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It is undeniable that aerial/satellite images can provide useful informationfor a large variety of tasks. But, since these images are always looking fromabove, some applications can benefit from complementary information provided byother perspective views of the scene, such as ground-level images. Despite alarge number of public repositories for both georeferenced photographs andaerial images, there is a lack of benchmark datasets that allow the developmentof approaches that exploit the benefits and complementarity of aerial/groundimagery. In this paper, we present two new publicly available datasets named\thedataset~and CV-BrCT. The first one contains triplets of images from thesame geographic coordinate with different perspectives of view extracted fromvarious places around the world. Each triplet is composed of an aerial RGBimage, a ground-level perspective image, and a Sentinel-2 sample. The seconddataset contains pairs of aerial and street-level images extracted fromsoutheast Brazil. We design an extensive set of experiments concerningmulti-view scene classification, using early and late fusion. Such experimentswere conducted to show that image classification can be enhanced usingmulti-view data.

AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification