. Then, the texture atlases are remapped onto the geometric meshes. For the triangle with the original non-tiled texture, new texture coordinates in the texture atlases can be calculated directly. However, as for the triangle with the original tiled texture, it is clipped into many unit triangles to apply texture mapping. Although the method increases the mesh vertex number, the increased geometric vertices have much less impact on the rendering efficiency compared with the method of increasing the texture space. The experiment results show that our method can significantly improve building model rendering efficiency for large-scale 3D city visualization.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 5 ( 2019-05-04), p. 202-
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 5 ( 2019-05-04), p. 202-
    Abstract: Competitive location problems (CLPs) are a crucial business concern. Evaluating customers’ sensitivities to different facility attractions (such as distance and business area) is the premise for solving a CLP. Currently, the development of location-based services facilitates the use of location data for sensitivity evaluations. Most studies based on location data assumed the customers’ sensitivities to be global and constant over space. In this paper, we proposed a new method of using social media data to solve competitive location problems based on the evaluation of customers’ local sensitivities. Regular units were first designed to spatially aggregate social media data to extract samples with uniform spatial distribution. Then, geographically weighted regression (GWR) and the Huff model were combined to evaluate local sensitivities. By applying the evaluation results, the captures for different feasible locations were calculated, and the optimal location for a new retail facility could be determined. In our study, the five largest retail agglomerations in Beijing were taken as test cases, and a possible new retail agglomeration was located. The results of our study can help people have a better understanding of the spatial variation of customers’ local sensitivities. In addition, our results indicate that our method can solve competitive location problems in a cost-effective way.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 10
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 11 ( 2021-10-26), p. 723-
    Abstract: Mastering the lodging risk of planting environment is of great significance to the optimal layout of maize varieties and the breeding of lodging resistant varieties. However, the existing lodging risk models are still at the stage of single or multi-factors independent analysis, and lack of assessment for different lodging types. To address this issue, based on the mechanism of different lodging types, the Archimedean copula function was used to describe the joint probability distribution of wind speed and precipitation, and the lodging risk assessment model of maize was established. By comparing the goodness of fit, when the rank correlation coefficient of these two is positive and negative, the corresponding optimal joint probability distribution functions are the Gumbel copula and Frank copula. According to the spatial distribution of lodging risk, the area from Liaodong Bay northward to Tongyu, Jilin province in the Northeast and the North China Plain has a high frequency of lodging, in which the probability of stalk lodging is two to four times that of root lodging. Finally, we discussed how to apply the lodging risk distribution results to optimize the maize variety test sites to improve the efficiency and reliability of the existing test system. The method proposed in this paper comprehensively considers the synergistic effect of multiple factors and can provide technical support for other risk assessment.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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