The green area factor model is a crucial tool for conserving and creating urban greenery and ecosystem services within neighborhood land. This model serves as a valuable index, streamlining the planning, assessment, and comparison of local-scale green infrastructures. However, conventional on-site measurements of the green area factor are resource intensive. In response, this study pioneers a computational approach that integrates ecological and social dimensions to estimate the green area factor. Employing satellite remote sensing and advanced deep learning techniques, the methodology utilizes satellite orthophotos of urban areas subjected to semantic segmentation, identifying and categorizing diverse green elements. Ground truths are established through on-site measurements of green area factors and satellite orthophotos from benchmarking sites in Örebro, Sweden. Results reveal an 82.0% average F1-score for semantic segmentations, signifying a favourable correlation between computationally estimated and measured green area factors. The proposed methodology is potential for adapting to various urban settings. In essence, this research introduces a promising, cost-effective solution for assessing urban greenness, particularly beneficial for urban administrators and planners aiming for insightful and comprehensive green strategies in city planning.

中文翻译：

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基于深度学习的卫星图像城市绿地指数自动估算：案例研究


绿地因子模型是在邻里土地内保护和创造城市绿化和生态系统服务的重要工具。该模型作为一个有价值的指标，简化了地方规模绿色基础设施的规划、评估和比较。然而，绿地面积系数的传统现场测量是资源密集型的。为此，本研究开创了一种计算方法，整合生态和社会维度来估计绿地系数。该方法采用卫星遥感和先进的深度学习技术，利用城市地区的卫星正射影像进行语义分割，识别和分类不同的绿色元素。地面实况是通过对瑞典厄勒布鲁基准点的绿地系数和卫星正射影像进行现场测量来确定的。结果显示语义分割的平均 F1 分数为 82.0%，这表明计算估计的绿地面积因子与测量的绿地面积因子之间存在良好的相关性。所提出的方法有可能适应各种城市环境。从本质上讲，这项研究介绍了一种有前景、具有成本效益的城市绿化评估解决方案，特别有利于城市管理者和规划者在城市规划中制定富有洞察力和全面的绿色战略。