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  • Li, Yi  (2)
  • Cartography and geographic base data  (2)
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  • Cartography and geographic base data  (2)
  • 1
    Online Resource
    Online Resource
    Urban Green Plastic Cover Mapping Based on VHR Remote Sensing Images and a Deep Semi-Supervised Learning Framework
    MDPI AG ; 2020
    In:  ISPRS International Journal of Geo-Information Vol. 9, No. 9 ( 2020-09-02), p. 527-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 9, No. 9 ( 2020-09-02), p. 527-
    Abstract: With the rapid process of both urban sprawl and urban renewal, large numbers of old buildings have been demolished in China, leading to wide spread construction sites, which could cause severe dust contamination. To alleviate the accompanied dust pollution, green plastic mulch has been widely used by local governments of China. Therefore, timely and accurate mapping of urban green plastic covered regions is of great significance to both urban environmental management and the understanding of urban growth status. However, the complex spatial patterns of the urban landscape make it challenging to accurately identify these areas of green plastic cover. To tackle this issue, we propose a deep semi-supervised learning framework for green plastic cover mapping using very high resolution (VHR) remote sensing imagery. Specifically, a multi-scale deformable convolution neural network (CNN) was exploited to learn representative and discriminative features under complex urban landscapes. Afterwards, a semi-supervised learning strategy was proposed to integrate the limited labeled data and massive unlabeled data for model co-training. Experimental results indicate that the proposed method could accurately identify green plastic-covered regions in Jinan with an overall accuracy (OA) of 91.63%. An ablation study indicated that, compared with supervised learning, the semi-supervised learning strategy in this study could increase the OA by 6.38%. Moreover, the multi-scale deformable CNN outperforms several classic CNN models in the computer vision field. The proposed method is the first attempt to map urban green plastic-covered regions based on deep learning, which could serve as a baseline and useful reference for future research.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi9090527
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2655790-3
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    Online Resource
  • 2
    Online Resource
    Online Resource
    A Virtual Geographic Environment for Debris Flow Risk Analysis in Residential Areas
    MDPI AG ; 2017
    In:  ISPRS International Journal of Geo-Information Vol. 6, No. 11 ( 2017-11-22), p. 377-
    Details
    In: ISPRS International Journal of Geo-Information, MDPI AG, Vol. 6, No. 11 ( 2017-11-22), p. 377-
    Abstract: Emergency risk assessment of debris flows in residential areas is of great significance for disaster prevention and reduction, but the assessment has disadvantages, such as a low numerical simulation efficiency and poor capabilities of risk assessment and geographic knowledge sharing. Thus, this paper focuses on the construction of a VGE (virtual geographic environment) system that provides an efficient tool to support the rapid risk analysis of debris flow disasters. The numerical simulation, risk analysis, and 3D (three-dimensional) dynamic visualization of debris flow disasters were tightly integrated into the VGE system. Key technologies, including quantitative risk assessment, multiscale parallel optimization, and visual representation of disaster information, were discussed in detail. The Qipan gully in Wenchuan County, Sichuan Province, China, was selected as the case area, and a prototype system was developed. According to the multiscale parallel optimization experiments, a suitable scale was chosen for the numerical simulation of debris flow disasters. The computational efficiency of one simulation step was 5 ms (milliseconds), and the rendering efficiency was approximately 40 fps (frames per second). Information about the risk area, risk population, and risk roads under different conditions can be quickly obtained. The experimental results show that our approach can support real-time interactive analyses and can be used to share and publish geographic knowledge.
    Type of Medium: Online Resource
    ISSN: 2220-9964
    URL: Article
    DOI: 10.3390/ijgi6110377
    Language: English
    Publisher: MDPI AG
    Publication Date: 2017
    detail.hit.zdb_id: 2655790-3
    Location Call Number Limitation Availability
    BibTip Others were also interested in ...
    Online Resource
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