Chen C.-W.Kuo Y.-H.Lee T.Lee C.-H.Hsu W.2019-07-102019-07-102018978153866100021607508https://scholars.lib.ntu.edu.tw/handle/123456789/413040Drones become popular recently and equip more sensors than traditional cameras, which bring emerging applications and research. To enable drone-based applications, providing related information (e.g., building) to understand the environment around the drone is essential. We frame this drone-view building identification as building retrieval problem: given a building (multimodal query) with its images, geolocation and drone's current location, we aim to retrieve the most likely proposal (building candidate) on a drone-view image. Despite few annotated drone-view images to date, there are many images of other views from the Web, like ground-level, street-view and aerial images. Thus, we propose a cross-view triplet neural network to learn visual similarity between drone-view and other views, further design relative spatial estimation of each proposal and the drone, and collect new drone-view datasets for the task. Our method outperforms triplet neural network by 0.12 mAP. (i.e., 22.9 to 35.0, +53% in a sub-dataset [LA]) ? 2018 IEEE.[SDGs]SDG11conference paper10.1109/CVPRW.2018.001972-s2.0-85060892391