跨视角城市感知：通过AlphaEarth嵌入与城市上下文绘制主观街景感知图

中文摘要 AI 辅助

居民对城市街景的感知是影响公共健康、主动出行和社会福祉的重要因素。街景图像（SVI）已成为评估这些感知特质的广泛使用数据源，但其覆盖不均和更新不规律限制了大规模测量。本文提出CVLNet，一种跨视角学习网络，该网络可在推理时无需SVI，仅通过AlphaEarth嵌入和多源城市上下文数据预测街道级感知。CVLNet应用任务自适应门控来联合建模五个感知维度，使用预训练SVI-Percept模型的标签作为真实值。该方法在四个东南亚城市（新加坡、吉隆坡、雅加达和马尼拉）进行评估，CVLNet在道路段层面实现0.76的中位数调整R²，且在五个感知维度上始终优于基线模型，提升幅度为5.9%至11.3%。 ablation实验表明，AlphaEarth特征和城市上下文特征提供互补信息。我们进一步生成四个城市全部五个主观感知维度的全市道路级街景感知图，将感知估计从可用SVI直接覆盖的13%至31%的道路网络扩展到每个城市的完整道路网络。将这些图与WorldPop网格人口数据结合，我们使用Deficit Palma比率量化了人口密度、人口统计和土地利用群体的暴露不平等。这些结果表明，遥感可作为SVI的可扩展替代方案，用于全市街景感知制图，从而实现对城市环境不平等的更全面评估。

英文摘要

Residents' perception of the urban streetscape is an important factor in public health, active mobility, and social wellbeing. Street view imagery (SVI) has emerged as a widely used data source for assessing these perceptual qualities, yet its uneven coverage and irregular updating limit large-scale measurement. Here, we present CVLNet, a Cross-View Learning Network that predicts street-level perception from AlphaEarth embeddings and multi-source urban contextual data without requiring SVI at inference. CVLNet applies per-task adaptive gating to jointly model five perceptual dimensions, using labels from the pretrained SVI-Percept model as ground truth. The proposed method is evaluated across four Southeast Asian cities: Singapore, Kuala Lumpur, Jakarta, and Manila. CVLNet achieves a median road-segment-level Adjusted $R^{2}$ of 0.76 and consistently outperforms the baseline models, with gains ranging from 5.9--11.3% across the five perceptual dimensions. Ablation experiments show that AlphaEarth features and urban contextual features contribute complementary information. We further produce citywide road-level streetscape perception maps for five subjective perceptual dimensions across all four cities, extending perception estimation from the 13--31% of the road network directly covered by available SVI to the complete road network of each city. Integrating these maps with WorldPop gridded population data, we quantify exposure inequality across population-density, demographic, and land-use groups using the Deficit Palma Ratio. These results demonstrate that remote sensing can serve as a scalable alternative to SVI for citywide streetscape perception mapping, enabling a more comprehensive assessment of urban environmental inequality.

发表机构

• School of Geospatial Engineering and Science, Sun Yat-sen University（中山大学地理空间工程学院）
• Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)（南方海洋科学与工程广东省实验室（珠海））
• Key Laboratory of Comprehensive Observation of Polar Environment (Sun Yat-sen University), Ministry of Education（教育部极地环境综合观测重点实验室（中山大学））

机构由 AI 辅助整理，请以论文原文为准。