Most previous studies of homegardens have used labor-intensive boots-on-the-ground plant surveys, owner questionnaires, and interviews, limiting them to at most a few hundred homegardens. We show that automated analysis of publicly available imagery can enable surveys of much greater scale that can augment these traditional data sources. Specifically, we demonstrate the feasibility of using the high-resolution street-level photographs in Google Street View and an object-detection network (RetinaNet) to create a large-scale high-resolution survey of the prevalence of at least six plant species widely grown in road-facing homegardens in Thailand. Our research team examined 4000 images facing perpendicular to the street and located within 10 m of a homestead, and manually outlined all perceived instances of eleven common plant species. A neural network trained on these tagged images was used to detect instances of these species in approximately 150,000 images constituting views of roughly one in every ten homesteads in five provinces of northern Thailand. The results for six of the plant species were visualized as heatmaps of both the average number of target species detected in each image and individual species prevalence, with spatial averaging performed at scales of 500 m and 2.5 km. Urban-rural contrasts in the average number of target species in each image are quantified, and large variations are observed even among neighboring villages. Spatial heterogeneity is seen to be more pronounced for banana and coconut than for other species. Star gooseberry and papaya are more frequently present immediately outside of towns while dracaena and mango persist into the cores of towns.

以前的大多数家庭花园研究都使用了劳动密集型的地面工厂调查，所有者问卷调查和访谈，最多将它们限制在数百个家庭花园中。我们表明，对公开提供的图像进行自动分析可以实现更大范围的调查，从而可以扩大这些传统数据源。具体来说，我们展示了在Google街景视图中使用高分辨率的街道级照片和对象检测网络（RetinaNet）来对至少六个广泛种植的植物物种的普遍性进行大规模高分辨率调查的可行性在泰国面对路面的花园里。我们的研究小组检查了4000张垂直于街道且位于宅基地10 m以内的图像，并手动勾勒出11种常见植物物种的所有感知实例。在这些标记的图像上训练的神经网络用于检测大约150,000张图像中这些物种的实例，这些图像构成了泰国北部五个省份每十个家园中大约一个的观点。将六个植物物种的结果显示为每个图像中检测到的目标物种的平均数量和单个物种的流行率的热图，并在500 m和2.5 km的尺度上进行空间平均。量化每个图像中目标物种的平均数量的城乡对比，甚至在相邻村庄之间也观察到很大的差异。香蕉和椰子的空间异质性比其他物种更为明显。猕猴桃和木瓜更经常出现在城镇外，而龙血树和芒果则一直存在于城镇的中心。