Big data from photo-sharing platforms offer unique opportunities for the study of human-nature interactions and landscape planning. Research increasingly relies on computer vision in artificial intelligence to identify elements of interest in photographs and user preferences and sentiment towards them. Studies largely rely on pre-trained models from one of several available cloud-based, commercial image recognition services, but the extent to which findings depend on the implemented technology has not yet been explored. Here, we analyze ∼ 10,000 outdoor photographs retrieved from three social media platforms and geolocated within green and blue spaces in Haifa (Israel) by means of machine tags from three popular cloud-based services. We find that clustering of the 45 investigated sites based on common characteristics of the photographs is considerably affected by the image recognition service chosen, especially for sites with limited data points (<80 photographs). Moreover, after associating the individual tags to specific aspects of the outdoor experience, we find substantial differences in the identification and ranking of outdoor recreational activities, characterization of the local biophysical environment (e.g., wildlife and vegetation), and feelings associated with the photographs. With no image recognition service clearly outperforming the others in all evaluation criteria, we argue that the optimal choice of image recognition service to rely on likely depends on the intended final application. Time and resource permitting, future studies should consider combining information from multiple sources for a characterization that is more nuanced and less prone to be affected by the idiosyncrasies of the individual technologies.

来自照片共享平台的大数据为研究人与自然相互作用和景观规划提供了独特的机会。研究越来越依赖人工智能中的计算机视觉来识别照片中感兴趣的元素以及用户对它们的偏好和情绪。研究在很大程度上依赖于来自几种可用的基于云的商业图像识别服务之一的预训练模型，但尚未探索发现在多大程度上取决于实施的技术。在这里，我们通过来自三个流行的基于云的服务的机器标签分析了从三个社交媒体平台检索到的约 10,000 张户外照片，并在海法（以色列）的绿色和蓝色空间内进行了地理定位。我们发现，基于照片共同特征的 45 个调查站点的聚类受到所选图像识别服务的显着影响，尤其是对于数据点有限（<80 张照片）的站点。此外，在将各个标签与户外体验的特定方面相关联后，我们发现户外休闲活动的识别和排名、当地生物物理环境（例如野生动物和植被）的特征以及与照片相关的感觉方面存在显着差异。由于没有任何图像识别服务在所有评估标准中明显优于其他服务，我们认为依赖的图像识别服务的最佳选择可能取决于预期的最终应用。在时间和资源允许的情况下，