随着数字工具日益影响空间规划实践，理解不同数据源如何反映人类对城市环境的体验至关重要。街景影像（SVI）与公众参与地理信息系统（PPGIS）是两种捕捉场所感知的代表性方法，可支持城市规划决策，但二者之间的可比性仍缺乏深入研究。本研究探讨了芬兰赫尔辛基市基于街景影像的感知吸引力与通过全市范围PPGIS调查获取的居民实际体验之间的匹配程度。利用参与者评分的街景影像数据和语义图像分割技术，我们训练了一个机器学习模型，以视觉特征预测感知吸引力。将模型预测结果与PPGIS识别出的吸引或不吸引地点进行对比，并采用严格和适度两套标准计算一致性。研究发现，两类数据集之间仅存在部分一致性。在适度阈值下，吸引性地点的一致性为67%，非吸引性地点为77%；而在严格阈值下，一致性分别降至27%和29%。通过分析包括噪声、交通、人口密度及土地利用在内的多种情境变量，我们发现非视觉因素显著导致了不一致。该模型未能涵盖活动水平和环境压力等影响感知但无法在图像中体现的体验维度。结果表明，尽管街景影像可作为城市感知的可扩展且可视化的代理指标，但无法完全替代PPGIS所捕捉的丰富体验。我们认为，两种方法各有价值，但功能不同，因此需要更整合的方法以全面捕捉城市感知。

As digital tools increasingly shape spatial planning practices, understanding how different data sources reflect human experiences of urban environments is essential. Street View Imagery (SVI) and Public Participation GIS (PPGIS) represent two prominent approaches for capturing place-based perceptions that can support urban planning decisions, yet their comparability remains underexplored. This study investigates the alignment between SVI-based perceived attractiveness and residents' reported experiences gathered via a city-wide PPGIS survey in Helsinki, Finland. Using participant-rated SVI data and semantic image segmentation, we trained a machine learning model to predict perceived attractiveness based on visual features. We compared these predictions to PPGIS-identified locations marked as attractive or unattractive, calculating agreement using two sets of strict and moderate criteria. Our findings reveal only partial alignment between the two datasets. While agreement (with a moderate threshold) reached 67% for attractive and 77% for unattractive places, agreement (with a strict threshold) dropped to 27% and 29%, respectively. By analysing a range of contextual variables, including noise, traffic, population presence, and land use, we found that non-visual cues significantly contributed to mismatches. The model failed to account for experiential dimensions such as activity levels and environmental stressors that shape perceptions but are not visible in images. These results suggest that while SVI offers a scalable and visual proxy for urban perception, it cannot fully substitute the experiential richness captured through PPGIS. We argue that both methods are valuable but serve different purposes; therefore, a more integrated approach is needed to holistically capture how people perceive urban environments.