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  • Cartography and geographic base data  (92)
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  • Cartography and geographic base data  (92)
  • 1
    Online Resource
    Online Resource
    A Deep Transfer Learning Toponym Extraction and Geospatial Clustering Framework for Investigating Scenic Spots as Cognitive Regions
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 5 ( 2023-05-12), p. 196-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 5 ( 2023-05-12), p. 196-
    Abstract: In recent years, the Chinese tourism industry has developed rapidly, leading to significant changes in the relationship between people and space patterns in scenic regions. To attract more tourists, the surrounding environment of a scenic region is usually well developed, attracting a large number of human activities, which creates a cognitive range for the scenic region. From the perspective of tourism, tourists’ perceptions of the region in which tourist attractions are located in a city usually differ from the objective region of the scenic spots. Among them, social media serves as an important medium for tourists to share information about scenic spots and for potential tourists to learn scenic spot information, and it interacts to influence people’s perceptions of the destination image. Extracting the names of tourist attractions from social media data and exploring their spatial distribution patterns is the basis for research on the cognitive region of tourist attractions. This study takes Hangzhou, a well-known tourist city in China, as a case study to explore the human cognitive region of its popular scenic spots. First, we propose a Chinese tourist attraction name extraction model based on RoBERTa-BiLSTM-CRF to extract the names of tourist attractions from social media data. Then, we use a multi-distance spatial clustering method called Ripley’s K to filter the extracted tourist attraction names. Finally, we combine road network data and polygons generated using the chi-shape algorithm to construct the vague cognitive regions of each scenic spot. The results show that the classification indicators of our proposed tourist attraction name extraction model are significantly better than those of previous toponym extraction models and algorithms (precision = 0.7371, recall = 0.6926, F1 = 0.7141), and the extracted vague cognitive regions of tourist attractions also generally conform to people’s habitual cognition.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12050196
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 2
    Online Resource
    Online Resource
    Spatial Concept Query Based on Lattice-Tree
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 5 ( 2022-05-15), p. 312-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 5 ( 2022-05-15), p. 312-
    Abstract: As a basic method of spatial data operation, spatial keyword query can provide meaningful information to meet user demands by searching spatial textual datasets. How to accurately understand users’ intentions and efficiently retrieve results from spatial textual big data are always the focus of research. Spatial textual big data and their complex correlation between textual features not only enrich the connotation of spatial objects but also bring difficulties to the efficient recognition and retrieval of similar spatial objects. Because there are a lot of many-to-many relationships between massive spatial objects and textual features, most of the existing research results that employ tree-like and table-like structures to index spatial data and textual data are inefficient in retrieving similar spatial objects. In this paper, firstly, we define spatial textual concept (STC) as a group of spatial objects with the same textual keywords in a limited spatial region in order to present the many-to-many relationships between spatial objects and textual features. Then we attempt to introduce the concept lattice model to maintain a group of related STCs and propose a hybrid tree-like spatial index structure, the lattice-tree, for spatial textual big data. Lattice-tree employs R-tree to index the spatial location of objects, and it embeds a concept lattice structure into specific tree nodes to organize the STC set from a large number of textual keywords of objects and their relationships. Based on this, we also propose a novel spatial keyword query, named Top-k spatial concept query (TkSCQ), to answer STC and retrieve similar spatial objects with multiple textual features. The empirical study is carried out on two spatial textual big data sets from Yelp and Amap. Experiments on the lattice-tree verify its feasibility and demonstrate that it is efficient to embed the concept lattice structure into tree nodes of 3 to 5 levels. Experiments on TkSCQ evaluate lattice from results, keywords, data volume, and so on, and two baseline index structures based on IR-tree and Fp-tree, named the inverted-tree and Fpindex-tree, are developed to compare with the lattice-tree on data sets from Yelp and Amap. Experimental results demonstrate that the Lattice-tree has the better retrieval efficiency in most cases, especially in the case of large amounts of data queries, where the retrieval performance of the lattice-tree is much better than the inverted-tree and Fpindex-tree.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11050312
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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  • 3
    Online Resource
    Online Resource
    A comparative study of the performances of joint RFE with machine learning algorithms for extracting Moso bamboo ( Phyllostachys pubescens ) forest based on UAV hyperspectral images
    Informa UK Limited ; 2023
    In:  Geocarto International Vol. 38, No. 1 ( 2023-06-10)
    Details
    In: Geocarto International, Informa UK Limited, Vol. 38, No. 1 ( 2023-06-10)
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2023.2207550
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2023
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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    Online Resource
  • 4
    Online Resource
    Online Resource
    Urban Geological 3D Modeling Based on Papery Borehole Log
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 6 ( 2020-06-12), p. 389-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 6 ( 2020-06-12), p. 389-
    Abstract: Borehole log is important data for urban geological 3D modeling. Most of the current borehole logs are stored in a papery form. The construction of a smart city puts forward requirements for the automatic and intelligent 3D modeling of urban geology. However, it is difficult to extract the information from the papery borehole log quickly. What is more, it is unreliable to rely entirely on automated algorithms for modeling without artificial participation, but there is no effective way to integrate geological knowledge into 3D geological modeling currently. Therefore, it is necessary to research how to use existing papery borehole logs efficiently. To overcome the above obstacles, we designed a method that combines structural analysis and layout understanding to extract information from the borehole log. Then, the knowledge-driven three-dimensional geological modeling is proposed based on dynamic profiles. With these methods, the papery borehole log can be converted into structured data which can be used for data analysis directly, and geological knowledge can be integrated into the process of 3D geological modeling. The 3D geological modeling of Xinyang City based on a papery borehole log has been taken as an example to verify the feasibility of the method.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9060389
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    ChineseCTRE: A Model for Geographical Named Entity Recognition and Correction Based on Deep Neural Networks and the BERT Model
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 10 ( 2023-09-27), p. 394-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 10 ( 2023-09-27), p. 394-
    Abstract: Social media is widely used to share real-time information and report accidents during natural disasters. Named entity recognition (NER) is a fundamental task of geospatial information applications that aims to extract location names from natural language text. As a result, the identification of location names from social media information has gradually become a demand. Named entity correction (NEC), as a complementary task of NER, plays a crucial role in ensuring the accuracy of location names and further improving the accuracy of NER. Despite numerous methods having been adopted for NER, including text statistics-based and deep learning-based methods, there has been limited research on NEC. To address this gap, we propose the CTRE model, which is a geospatial named entity recognition and correction model based on the BERT model framework. Our approach enhances the BERT model by introducing incremental pre-training in the pre-training phase, significantly improving the model’s recognition accuracy. Subsequently, we adopt the pre-training fine-tuning mode of the BERT base model and extend the fine-tuning process, incorporating a neural network framework to construct the geospatial named entity recognition model and geospatial named entity correction model, respectively. The BERT model utilizes data augmentation of VGI (volunteered geographic information) data and social media data for incremental pre-training, leading to an enhancement in the model accuracy from 85% to 87%. The F1 score of the geospatial named entity recognition model reaches an impressive 0.9045, while the precision of the geospatial named entity correction model achieves 0.9765. The experimental results robustly demonstrate the effectiveness of our proposed CTRE model, providing a reference for subsequent research on location names.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12100394
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 6
    Online Resource
    Online Resource
    Inventory and Distribution Characteristics of Large-Scale Landslides in Baoji City, Shaanxi Province, China
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 1 ( 2021-12-29), p. 10-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 1 ( 2021-12-29), p. 10-
    Abstract: Inventories of historical landslides play an important role in the assessment of natural hazards. In this study, we used high-resolution satellite imagery from Google Earth to interpret large landslides in Baoji city, Shaanxi Province on the southwestern edge of the Loess Plateau. Then, a comprehensive and detailed map of the landslide distribution in this area was prepared in conjunction with the historical literature, which includes 3440 landslides. On this basis, eight variables, including elevation, slope, aspect, slope position, distance to the fault, land cover, lithology and distance to the stream were selected to examine their influence on the landslides in the study area. Landslide number density (LND) and landslide area percentage (LAP) were used as evaluation indicators to analyze the spatial distribution characteristics of the landslides. The results show that most of the landslides are situated at elevations from 500 to 1400 m. The LND and LAP reach their peaks at slopes of 10–20°. Slopes facing WNW and NW directions, and middle and lower slopes are more prone to sliding with higher LND and LAP. LND and LAP show a decreasing trend as the distance to the fault or stream increases, followed by a slow rise. Landslides occur primarily in the areas covered by crops. Regarding lithology, the regions covered by the Quaternary loess and Cretaceous gravels are the main areas where landslides occur. The results would be helpful for further understanding the developmental characteristics and spatial distribution of landslides on the Loess Plateau, and also provide a support to subsequent landslide susceptibility mapping in this region.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11010010
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    Water Areas Segmentation from Remote Sensing Images Using a Separable Residual SegNet Network
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-04-18), p. 256-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-04-18), p. 256-
    Abstract: Changes on lakes and rivers are of great significance for the study of global climate change. Accurate segmentation of lakes and rivers is critical to the study of their changes. However, traditional water area segmentation methods almost all share the following deficiencies: high computational requirements, poor generalization performance, and low extraction accuracy. In recent years, semantic segmentation algorithms based on deep learning have been emerging. Addressing problems associated to a very large number of parameters, low accuracy, and network degradation during training process, this paper proposes a separable residual SegNet (SR-SegNet) to perform the water area segmentation using remote sensing images. On the one hand, without compromising the ability of feature extraction, the problem of network degradation is alleviated by adding modified residual blocks into the encoder, the number of parameters is limited by introducing depthwise separable convolutions, and the ability of feature extraction is improved by using dilated convolutions to expand the receptive field. On the other hand, SR-SegNet removes the convolution layers with relatively more convolution kernels in the encoding stage, and uses the cascading method to fuse the low-level and high-level features of the image. As a result, the whole network can obtain more spatial information. Experimental results show that the proposed method exhibits significant improvements over several traditional methods, including FCN, DeconvNet, and SegNet.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040256
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    A Hybrid Method to Incrementally Extract Road Networks Using Spatio-Temporal Trajectory Data
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 4 ( 2020-03-25), p. 186-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 4 ( 2020-03-25), p. 186-
    Abstract: With the rapid development of urban traffic, accurate and up-to-date road maps are in crucial demand for daily human life and urban traffic control. Recently, with the emergence of crowdsourced mapping, a surge in academic attention has been paid to generating road networks from spatio-temporal trajectory data. However, most existing methods do not explore changing road patterns contained in multi-temporal trajectory data and it is still difficult to satisfy the precision and efficiency demands of road information extraction. Hence, in this paper, we propose a hybrid method to incrementally extract urban road networks from spatio-temporal trajectory data. First, raw trajectory data were partitioned into K time slices and were used to initialize K-temporal road networks by a mathematical morphology method. Then, the K-temporal road networks were adjusted according to a gravitation force model so as to amend their geometric inconsistencies. Finally, road networks were geometrically delineated using the k-segment fitting algorithm, and the associated road attributes (e.g., road width and driving rule) were inferred. Several case studies were examined to demonstrate that our method can effectively improve the efficiency and precision of road extraction and can make a significant attempt to mine the incremental change patterns in road networks from spatio-temporal trajectory data to help with road map renewal.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9040186
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    Occlusion-Free Visualization of Important Geographic Features in 3D Urban Environments
    MDPI AG ; 2016
    In:  ISPRS International Journal of Geo-Information Vol. 5, No. 8 ( 2016-08-08), p. 138-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 5, No. 8 ( 2016-08-08), p. 138-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi5080138
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
    detail.hit.zdb_id: 2655790-3
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  • 10
    Online Resource
    Online Resource
    Fire Scenario Zone Construction and Personnel Evacuation Planning Based on a Building Information Model and Geographical Information System
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 2 ( 2022-02-03), p. 110-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 2 ( 2022-02-03), p. 110-
    Abstract: The spatial–temporal simulation of fire disasters and evacuation route planning are important research fields for urban emergency responses and are primary tasks that answer complex questions after fires break out. The increasing demand for refined building information models will sharply increase the calculated and analyzed quantity. This demand presents a challenge for fire emergency responses based on massive building information. In this paper, the principle of the realistic worst case (RWC) is introduced into fire simulation and evacuation route planning. Taking the library of the Nanjing Forestry University as the study object, the spatial–temporal characteristics of the influential environmental factors of the fire are simulated, such as the meteorological elements, building structure, and building skin. The scenario zones that are relatively prone to fire are selected using an overlay analysis across the four seasons. Then, the risk threshold for evacuating personnel is analyzed in the fire zone according to international standards and firefighting criteria. Specific parameters are determined based on the analysis of the above. The growing trends for fires across the four seasons are simulated with scenario zones as the starting positions and incorporate factors such as temperature, carbon monoxide, and smoke. Lastly, a life safety assurance path (LSAP) for personnel evacuation is designed based on an indoor road network and path search algorithm. The evacuation planning result is compared with the traditional shortest-time path and shortest-distance path. Based on the study results, fire scenario zones can improve the speed and operating efficiency of spatial–temporal simulation models of fire and can also support path planning and design for emergency responses.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11020110
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2655790-3
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