一个新的框架整合了GeoAI和人类的感知,以估计中国武汉市的可行性
Xue Yang1,2, Tianyu Li3, Yanjia Cao4
1School of Geography and Information Engineering, China University of Geosciences, Wuhan, 430074, China.
Scientific reports
|July 14, 2025
概括
通过先进的图像分析和人类感知来衡量的增强的城市可行性,揭示了武汉地区的显著差异. 这些可行性不平等与社会经济地位相关,并可能加剧健康差异.
科学领域:
- 城市规划 城市规划
- 环境健康 环境健康
- 计算机视觉 计算机视觉
背景情况:
- 传统的地理信息系统 (GIS) 方法和图像细分技术在微观水平上评估城市可行性方面存在局限性.
- 现有的方法难以捕捉细微的街道环境特征和人类的感知,影响可行性评估的准确性.
- 改善步行环境与增加体力活动和降低慢性疾病和精神健康障碍的风险有关.
研究的目的:
- 开发一种新的,高精度的框架来评估城市步行能力.
- 将先进的图像细分与主观的人类感知测量集成在一起.
- 分析城市步行能力的空间分布和社会经济相关性.
主要方法:
- 使用高精度图像细分和对绿色,开放,拥挤和安全等指标的主观测量,开发了一个层次化的可行性评估框架.
- 使用基于120个志愿者问卷的权方法量化的指标.
- 创建了细节增强高分辨率网络 (DS-HRNet),这是一个深度学习模型,与现有模型相比,将街景细分提高了15%.
- 使用DS-HRNet模型分析了武汉市中心的113,900张街景图像.
主要成果:
- 该研究揭示了武汉城市步行能力的显著空间异质性,邻近地区之间存在显著差异,特别是在商业区.
- 研究发现,在社会经济地位较高,非当地居民比例较低的地区,步行能力更高.
- 先进的DS-HRNet模型在街景细分方面显示出15%的性能改善.
结论:
- 开发的框架和DS-HRNet模型提供了更准确和详细的城市可行性评估.
- 武汉市内存在显著的可行性不平等,与社会经济因素相关.
- 这些可行性差异可能通过影响身体活动和社会互动模式,导致健康不平等.
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