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  • Cartography and geographic base data  (6)
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  • Cartography and geographic base data  (6)
  • 1
    Online Resource
    Online Resource
    Employing Incremental Outlines for OpenStreetMap Data Updating
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 7 ( 2018-07-20), p. 277-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 7 ( 2018-07-20), p. 277-
    Abstract: The updating of changing information plays a significant role in ensuring the quality of OpenStreetMap, which is usually completed by mapping the whole changing objects with a high degree of uncertainty. The incremental object-based approach provides opportunities to reduce the unreliability of data, while challenges of data inaccuracy and redundancy remain. This paper provides an incremental outline-based approach for OpenStreetMap data updating to solve this issue. First, incremental outlines are delineated from the changed objects and distinguished through a spatial classification. Then, attribute information corresponding to incremental outlines is proposed to assist in describing the physical changes. Finally, through a geometric calculation based on both the spatial and attribute information, updating operations are constructed with a variety of rules to activate the data updating process. The proposed approach was verified by updating an area in the OpenStreetMap datasets. The result shows that the incremental outline-based updating approach can reduce both the time and storage costs compared to incremental objects and further improve data quality in the updating process.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7070277
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Landslide Image Captioning Method Based on Semantic Gate and Bi-Temporal LSTM
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-03-26), p. 194-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-03-26), p. 194-
    Abstract: When a landslide happens, it is important to recognize the hazard-affected bodies surrounding the landslide for the risk assessment and emergency rescue. In order to realize the recognition, the spatial relationship between landslides and other geographic objects such as residence, roads and schools needs to be defined. Comparing with semantic segmentation and instance segmentation that can only recognize the geographic objects separately, image captioning can provide richer semantic information including the spatial relationship among these objects. However, the traditional image captioning methods based on RNNs have two main shortcomings: the errors in the prediction process are often accumulated and the location of attention is not always accurate which would lead to misjudgment of risk. To handle these problems, a landslide image interpretation network based on a semantic gate and a bi-temporal long-short term memory network (SG-BiTLSTM) is proposed in this paper. In the SG-BiTLSTM architecture, a U-Net is employed as an encoder to extract features of the images and generate the mask maps of the landslides and other geographic objects. The decoder of this structure consists of two interactive long-short term memory networks (LSTMs) to describe the spatial relationship among these geographic objects so that to further determine the role of the classified geographic objects for identifying the hazard-affected bodies. The purpose of this research is to judge the hazard-affected bodies of the landslide (i.e., buildings and roads) through the SG-BiTLSTM network to provide geographic information support for emergency service. The remote sensing data was taken by Worldview satellite after the Wenchuan earthquake happened in 2008. The experimental results demonstrate that SG-BiTLSTM network shows remarkable improvements on the recognition of landslide and hazard-affected bodies, compared with the traditional LSTM (the Baseline Model), the BLEU1 of the SG-BiTLSTM is improved by 5.89%, the matching rate between the mask maps and the focus matrix of the attention is improved by 42.81%. In conclusion, the SG-BiTLSTM network can recognize landslides and the hazard-affected bodies simultaneously to provide basic geographic information service for emergency decision-making.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040194
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Multiclassification Method of Landslide Risk Assessment in Consideration of Disaster Levels: A Case Study of Xianyang City, Shaanxi Province
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 10 ( 2021-09-26), p. 646-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 10 ( 2021-09-26), p. 646-
    Abstract: Geological disaster risk assessment can quantitatively assess the risk of disasters to hazard-bearing bodies. Visualizing the risk of geological disasters can provide scientific references for regional engineering construction, urban planning, and disaster prevention and mitigation. There are some problems in the current binary classification landslide risk assessment model, such as a single sample type, slow multiclass classification speed, large differences in the number of positive and negative samples, and large errors in classification results. This paper introduces multilevel landslide hazard scale samples, selects multiple types of samples according to the divided multilevel landslide hazard scale grade, and proposes a landslide hazard assessment model based on a multiclass support vector machine (SVM). Due to the objective limitations of the single weighting method, the combined weights are used to determine the vulnerability of the landslide hazard-bearing body, and the analytic hierarchy process (AHP) and entropy method are combined to construct a landslide vulnerability assessment model that considers subjective and objective weights. This paper takes landslide disasters in Xianyang City, Shaanxi Province, as the research object. Based on the landslide hazard assessment model and the landslide vulnerability assessment model, a landslide risk assessment experiment is carried out. It generates the landslide risk assessment zoning map and summarizes the risk characteristics of landslides in various towns. The experimental results verify the feasibility and effectiveness of the proposed model and provide important decision support for decision makers in Xianyang City.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10100646
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Graphic Simplification and Intelligent Adjustment Methods of Road Networks for Navigation with Reduced Precision
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 8 ( 2020-08-16), p. 490-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 8 ( 2020-08-16), p. 490-
    Abstract: With the rapid development of high-precision road network maps, low-precision road network maps (basic data unrelated to hardware) will need to be directly produced for traditional navigation software from high-precision maps. To do so, large amounts of vector data representing road networks must be simplified and spatial directional similarity in road networks must be maintained while reducing precision. In this study, an elite strategy genetic algorithm based on the grid model is applied to spatial directional adjustment in road networks for producing road network maps for traditional navigation. Firstly, semantic features and critical vertices are extracted from the road network with high precision. Secondly, some high-precision vertices are eliminated under constraints of the digital navigation map. During this process, the local shape maintenance of the road is considered, and the destruction of the spatial topological relationships is avoided. Thirdly, a genetic algorithm for minimizing the total changes in road azimuths at nodes of road networks is developed to maintain spatial directional relationships while reducing precision. Experimental results and visualization effects on the test data of different cities show that this method is suitable for generating road network maps for traditional navigation software from high-precision ones.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9080490
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    Exploring human mobility patterns using geo-tagged social media data at the group level
    Informa UK Limited ; 2019
    In:  Journal of Spatial Science Vol. 64, No. 2 ( 2019-05-04), p. 221-238
    Details
    In: Journal of Spatial Science, Informa UK Limited, Vol. 64, No. 2 ( 2019-05-04), p. 221-238
    Type of Medium: Online Resource
    ISSN: 1449-8596 , 1836-5655
    URL: Article
    DOI: 10.1080/14498596.2017.1421487
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2019
    detail.hit.zdb_id: 2216491-1
    SSG: 14,1
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  • 6
    Online Resource
    Online Resource
    Multi-Scale Remote Sensing Semantic Analysis Based on a Global Perspective
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 9 ( 2019-09-17), p. 417-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 9 ( 2019-09-17), p. 417-
    Abstract: Remote sensing image captioning involves remote sensing objects and their spatial relationships. However, it is still difficult to determine the spatial extent of a remote sensing object and the size of a sample patch. If the patch size is too large, it will include too many remote sensing objects and their complex spatial relationships. This will increase the computational burden of the image captioning network and reduce its precision. If the patch size is too small, it often fails to provide enough environmental and contextual information, which makes the remote sensing object difficult to describe. To address this problem, we propose a multi-scale semantic long short-term memory network (MS-LSTM). The remote sensing images are paired into image patches with different spatial scales. First, the large-scale patches have larger sizes. We use a Visual Geometry Group (VGG) network to extract the features from the large-scale patches and input them into the improved MS-LSTM network as the semantic information, which provides a larger receptive field and more contextual semantic information for small-scale image caption so as to play the role of global perspective, thereby enabling the accurate identification of small-scale samples with the same features. Second, a small-scale patch is used to highlight remote sensing objects and simplify their spatial relations. In addition, the multi-receptive field provides perspectives from local to global. The experimental results demonstrated that compared with the original long short-term memory network (LSTM), the MS-LSTM’s Bilingual Evaluation Understudy (BLEU) has been increased by 5.6% to 0.859, thereby reflecting that the MS-LSTM has a more comprehensive receptive field, which provides more abundant semantic information and enhances the remote sensing image captions.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8090417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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    Online Resource
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