居民对城市街道景观的主观感知是影响公共健康、主动出行及社会福祉的重要因素。街景图像（SVI）已成为评估此类感知属性的常用数据源，但其覆盖不均与更新不规律限制了大范围测量。本文提出CVLNet——一种跨视角学习网络，该网络无需在推理阶段使用SVI，即可利用AlphaEarth嵌入与多源城市上下文数据预测街道层级的感知特征。CVLNet采用任务自适应门控机制，联合建模五个感知维度，并以预训练SVI-Percept模型生成的标签作为监督真值。所提方法在四个东南亚城市——新加坡、吉隆坡、雅加达和马尼拉——开展评估。CVLNet在路段层级上取得中位数校正决定系数（Adjusted $R^{2}$）为0.76，且在全部五个感知维度上持续优于基线模型，性能提升幅度为5.9%–11.3%。消融实验表明，AlphaEarth特征与城市上下文特征提供互补信息。我们进一步为全部四座城市生成覆盖全域道路网络的街道景观感知地图，涵盖五个主观感知维度，将感知估计范围从现有SVI直接覆盖的13%–31%道路扩展至各城市完整路网。结合WorldPop格网化人口数据，我们采用赤字帕尔玛比率（Deficit Palma Ratio）量化不同人口密度、人口统计学及土地利用群体间的暴露不平等。结果表明，遥感技术可作为SVI的可扩展替代方案，用于全域街道景观感知制图，从而支持对城市环境不平等更全面的评估。

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.