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  • Cartography and geographic base data  (36)
  • 1
    Online Resource
    Online Resource
    Machine learning with a susceptibility index-based sampling strategy for landslide susceptibility assessment
    Informa UK Limited ; 2024
    In:  Geocarto International Vol. 37, No. 27 ( 2024-02-20), p. 15683-15713
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 27 ( 2024-02-20), p. 15683-15713
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2022.2102221
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2024
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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  • 2
    Online Resource
    Online Resource
    A bibliometric analysis of the landslide susceptibility research (1999–2021)
    Informa UK Limited ; 2022
    In:  Geocarto International Vol. 37, No. 26 ( 2022-12-13), p. 14309-14334
    Details
    In: Geocarto International, Informa UK Limited, Vol. 37, No. 26 ( 2022-12-13), p. 14309-14334
    Type of Medium: Online Resource
    ISSN: 1010-6049 , 1752-0762
    URL: Article
    DOI: 10.1080/10106049.2022.2087753
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2109550-4
    SSG: 14
    SSG: 14,1
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Evaluating Natural Ecological Land Change in Function-Oriented Planning Regions Using the National Land Use Survey Data from 2009 to 2018 in China
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 3 ( 2021-03-16), p. 172-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 3 ( 2021-03-16), p. 172-
    Abstract: The natural ecological lands, such as forest land, grassland, wetland, etc., constitute the most important factor for maintaining and preserving the earth’s ecosystem, which must be well concerned in the regional function-oriented planning for the sustainability of human economic development. We analyzed and evaluated the change of natural ecological land in the function-oriented planning regions where we applied the major function-oriented zones introduced as a new concept in China. Using the land-use data from 2009 to 2018 that were produced by the National Land Use Survey, we re-classified natural ecological land types into the forest, grassland, wetland, and bare land, and then addressed the changes of natural ecological land types from 2009 to 2018 in the major function-oriented zones. As a result, the area of natural ecological lands generally tended to decrease from 2009 to 2018, while the decreasing trend of natural ecological land areas was controlled after 2015 with the implementation of governmental policies for environment protection and eco-logical projects. Especially, the decrease of forest land area significantly tended to be zero in 2018 in optimal development zones. The decreased areas of natural ecological lands were mostly converted from artificial land from 2008 to 2019. On the other side, the forest lands mostly changed from cropland and grassland in key development zones, agricultural production zones, and key ecological function zones, due to the fact that grassland conversed in afforestation during this period. The evaluation of natural ecological land changes, which could be implemented by using the annual updates of national land-use data in China, is significant to support the government’s spatial regulation design, to reshape the planned regions, and make policies for environmental restoration and protection management.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10030172
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 4
    Online Resource
    Online Resource
    Cloud and Snow Segmentation in Satellite Images Using an Encoder–Decoder Deep Convolutional Neural Networks
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 7 ( 2021-07-06), p. 462-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 7 ( 2021-07-06), p. 462-
    Abstract: The segmentation of cloud and snow in satellite images is a key step for subsequent image analysis, interpretation, and other applications. In this paper, a cloud and snow segmentation method based on a deep convolutional neural network (DCNN) with enhanced encoder–decoder architecture—ED-CNN—is proposed. In this method, the atrous spatial pyramid pooling (ASPP) module is used to enhance the encoder, while the decoder is enhanced with the fusion of features from different stages of the encoder, which improves the segmentation accuracy. Comparative experiments show that the proposed method is superior to DeepLabV3+ with Xception and ResNet50. Additionally, a rough-labeled dataset containing 23,520 images and fine-labeled data consisting of 310 images from the TH-1 satellite are created, where we studied the relationship between the quality and quantity of labels and the performance of cloud and snow segmentation. Through experiments on the same network with different datasets, we found that the cloud and snow segmentation performance is related more closely to the quantity of labels rather than their quality. Namely, under the same labeling consumption, using rough-labeled images only performs better than rough-labeled images plus 10% fine-labeled images.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10070462
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 5
    Online Resource
    Online Resource
    A parallel annotation placement method for dense point of interest labels using hexagonal grid
    Informa UK Limited ; 2021
    In:  Cartography and Geographic Information Science Vol. 48, No. 2 ( 2021-03-04), p. 95-104
    Details
    In: Cartography and Geographic Information Science, Informa UK Limited, Vol. 48, No. 2 ( 2021-03-04), p. 95-104
    Type of Medium: Online Resource
    ISSN: 1523-0406 , 1545-0465
    URL: Article
    DOI: 10.1080/15230406.2020.1833761
    Language: English
    Publisher: Informa UK Limited
    Publication Date: 2021
    detail.hit.zdb_id: 2111978-8
    SSG: 14,1
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  • 6
    Online Resource
    Online Resource
    Using Restaurant POI Data to Explore Regional Structure of Food Culture Based on Cuisine Preference
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 1 ( 2021-01-18), p. 38-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 1 ( 2021-01-18), p. 38-
    Abstract: As a result of the influence of geographical environment and historical heritage, food preference has significant regional differentiation characteristics. However, the spatial structure of food culture represented by the cuisine culture at the regional level has not yet been explored from the perspective of geography. Cultural regionalization is an important way to analyze and understand the spatial structure of food culture. It is of great significance to deeply mine intra-regional homogeneity and scientifically cognize inter-regional cultural characteristics. This study aims to explore such patterns by focusing on the restaurants of the eight most famous cuisines in Mainland China. Initially, the density based geospatial hotspot detector method is proposed to analyze and mapping the spatial quantitative characteristics of the eight major cuisines. A heuristic method for geographical regionalization based on machine learning was used to analyze spatial distribution patterns in accordance with the proportion of these cuisines in each prefecture-level city. Results show that some types of single-category cuisines have a stronger spatial concentration effect in the present, whereas others have a strong diffusion trend. In the comprehensive analysis of multicategory cuisines, the eight major cuisines formed a new structure of geographical regionalization of Chinese cuisine culture. This study is helpful to understand regional structure characteristics of food preference, and the density-based hotspot detector proposed in this paper can also be used in the analysis of other type of point of interest (POI) data.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10010038
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 7
    Online Resource
    Online Resource
    Semantic 3D Modeling Based on CityGML for Ancient Chinese-Style Architectural Roofs of Digital Heritage
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 5 ( 2017-04-27), p. 132-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 5 ( 2017-04-27), p. 132-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6050132
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
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  • 8
    Online Resource
    Online Resource
    Using Landsat OLI and Random Forest to Assess Grassland Degradation with Aboveground Net Primary Production and Electrical Conductivity Data
    MDPI AG ; 2019
    In:  ISPRS International Journal of Geo-Information Vol. 8, No. 11 ( 2019-11-12), p. 511-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 8, No. 11 ( 2019-11-12), p. 511-
    Abstract: Grassland coverage, aboveground net primary production (ANPP), and species composition are used as indicators of grassland degradation. However, soil salinization deficiency, which is also a factor of grassland degradation, is rarely used in grassland degradation assessment in semiarid regions. We assessed grassland degradation by its quality, quantity, and spatial pattern over semiarid west Jilin, China. Considering soil salinization in west Jilin, electrical conductivity (EC) is used as an index with ANPP to assess grassland degradation. First, the spatial distribution of the grassland was measured with information mined from multi-temporal remote sensing images using an object-based image analysis combined with classification and decision tree methods. Second, with 166 field samples, we utilized the random forest (RF) algorithm as the variable selection and regression method for predicting EC and ANPP. Finally, we created a new grassland degradation model (GDM) based on ANPP and EC. The results showed the R2 (0.91) and RMSE (0.057 mS/cm) of the EC model were generally highest and lowest when the ntree was 400; the ANPP model was optimal (R2 = 0.85 and RMSE = 15.81 gC/m2) when the ntree was 600. Grassland area of west Jilin was 609.67 × 103 ha in 2017, there were 373.79 × 103 ha of degraded grassland, with 210.47 × 103 ha being intensively degraded. This paper surpasses past limitations of excessive reliance on vegetation index to construct a grassland degradation model which considers the characteristics of the study area and soil salinity. The results confirm the positive influence of the ecological conservation projects sponsored by the government. The research outcome could offer supporting data for decision making to help alleviate grassland degradation and promote the rehabilitation of grassland vegetation.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi8110511
    Language: English
    Publisher: MDPI AG
    Publication Date: 2019
    detail.hit.zdb_id: 2655790-3
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  • 9
    Online Resource
    Online Resource
    Spatiotemporal Analytics of Environmental Sounds and Influencing Factors Based on Urban Sensor Network Data
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 9 ( 2023-09-21), p. 386-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 9 ( 2023-09-21), p. 386-
    Abstract: Urban construction has accelerated the deterioration of the urban sound environment, which has constrained urban development and harmed people’s health. This study aims to explore the spatiotemporal patterns of environmental sound and determine the influencing factors on the spatial differentiation of sound, thus supporting sustainable urban planning and decision-making. Fine-grained sound data are used in most urban sound-related research, but such data are difficult to obtain. For this problem, this study analyzed sound trends using Array of Things (AoT) sensing data. Additionally, this study explored the influences on the spatial differentiation of sound using GeoDetector (version number: 1.0-4), thus addressing the limitation of previous studies that neglected to explore the influences on spatial heterogeneity. Our experimental results showed that sound levels in different areas of Chicago fluctuated irregularly over time. During the morning peak on weekdays: the four southern areas of Chicago have a high–high sound gathering mode, and the remaining areas are mostly randomly distributed; the sound level of a certain area has a significant negative correlation with population density, park area, and density of bike route; park area and population density are the main factors affecting the spatial heterogeneity of Chicago’s sound; and population density and park area play an essential role in factor interaction. This study has some theoretical significance and practical value. Residents can choose areas with lower noise for leisure activities according to the noise map of this study. While planning urban development, urban planners should pay attention to the single and interactive effects of factors in the city, such as parks, road network structures, and points of interest, on the urban sound environment. Researchers can build on this study to conduct studies on larger time scales.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12090386
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2655790-3
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  • 10
    Online Resource
    Online Resource
    An Accurate Matching Method for Projecting Vector Data into Surveillance Video to Monitor and Protect Cultivated Land
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 7 ( 2020-07-17), p. 448-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 7 ( 2020-07-17), p. 448-
    Abstract: The integration of intelligent video surveillance and GIS (geograhical information system) data provides a new opportunity for monitoring and protecting cultivated land. For a GIS-based video monitoring system, the prerequisite is to align the GIS data with video image. However, existing methods or systems have their own shortcomings when implemented in monitoring cultivated land. To address this problem, this paper aims to propose an accurate matching method for projecting vector data into surveillance video, considering the topographic characteristics of cultivated land in plain area. Once an adequate number of control points are identified from 2D (two-dimensional) GIS data and the selected reference video image, the alignment of 2D GIS data and PTZ (pan-tilt-zoom) video frames can be realized by automatic feature matching method. Based on the alignment results, we can easily identify the occurrence of farmland destruction by visually inspecting the image content covering the 2D vector area. Furthermore, a prototype of intelligent surveillance video system for cultivated land is constructed and several experiments are conducted to validate the proposed approach. Experimental results show that the proposed alignment methods can achieve a high accuracy and satisfy the requirements of cultivated land monitoring.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9070448
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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