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  • Cartography and geographic base data  (8)
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  • Cartography and geographic base data  (8)
  • 1
    Online Resource
    Online Resource
    A New Approach to Measuring the Similarity of Indoor Semantic Trajectories
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 2 ( 2021-02-20), p. 90-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 2 ( 2021-02-20), p. 90-
    Abstract: People spend more than 80% of their time in indoor spaces, such as shopping malls and office buildings. Indoor trajectories collected by indoor positioning devices, such as WiFi and Bluetooth devices, can reflect human movement behaviors in indoor spaces. Insightful indoor movement patterns can be discovered from indoor trajectories using various clustering methods. These methods are based on a measure that reflects the degree of similarity between indoor trajectories. Researchers have proposed many trajectory similarity measures. However, existing trajectory similarity measures ignore the indoor movement constraints imposed by the indoor space and the characteristics of indoor positioning sensors, which leads to an inaccurate measure of indoor trajectory similarity. Additionally, most of these works focus on the spatial and temporal dimensions of trajectories and pay less attention to indoor semantic information. Integrating indoor semantic information such as the indoor point of interest into the indoor trajectory similarity measurement is beneficial to discovering pedestrians having similar intentions. In this paper, we propose an accurate and reasonable indoor trajectory similarity measure called the indoor semantic trajectory similarity measure (ISTSM), which considers the features of indoor trajectories and indoor semantic information simultaneously. The ISTSM is modified from the edit distance that is a measure of the distance between string sequences. The key component of the ISTSM is an indoor navigation graph that is transformed from an indoor floor plan representing the indoor space for computing accurate indoor walking distances. The indoor walking distances and indoor semantic information are fused into the edit distance seamlessly. The ISTSM is evaluated using a synthetic dataset and real dataset for a shopping mall. The experiment with the synthetic dataset reveals that the ISTSM is more accurate and reasonable than three other popular trajectory similarities, namely the longest common subsequence (LCSS), edit distance on real sequence (EDR), and the multidimensional similarity measure (MSM). The case study of a shopping mall shows that the ISTSM effectively reveals customer movement patterns of indoor customers.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10020090
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Progress and Challenges on Entity Alignment of Geographic Knowledge Bases
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 2 ( 2019-02-06), p. 77-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 2 ( 2019-02-06), p. 77-
    Abstract: Geographic knowledge bases (GKBs) with multiple sources and forms are of obvious heterogeneity, which hinders the integration of geographic knowledge. Entity alignment provides an effective way to find correspondences of entities by measuring the multidimensional similarity between entities from different GKBs, thereby overcoming the semantic gap. Thus, many efforts have been made in this field. This paper initially proposes basic definitions and a general framework for the entity alignment of GKBs. Specifically, the state-of-the-art of algorithms of entity alignment of GKBs is reviewed from the three aspects of similarity metrics, similarity combination, and alignment judgement; the evaluation procedure of alignment results is also summarized. On this basis, eight challenges for future studies are identified. There is a lack of methods to assess the qualities of GKBs. The alignment process should be improved by determining the best composition of heterogeneous features, optimizing alignment algorithms, and incorporating background knowledge. Furthermore, a unified infrastructure, techniques for aligning large-scale GKBs, and deep learning-based alignment techniques should be developed. Meanwhile, the generation of benchmark datasets for the entity alignment of GKBs and the applications of this field need to be investigated. The progress of this field will be accelerated by addressing these challenges.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8020077
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Filtering Link Outliers in Vehicle Trajectories by Spatial Reasoning
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 5 ( 2021-05-14), p. 333-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 5 ( 2021-05-14), p. 333-
    Abstract: Vehicle trajectories derived from Global Navigation Satellite Systems (GNSS) are used in various traffic applications based on trajectory quality analysis for the development of successful traffic models. A trajectory consists of points and links that are connected, where both the points and links are subject to positioning errors in the GNSS. Existing trajectory filters focus on point outliers, but neglect link outliers on tracks caused by a long sampling interval. In this study, four categories of link outliers are defined, i.e., radial, drift, clustered, and shortcut; current available algorithms are applied to filter apparent point outliers for the first three categories, and a novel filtering approach is proposed for link outliers of the fourth category in urban areas using spatial reasoning rules without ancillary data. The proposed approach first measures specific geometric properties of links from trajectory databases and then evaluates the similarities of geometric measures among the links, following a set of spatial reasoning rules to determine link outliers. We tested this approach using taxi trajectory datasets for Beijing with a built-in sampling interval of 50 to 65 s. The results show that clustered links (27.14%) account for the majority of link outliers, followed by shortcut (6.53%), radial (3.91%), and drift (0.62%) outliers.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10050333
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 4
    Online Resource
    Online Resource
    A3T-GCN: Attention Temporal Graph Convolutional Network for Traffic Forecasting
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 7 ( 2021-07-15), p. 485-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 7 ( 2021-07-15), p. 485-
    Abstract: Accurate real-time traffic forecasting is a core technological problem against the implementation of the intelligent transportation system. However, it remains challenging considering the complex spatial and temporal dependencies among traffic flows. In the spatial dimension, due to the connectivity of the road network, the traffic flows between linked roads are closely related. In the temporal dimension, although there exists a tendency among adjacent time points, the importance of distant time points is not necessarily less than that of recent ones, since traffic flows are also affected by external factors. In this study, an attention temporal graph convolutional network (A3T-GCN) was proposed to simultaneously capture global temporal dynamics and spatial correlations in traffic flows. The A3T-GCN model learns the short-term trend by using the gated recurrent units and learns the spatial dependence based on the topology of the road network through the graph convolutional network. Moreover, the attention mechanism was introduced to adjust the importance of different time points and assemble global temporal information to improve prediction accuracy. Experimental results in real-world datasets demonstrate the effectiveness and robustness of the proposed A3T-GCN. We observe the improvements in RMSE of 2.51–46.15% and 2.45–49.32% over baselines for the SZ-taxi and Los-loop, respectively. Meanwhile, the Accuracies are 0.95–89.91% and 0.26–10.37% higher than the baselines for the SZ-taxi and Los-loop, respectively.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10070485
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    Analyzing the Research Evolution in Response to COVID-19
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 4 ( 2021-04-07), p. 237-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 4 ( 2021-04-07), p. 237-
    Abstract: In order to understand how these studies are evolving to respond to COVID-19 and to facilitate the containment of COVID-19, this paper accurately extracted the spatial and topic information from the metadata of papers related to COVID-19 using text mining techniques, and with the extracted information, the research evolution was analyzed from the temporal, spatial, and topic perspectives. From a temporal view, in the three months after the emergence of COVID-19, the number of published papers showed an obvious growth trend, and it showed a relatively stable cyclical trend in the later period, which is basically consistent with the development of COVID-19. Spatially, most of the authors who participated in related research are concentrated in the United States, China, Italy, the United Kingdom, Spain, India, and France. At the same time, with the continuous spread of COVID-19 in the world, the distribution of the number of authors has gradually expanded, showing to be correlated with the severity of COVID-19 at a spatial scale. From the perspective of topic, the early stage of COVID-19 emergence, the related research mainly focused on the origin and gene identification of the virus. After the emergence of the pandemic, studies related to the diagnosis and analysis of psychological health, personal security, and violent conflict are added. Meanwhile, some categories are most closely related to the control and prevention of the epidemic, such as pathology analysis, diagnosis, and treatment; epidemic situation and coping strategies; and prediction and assessment of epidemic situation. In most time periods, the majority of studies focused on these three categories.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10040237
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 6
    Online Resource
    Online Resource
    A Novel Simplified Algorithm for Bare Surface Soil Moisture Retrieval Using L-Band Radiometer
    MDPI AG ; 2016
    In:  ISPRS International Journal of Geo-Information Vol. 5, No. 8 ( 2016-08-09), p. 143-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 5, No. 8 ( 2016-08-09), p. 143-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi5080143
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    Deep Learning-Based Named Entity Recognition and Knowledge Graph Construction for Geological Hazards
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 1 ( 2019-12-27), p. 15-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 1 ( 2019-12-27), p. 15-
    Abstract: Constructing a knowledge graph of geological hazards literature can facilitate the reuse of geological hazards literature and provide a reference for geological hazard governance. Named entity recognition (NER), as a core technology for constructing a geological hazard knowledge graph, has to face the challenges that named entities in geological hazard literature are diverse in form, ambiguous in semantics, and uncertain in context. This can introduce difficulties in designing practical features during the NER classification. To address the above problem, this paper proposes a deep learning-based NER model; namely, the deep, multi-branch BiGRU-CRF model, which combines a multi-branch bidirectional gated recurrent unit (BiGRU) layer and a conditional random field (CRF) model. In an end-to-end and supervised process, the proposed model automatically learns and transforms features by a multi-branch bidirectional GRU layer and enhances the output with a CRF layer. Besides the deep, multi-branch BiGRU-CRF model, we also proposed a pattern-based corpus construction method to construct the corpus needed for the deep, multi-branch BiGRU-CRF model. Experimental results indicated the proposed deep, multi-branch BiGRU-CRF model outperformed state-of-the-art models. The proposed deep, multi-branch BiGRU-CRF model constructed a large-scale geological hazard literature knowledge graph containing 34,457 entities nodes and 84,561 relations.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9010015
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    Analyzing the Impact of Highways Associated with Farmland Loss under Rapid Urbanization
    MDPI AG ; 2016
    In:  ISPRS International Journal of Geo-Information Vol. 5, No. 6 ( 2016-06-15), p. 94-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 5, No. 6 ( 2016-06-15), p. 94-
    Abstract: Highway construction has accelerated urban growth and induced direct and indirect changes to land use. Although many studies have analyzed the relationship between highway construction and local development, relatively less attention has been paid to clarifying the various impacts of highways associated with farmland loss. This paper integrates GIS spatial analysis, remote sensing, buffer analysis and landscape metrics to analyze the landscape pattern change induced by direct and indirect highway impacts. This paper explores the interaction between the impact of highways and farmland loss, using the case of the highly urbanized traffic hubs in eastern China, Hang-Jia-Hu Plain. Our results demonstrate that the Hang-Jia-Hu Plain experienced extensive highway construction during 1990–2010, with a clear acceleration of expressway development since 2000. This unprecedented highway construction has directly fragmented the regional landscape and indirectly disturbed the regional landscape by attracting a large amount of built-up land transition from farmland during the last two decades. In the highway-effect zone, serious farmland loss initially occurred in the urban region and then spread to the rural region. Moreover, we found the discontinuous expansion of built-up land scattered the farmland in the rural region and expressway-effect zone. Furthermore, farmland protection policies in the 1990s had the effect of controlling the total area of farmland loss. However, the cohesive farmland structure was still fragmented by the direct and indirect impacts of highway construction. We suggest that an overall farmland protection system should be established to enhance spatial control and mitigate the adverse impacts caused by highway construction. This work improves the understanding of regional sustainable development, and provides a scientific basis for balanced urban development with farmland protection in decision-making processes.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi5060094
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2655790-3
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