Monitoring and Analyzing the Effectiveness of the Effective Refuge Area of Emergency Shelters by Using Remote Sensing: A Case Study of Beijing’s Fifth Ring Road

The effective refuge area is a key indicator in the study of emergency shelters. Accurately extracting the effective refuge area and analyzing the effectiveness of emergency shelters are of great significance for site selection, spatial distribution, and the evaluation of suitability. Beijing is one of only three capitals in the world located in a high-seismic-intensity zone of magnitude 8. The fast and accurate monitoring of effective refuge areas and an analysis of the effectiveness of emergency shelters are conducive to evacuation planning and disaster prevention and mitigation, and they promote the construction of a resilient city. However, the extraction of effective refuge areas in existing studies is not only a time-consuming and labor-intensive task but also has accuracy and efficiency problems, resulting in less precise validity analyses. In this paper, a remote sensing monitoring technology system for the effective refuge areas of emergency shelters is proposed based on multi-source data. Different methods were used to extract various land features, such as buildings and collapsed areas, water, dense areas of understory vegetation, and steep slope areas that cannot be evacuated, to obtain the effective refuge area at a detailed scale, in combination with the service radius of emergency shelters, the population distribution, and the actual road network, the criteria for effectiveness analysis were established for the effective open space ratio, capacity, per capita accessible effective refuge area, and population allocation gap. Taking the area within the Fifth Ring Road of Beijing as an example, the effectiveness of emergency shelters was analyzed at both the whole scale and a local scale. The results show that the effective refuge areas of different emergency shelters in Beijing vary significantly, with the smallest effective refuge area being located in Rings 2–3 and the largest one being located in Rings 4–5; between different regions, there are differences in the effectiveness. This study provides a feasible method for the fast, accurate, and detailed extraction of the effective refuge areas of emergency shelters and also provides a reference for emergency planning for disaster prevention and mitigation.

[1]  Jing Chen,et al.  Double Branch Parallel Network for Segmentation of Buildings and Waters in Remote Sensing Images , 2023, Remote. Sens..

[2]  L. Weng,et al.  Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image , 2023, Sustainability.

[3]  Liang Zhou,et al.  Fine Classification of UAV Urban Nighttime Light Images Based on Object-Oriented Approach , 2023, Sensors.

[4]  S. Wei,et al.  Using object-oriented coupled deep learning approach for typical object inspection of transmission channel , 2023, Int. J. Appl. Earth Obs. Geoinformation.

[5]  Gu Zhong,et al.  Multi-objective optimization approach of shelter location with maximum equity: an empirical study in Xin Jiekou district of Nanjing, China , 2023, Geomatics, Natural Hazards and Risk.

[6]  B. M. Sopha,et al.  Geographic Information System Based Suitable Temporary Shelter Location for Mount Merapi Eruption , 2023, Sustainability.

[7]  Yiying Wang,et al.  An Emergency Shelter Location Model Based on the Sense of Security and the Reliability Level , 2023, Journal of Systems Science and Systems Engineering.

[8]  S. Du,et al.  Water extraction from optical high-resolution remote sensing imagery: a multi-scale feature extraction network with contrastive learning , 2023, GIScience & Remote Sensing.

[9]  Dongzhu Chu,et al.  Resilience of Public Open Spaces to Earthquakes: A Case Study of Chongqing, China , 2023, Sustainability.

[10]  Wei Wang,et al.  An optimal design method of emergency evacuation space in the high-density community after earthquake based on evacuation simulation , 2023, Natural Hazards.

[11]  J. Yee,et al.  Emergency Shelter Geospatial Location Optimization for Flood Disaster Condition: A Review , 2022, Sustainability.

[12]  Yong Fan,et al.  A Multi-Indicator Evaluation Method for Spatial Distribution of Urban Emergency Shelters , 2022, Remote. Sens..

[13]  Shixin Wang,et al.  EfficientUNet+: A Building Extraction Method for Emergency Shelters Based on Deep Learning , 2022, Remote. Sens..

[14]  M. Janalipour,et al.  Selection of shelters after earthquake using probabilistic seismic aftershock hazard analysis and remote sensing , 2022, Natural Hazards.

[15]  Padmavathi Kora,et al.  EfficientNet-B0 Based Monocular Dense-Depth Map Estimation , 2021, Traitement du Signal.

[16]  Yuanzhi Zhang,et al.  A GIS-Based System for Spatial-Temporal Availability Evaluation of the Open Spaces Used as Emergency Shelters: The Case of Victoria, British Columbia, Canada , 2021, ISPRS Int. J. Geo Inf..

[17]  Zhiyu Xu,et al.  A Novel Intelligent Classification Method for Urban Green Space Based on High-Resolution Remote Sensing Images , 2020, Remote. Sens..

[18]  O. Uzun,et al.  An assessment on size and site selection of emergency assembly points and temporary shelter areas in Düzce , 2020, Natural Hazards.

[19]  Jia Yu,et al.  Supply–Demand Analysis of Urban Emergency Shelters Based on Spatiotemporal Population Estimation , 2020, International Journal of Disaster Risk Science.

[20]  Meng Lu,et al.  Toward Automatic Building Footprint Delineation From Aerial Images Using CNN and Regularization , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Yao Fang,et al.  Assessing Emergency Shelter Demand Using POI Data and Evacuation Simulation , 2020, ISPRS Int. J. Geo Inf..

[22]  Meng Lu,et al.  Fully Convolutional Networks for Multisource Building Extraction From an Open Aerial and Satellite Imagery Data Set , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Rui Liu,et al.  Integrating multi-agent evacuation simulation and multi-criteria evaluation for spatial allocation of urban emergency shelters , 2018, Int. J. Geogr. Inf. Sci..

[24]  Yan Sun,et al.  Planning Emergency Shelters for Urban Disaster Resilience: An Integrated Location-Allocation Modeling Approach , 2017 .

[25]  Yakoub Bazi,et al.  Classification of Remote Sensing Images Using EfficientNet-B3 CNN Model With Attention , 2021, IEEE Access.

[26]  Xiaolu Gao,et al.  Evaluation method and empirical study on service quality of seismic emergency shelters , 2014 .

[27]  Liu Ta Reasonability of Spatial Distribution for Urban Emergency Shelter , 2012 .

[28]  Zhang Xiao-wei Analysis of the ratio of arbor to shrub of several types of green space in Beijing , 2010 .

[29]  Z. Ya Planting Design of Beijing Olympic Forest Park , 2006 .