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Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale., Author summary Providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. Specifically, we use convolutional neural networks to detect a variety of types of breeding site container types in Google street view images and use the container counts to create container density maps. Evaluation of the approach is carried out over three provinces in Thailand: Bangkok, Krabi, and Nakhon Si Thammarat. Our evaluation shows that the object recognition network can accurately recognize several of the most important types of containers in Thailand. The container counts obtained from the street view images agree well with container counts from available manual surveys. We further show that simple multi-linear models using container density values provide good predictions of Breteau index (number of positive containers per 100 houses inspected) values. This is the first study to present results validating container counts from image analysis against such data.