城市场景多源低空风险量化与航路生成

doi:10.11947/j.AGCS.2026.20250371

测绘学报 ›› 2026, Vol. 55 ›› Issue (7): 1306-1320.doi: 10.11947/j.AGCS.2026.20250371

• 地图学与地理信息 • 上一篇

城市场景多源低空风险量化与航路生成

谭清华¹(), 齐恒¹, 施泓羽¹, 唐炉亮¹^,²(), 阚子涵³^,⁴, 杨红¹, 孙乐乐¹, 刘亚非⁵^,⁶, 顾正雄⁵^,⁶

^1.武汉大学测绘遥感信息工程全国重点实验室，湖北　武汉　430079
^2.湖南科技大学地理空间信息技术国家地方联合工程实验室，湖南　湘潭　411100
^3.香港中文大学地理与资源管理系，香港　999077
^4.香港中文大学太空与地球信息科学研究所，香港　999077
^5.云南省地矿测绘院有限公司，云南　昆明　650051
^6.云南省自然资源智能监测与时空大数据治理重点实验室，云南　昆明　650051

收稿日期:2025-09-25 修回日期:2026-07-10 发布日期:2026-08-18
通讯作者: 唐炉亮 E-mail:tanqinghua@whu.edu.cn;tll@whu.edu.cn
作者简介:谭清华（2004—），男，硕士生，研究方向为低空风险评估与航路生成。 E-mail：tanqinghua@whu.edu.cn
基金资助:
国家重点研发计划(2024YFB3908603);国家自然科学基金(42301475);云南省自然资源智能监测与时空大数据治理重点实验室开放课题(202449CE340023);广东省基础与应用基础研究基金(2024A1515012270);湖南省科技创新计划项目(2022RC4039)

Multi-source low-altitude risk quantification and route generation in urban environments

Qinghua Tan¹(), Heng Qi¹, Hongyu Shi¹, Luliang Tang¹^,²(), Zihan Kan³^,⁴, Hong Yang¹, Lele Sun¹, Yafei Liu⁵^,⁶, Zhengxiong Gu⁵^,⁶

^1.State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079
^2.National-Local Joint Engineering Laboratory of Geo-Spatial Information Technology, Hunan University of Science and Technology, Xiangtan 411100
^3.Department of Geography and Resource Management, The Chinese University of Hong Kong, Hong Kong 999077
^4.Institute of Space and Earth Information Science, The Chinese University of Hong Kong, Hong Kong 999077
^5.Yunnan Institute of Geology and Mineral Surveying and Mapping Co., Ltd., Kunming 650051
^6.Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources, Kunming 650051

Received:2025-09-25 Revised:2026-07-10 Published:2026-08-18
Contact: Luliang Tang E-mail:tanqinghua@whu.edu.cn;tll@whu.edu.cn
About author:Tan Qinghua (2004—), male, postgraduate, majors in low-altitude risk quantification and route generation. E-mail: tanqinghua@whu.edu.cn
Supported by:
The National Key Research and Development Program of China(2024YFB3908603);The National Natural Science Foundation of China(42301475);The Open Fund Program of Yunnan Key Laboratory of Intelligent Monitoring and Spatiotemporal Big Data Governance of Natural Resources(202449CE340023);The Basic and Applied Basic Research Fund of Guangdong Province(2024A1515012270);The Science and Technology Innovation Program of Hunan Province(2022RC4039)

摘要/Abstract

摘要：

低空开发与利用正逐渐成为推动低空经济发展的战略支撑，但低空飞行器的大规模涌入使城市上空面临前所未有的安全挑战。如何科学、全面、精准量化复杂城市低空环境的风险分布，生成安全、高效的低空航路已成为关键科学问题。现有研究忽视城市低空与地面空间的多维耦合风险，航路生成算法局限于局部搜索，难以实现风险准确量化与最优航路生成。为此，本文提出以地面风险为核心约束的低空风险量化方法与航路智能生成模型。首先，融合人口密度、建筑密度与用地类型3类数据，提出低空风险因子量化与组合赋权方法，构建了高-中-低多级低空风险分布地图。然后，提出基于自适应粒子群优化算法的航路智能生成模型（APSO-AR），通过自适应航路节点构建、风险感知采样与间隔惩罚机制，实现航路对地面高风险区域的全局感知与规避。最后，以武汉市为研究区域进行航路生成试验，结果表明，相较于蚁群算法、遗传算法、risk A^*算法及传统粒子群算法，APSO-AR生成的航路长度平均降低了12.0%，风险度平均降低了30.7%，代码运行时长平均降低了77.4%，在航程效率，风险规避与空间合理性之间取得较好平衡。本文提供了“风险量化—航路生成”的技术路径，为城市低空风险地图构建与航路规划提供方法支撑。

关键词: 风险量化, 航路生成, 粒子群算法, 低空经济

Abstract:

The development and utilization of low-altitude airspace are gradually becoming a strategic pillar for advancing the low-altitude economy. However, the massive influx of low-altitude aircraft poses unprecedented safety challenges over urban areas. How to scientifically, comprehensively, and accurately quantify the risk distribution in complex urban low-altitude environments, and subsequently generate safe and efficient air routes, has become a critical scientific problem. Existing research often overlooks the multi-dimensional coupled risks between urban low-altitude and ground space, and air route generation algorithms are typically confined to local search, making it difficult to achieve accurate risk quantification and globally optimal route generation. To this end, this paper proposes a low-altitude risk quantification method and an intelligent route generation model constrained primarily by ground risk. Firstly, by fusing three types of data—population density, building density, and land use type—we propose a low-altitude risk factor quantification and combined weighting method to construct a high-medium-low multi-level low-altitude risk distribution map. Secondly, an intelligent air route generation model based on adaptive particle swarm optimization (APSO-AR) is proposed. By incorporating an adaptive air route node construction, a risk perception sampling mechanism, and an interval penalty mechanism, the model achieves global risk perception and optimal route generation for low-altitude aircraft within complex urban scenes. Finally, air route generation experiments were conducted using Wuhan city as the research area. The results demonstrate that, compared to ant colony optimization (ACO), genetic algorithm (GA), risk A^* algorithm, and the traditional particle swarm optimization (PSO), the air routes generated by APSO-AR show an average reduction of 12.0% in route length and an average reduction of 30.7% in risk level, while the average code runtime is reduced by 77.4%. The model simultaneously ensures route optimality, risk minimization, and global rationality. This paper provides a new technical path for “risk quantification—route generation”, providing methodological support for urban low-altitude risk map construction and route planning.

Key words: risk quantification, route generation, particle swarm optimization algorithm, low-altitude economy

中图分类号:

P237

谭清华, 齐恒, 施泓羽, 唐炉亮, 阚子涵, 杨红, 孙乐乐, 刘亚非, 顾正雄. 城市场景多源低空风险量化与航路生成[J]. 测绘学报, 2026, 55(7): 1306-1320.

Qinghua Tan, Heng Qi, Hongyu Shi, Luliang Tang, Zihan Kan, Hong Yang, Lele Sun, Yafei Liu, Zhengxiong Gu. Multi-source low-altitude risk quantification and route generation in urban environments[J]. Acta Geodaetica et Cartographica Sinica, 2026, 55(7): 1306-1320.

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链接本文: http://xb.chinasmp.com/CN/10.11947/j.AGCS.2026.20250371

http://xb.chinasmp.com/CN/Y2026/V55/I7/1306

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风险度	风险区域	风险类型	用地分类
0.85	建筑	硬性聚集风险	H-建设用地、R-居住用地、A-公共管理与公共服务用地、B-商业服务业设施用地、M-工业用地、W-物流仓储用地
0.50	道路	动态损失风险	S-道路与交通设施用地
0.25	植被	柔性缓冲风险	G-绿地与广场用地、E-非建设用地
0.10	水域	柔性缓冲风险	E-非建设用地（E1水域）

风险因子	特征向量	权重值/（%）	最大特征根	CR值
人口密度	1.471	49.048	3.054	0.027
建筑密度	0.593	19.762
土地覆盖	0.936	31.190

风险因子	AHP权重	熵值法权重	组合权重
人口	0.490	0.395	0.44
建筑	0.198	0.418	0.31
土地	0.312	0.188	0.25

风险级别	聚类中心	风险值下界	风险值上界
低风险	0.024 0	0.000 0	0.184 1
中风险	0.344 1	0.184 1	0.446 1
高风险	0.548 1	0.446 1	1.000 0

航路名称	起点		终点		APSO-AR生成航路情况
航路名称	像素坐标	地理坐标	像素坐标	地理坐标	航路综合代价	航路长度/m	风险度
航路a	（85，7）	30.546°N，114.298°E	（18，175）	30.519°N，114.382°E	96.94	9 767.02	0.084
航路b	（46，0）	30.528°N，114.294°E	（0，136）	30.508°N，114.363°E	81.00	7 336.45	0.186
航路c	（147，36）	30.576°N，114.312°E	（58，182）	30.533°N，114.389°E	82.00	8 647.15	0.044
航路d	（0，93）	30.508°N，114.341°E	（154，142）	30.577°N，114.367°E	85.44	8 379.25	0.109
航路e	（153，116）	30.576°N，114.354°E	（65，62）	30.536°N，114.325°E	60.92	5 786.55	0.139
航路f	（0，7）	30.508°N，114.297°E	（70，48）	30.539°N，114.319°E	42.65	4 070.69	0.134

城市场景多源低空风险量化与航路生成

Multi-source low-altitude risk quantification and route generation in urban environments

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模型	航路综合代价	航路长度/m	航路风险度	收敛代数	运行时长/s	风险等级
risk A^*	92.414	8247	0.206	—	0.227	中
PSO	85.274	7895	0.167	207	63.299	低
GA	91.842	8228	0.189	540	38.447	中
ACO	96.297	8882	0.173	238	245.386	低
APSO-AR	75.593	7318	0.128	112	19.605	低

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