A Rapid Extraction Method of Summit Point for Emergency Communication System Siting Based on Enhanced Convolutional Neural Network

ZHEN Jie; QI Junjie; WANG Yong; XU Shenghua; LIU Jiping

doi:10.13203/j.whugis20250097

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A Rapid Extraction Method of Summit Point for Emergency Communication System Siting Based on Enhanced Convolutional Neural Network

Review | 更新时间：2026-11-05

- A Rapid Extraction Method of Summit Point for Emergency Communication System Siting Based on Enhanced Convolutional Neural Network
- Geomatics and Information Science of Wuhan University Vol. 50, Issue 6, Pages: 1042-1053(2025)
- 作者机构：
  
  1.中国测绘科学研究院，北京，100036
  2.辽宁工程技术大学测绘与地理科学学院,辽宁阜新,123000
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20250097
  CLC： TP208
- Received：10 March 2025，
  
  Published：05 June 2025
- 稿件说明：
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甄杰,祁俊杰,王勇，等.面向应急通信选址的增强卷积神经网络山顶点快速提取方法[J].武汉大学学报（信息科学版）,2025,50(6):1042-1053.

ZHEN Jie,QI Junjie,WANG Yong,et al.A Rapid Extraction Method of Summit Point for Emergency Communication System Siting Based on Enhanced Convolutional Neural Network[J].Geomatics and Information Science of Wuhan University,2025,50(6):1042-1053.
甄杰,祁俊杰,王勇，等.面向应急通信选址的增强卷积神经网络山顶点快速提取方法[J].武汉大学学报（信息科学版）,2025,50(6):1042-1053. DOI：10.13203/j.whugis20250097

ZHEN Jie,QI Junjie,WANG Yong,et al.A Rapid Extraction Method of Summit Point for Emergency Communication System Siting Based on Enhanced Convolutional Neural Network[J].Geomatics and Information Science of Wuhan University,2025,50(6):1042-1053. DOI：10.13203/j.whugis20250097

摘要

为解决重特大自然灾害发生时应急通信网络快速构建及优化部署问题，通过数字高程模型与等高线、坡度、坡向融合的方法制作山顶点特征数据集，提升山顶区域特征信息，给出应急通信山顶区域筛选条件，提出适应多尺度山顶区域定位的增强更快速区域卷积神经网络算法模型，并针对大区域进行拆分、检测再合并，提高山顶区域识别效果，运用局部极大值方法实现了应急通信山顶点的精确化提取。所提改进算法模型与其他模型对比，均值平均精度达到94.92%，山顶点提取的准确度达到94.2%，以此为通信节点的信号有效覆盖率达到80.56%，可视率达到77.43%，均优于正反地形法和邻域分析法。

Abstract

Objectives

It is urgent to solve the problem of rapid construction and optimal deployment of emergency communication network when large-scale natural disaster occurs.

Methods

First

the peak points feature dataset is built up by fusing the digital elevation model with contour

slope

and slope aspect to improve the feature information of the peak area. Then

the screening conditions of the peak area for emergency communication are given and an enhanced faster region-based convolutional neural network (Faster R-CNN) is proposed to adapted to the multi-scale positioning of the peak area. Finally

we split

detect

and then merge the large area to improve the recognition effect of the peak area

and use the local maximum value method to achieve the precise extraction of peak points for emergency communication.

Results

Comparing the proposed improved algorithm with other algorithms

the mean average precision (mAP) reaches 94.92%

the accuracy of peak points extraction reaches 94.2%

the effective coverage rate of the communication node reaches 80.56%

and the visibility rate reaches 77.43%.

Conclusions

The proposed method is effective in identifying small targets

more in line with the terrain characteristics than other methods

and meets the actual communication needs in terms of communication to achieve a larger coverage. The extraction results of the proposed method can be used as a reference for the deployment of emergency communication nodes to help complete the optimization of the deployment of communication nodes for emergency rescue

and to protect the regional communication smooth.

关键词

Keywords

references

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