Hybrid macro-micro visual analysis for city-scale state estimation

We address the task of estimating large-scale land surface conditions using overhead aerial (macro-level) images and street view (micro-level) images. These two types of images are captured from orthogonal viewpoints and have different resolutions, thus conveying very different types of information that can be used in a complementary way. Moreover, their integration is necessary to enable an accurate understanding of changes in natural phenomena over massive city-scale landscapes. The key technical challenge is devising a method to integrate these two disparate types of image data in an effective manner, to leverage the wide coverage capabilities of macro-level images and detailed resolution of micro-level images. The strategy proposed in this work uses macro-level imaging to learn the extent to which the land condition corresponds between land regions that share similar visual characteristics (e.g., mountains, streets, buildings, rivers), whereas micro-level images are used to acquire high resolution statistics of land conditions (e.g., the amount of debris on the ground). By combining macro- and micro-level information about regional correspondences and surface conditions, our proposed method is capable of generating detailed estimates of land surface conditions over an entire city.