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  • Cartography and geographic base data  (27)
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  • Cartography and geographic base data  (27)
  • 1
    Online Resource
    Online Resource
    Detection and Analysis of Degree of Maize Lodging Using UAV-RGB Image Multi-Feature Factors and Various Classification Methods
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 5 ( 2021-05-06), p. 309-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 5 ( 2021-05-06), p. 309-
    Abstract: Maize (Zea mays L.), one of the most important agricultural crops in the world, which can be devastated by lodging, which can strike maize during its growing season. Maize lodging affects not only the yield but also the quality of its kernels. The identification of lodging is helpful to evaluate losses due to natural disasters, to screen lodging-resistant crop varieties, and to optimize field-management strategies. The accurate detection of crop lodging is inseparable from the accurate determination of the degree of lodging, which helps improve field management in the crop-production process. An approach was developed that fuses supervised and object-oriented classifications on spectrum, texture, and canopy structure data to determine the degree of lodging with high precision. The results showed that, combined with the original image, the change of the digital surface model, and texture features, the overall accuracy of the object-oriented classification method using random forest classifier was the best, which was 86.96% (kappa coefficient was 0.79). The best pixel-level supervised classification of the degree of maize lodging was 78.26% (kappa coefficient was 0.6). Based on the spatial distribution of degree of lodging as a function of crop variety, sowing date, densities, and different nitrogen treatments, this work determines how feature factors affect the degree of lodging. These results allow us to rapidly determine the degree of lodging of field maize, determine the optimal sowing date, optimal density and optimal fertilization method in field production.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10050309
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2655790-3
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  • 2
    Online Resource
    Online Resource
    An Assessment of Urban Surface Energy Fluxes Using a Sub-Pixel Remote Sensing Analysis: A Case Study in Suzhou, China
    MDPI AG ; 2016
    In:  ISPRS International Journal of Geo-Information Vol. 5, No. 2 ( 2016-02-04), p. 11-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 5, No. 2 ( 2016-02-04), p. 11-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi5020011
    Language: English
    Publisher: MDPI AG
    Publication Date: 2016
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  • 3
    Online Resource
    Online Resource
    Efficient Construction of Voxel Models for Ore Bodies Using an Improved Winding Number Algorithm and CUDA Parallel Computing
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 12 ( 2023-11-21), p. 473-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 12 ( 2023-11-21), p. 473-
    Abstract: The three-dimensional (3D) geological voxel model is essential for numerical simulation and resource calculation. However, it can be challenging due to the point in polygon test in 3D voxel modeling. The commonly used Winding number algorithm requires the manual setting of observation points and uses their relative positions to restrict the positive and negative solid angles. Therefore, we proposed the Winding number with triangle network coding (WNTC) algorithm and applied it to automatically construct a 3D voxel model of the ore body. The proposed WNTC algorithm encodes the stratum model by using the Delaunay triangulation network to constrain the index order of each vertex of the triangular plane unit. GPU parallel computing was used to optimize its computational speed. Our results demonstrated that the WNTC algorithm can greatly improve the efficiency and automation of 3D ore body modeling. Compared to the Ray casting method, it can compensate for a voxel loss of about 0.7%. We found the GPU to be 99.96% faster than the CPU, significantly improving voxel model construction speed. Additionally, this method is less affected by the complexity of the stratum model. Our study has substantial potential for similar work in 3D geological modeling and other relevant fields.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12120473
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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  • 4
    Online Resource
    Online Resource
    Spatial Mismatch between the Supply and Demand of Urban Leisure Services with Multisource Open Data
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 8 ( 2020-07-22), p. 466-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 8 ( 2020-07-22), p. 466-
    Abstract: Understanding the balance between the supply and demand of leisure services (LSs) in urban areas can benefit urban spatial planning and improve the quality of life of residents. In cities in developing countries, the pursuit of rapid economic growth has ignored residents’ demand for LSs, thereby leading to a high demand for and short supply of these services. However, due to the lack of relevant research data, few studies have focused on the spatial mismatch in the supply and demand of LSs in urban areas. As typical representatives of multisource geographic data, social sensing data are readily available at various temporal and spatial scales, thus making social sensing data ideal for quantitative urban research. The objectives of this study are to use openly accessible datasets to explore the spatial pattern of the supply and demand of LSs in urban areas and then to depict the relationship between the supply and demand by using correlation analysis. Therefore, taking Beijing, China, as an example, the LS supply index (SI) and societal needs index (SNI) are proposed based on open data to reflect the supply and demand of LSs. The results show that the spatial distribution of the LS supply and demand in Beijing varies with a concentric pattern from the urban center to suburban areas. There is a strong correlation between the supply and demand of commercial and multifunctional services in Chaoyang, Fengtai, Haidian and Shijingshan, but there is no obvious correlation between the supply and demand of ecological and cultural services in Beijing. Especially in Dongcheng and Xicheng, there is no obvious correlation between the supply and demand of all services. The proposed approach provides an effective urban LS supply and demand evaluation method. In addition, the research results can provide a reference for the construction of “happy cities” in China.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9080466
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 5
    Online Resource
    Online Resource
    An Automated Processing Method for Agglomeration Areas
    MDPI AG ; 2018
    In:  ISPRS International Journal of Geo-Information Vol. 7, No. 6 ( 2018-05-29), p. 204-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 7, No. 6 ( 2018-05-29), p. 204-
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi7060204
    Language: English
    Publisher: MDPI AG
    Publication Date: 2018
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  • 6
    Online Resource
    Online Resource
    Bidirectional Gated Recurrent Unit Neural Network for Chinese Address Element Segmentation
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 11 ( 2020-10-26), p. 635-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 11 ( 2020-10-26), p. 635-
    Abstract: Chinese address element segmentation is a basic and key step in geocoding technology, and the segmentation results directly affect the accuracy and certainty of geocoding. However, due to the lack of obvious word boundaries in Chinese text, the grammatical and semantic features of Chinese text are complicated. Coupled with the diversity and complexity in Chinese address expressions, the segmentation of Chinese address elements is a substantial challenge. Therefore, this paper proposes a method of Chinese address element segmentation based on a bidirectional gated recurrent unit (Bi-GRU) neural network. This method uses the Bi-GRU neural network to generate tag features based on Chinese word segmentation and then uses the Viterbi algorithm to perform tag inference to achieve the segmentation of Chinese address elements. The neural network model is trained and verified based on the point of interest (POI) address data and partial directory data from the Baidu map of Beijing. The results show that the method is superior to previous neural network models in terms of segmentation performance and efficiency.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9110635
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
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  • 7
    Online Resource
    Online Resource
    RepDarkNet: A Multi-Branched Detector for Small-Target Detection in Remote Sensing Images
    MDPI AG ; 2022
    In:  ISPRS International Journal of Geo-Information Vol. 11, No. 3 ( 2022-02-22), p. 158-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 11, No. 3 ( 2022-02-22), p. 158-
    Abstract: Recent years have seen rapid progress in target-detection missions, whereas small targets, dense target distribution, and shadow occlusion continue to hinder progress in the detection of small targets, such as cars, in remote sensing images. To address this shortcoming, we propose herein a backbone feature-extraction network called “RepDarkNet” that adds several convolutional layers to CSPDarkNet53. RepDarkNet considerably improves the overall network accuracy with almost no increase in inference time. In addition, we propose a multi-scale cross-layer detector that significantly improves the capability of the network to detect small targets. Finally, a feature fusion network is proposed to further improve the performance of the algorithm in the AP@0.75 case. Experiments show that the proposed method dramatically improves detection accuracy, achieving AP = 75.53% for the Dior-vehicle dataset and mAP = 84.3% for the Dior dataset, both of which exceed the state-of-the-art level. Finally, we present a series of improvement strategies that justifies our improvement measures.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi11030158
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
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  • 8
    Online Resource
    Online Resource
    Rurality and Origin–Destination Trajectories of Medical School Application and Matriculation in the United States
    MDPI AG ; 2021
    In:  ISPRS International Journal of Geo-Information Vol. 10, No. 6 ( 2021-06-16), p. 417-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 10, No. 6 ( 2021-06-16), p. 417-
    Abstract: Physician shortages are more pronounced in rural than in urban areas. The geography of medical school application and matriculation could provide insights into geographic differences in physician availability. Using data from the Association of American Medical Colleges (AAMC), we conducted geospatial analyses, and developed origin–destination (O–D) trajectories and conceptual graphs to understand the root cause of rural physician shortages. Geographic disparities exist at a significant level in medical school applications in the US. The total number of medical school applications increased by 38% from 2001 to 2015, but the number had decreased by 2% in completely rural counties. Most counties with no medical school applicants were in rural areas (88%). Rurality had a significant negative association with the application rate and explained 15.3% of the variation at the county level. The number of medical school applications in a county was disproportional to the population by rurality. Applicants from completely rural counties (2% of the US population) represented less than 1% of the total medical school applications. Our results can inform recruitment strategies for new medical school students, elucidate location decisions of new medical schools, provide recommendations to close the rural–urban gap in medical school applications, and reduce physician shortages in rural areas.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi10060417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
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  • 9
    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
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  • 10
    Online Resource
    Online Resource
    Data-Driven Models Informed by Spatiotemporal Mobility Patterns for Understanding Infectious Disease Dynamics
    MDPI AG ; 2023
    In:  ISPRS International Journal of Geo-Information Vol. 12, No. 7 ( 2023-07-03), p. 266-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 12, No. 7 ( 2023-07-03), p. 266-
    Abstract: Data-driven approaches predict infectious disease dynamics by considering various factors that influence severity and transmission rates. However, these factors may not fully capture the dynamic nature of disease transmission, limiting prediction accuracy and consistency. Our proposed data-driven approach integrates spatiotemporal human mobility patterns from detailed point-of-interest clustering and population flow data. These patterns inform the creation of mobility-informed risk indices, which serve as auxiliary factors in data-driven models for detecting outbreaks and predicting prevalence trends. We evaluated our approach using real-world COVID-19 outbreaks in Beijing and Guangzhou, China. Incorporating the risk indices, our models successfully identified 87% (95% Confidence Interval: 83–90%) of affected subdistricts in Beijing and Guangzhou. These findings highlight the effectiveness of our approach in identifying high-risk areas for targeted disease containment. Our approach was also tested with COVID-19 prevalence data in the United States, which showed that including the risk indices reduced the mean absolute error and improved the R-squared value for predicting weekly case increases at the county level. It demonstrates applicability for spatiotemporal forecasting of widespread diseases, contributing to routine transmission surveillance. By leveraging comprehensive mobility data, we provide valuable insights to optimize control strategies for emerging infectious diseases and facilitate proactive measures against long-standing diseases.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi12070266
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
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