AiRound and CV-BrCT: Novel Multi-View Datasets for Scene Classification
This provides a resource for researchers in computer vision and remote sensing to develop multi-view approaches, though it is incremental as it focuses on dataset creation rather than novel methods.
The authors tackled the lack of benchmark datasets for multi-view scene classification by introducing two new datasets, AiRound and CV-BrCT, containing aerial and ground-level images, and demonstrated through experiments that multi-view data enhances image classification.
It is undeniable that aerial/satellite images can provide useful information for a large variety of tasks. But, since these images are always looking from above, some applications can benefit from complementary information provided by other perspective views of the scene, such as ground-level images. Despite a large number of public repositories for both georeferenced photographs and aerial images, there is a lack of benchmark datasets that allow the development of approaches that exploit the benefits and complementarity of aerial/ground imagery. In this paper, we present two new publicly available datasets named \thedataset~and CV-BrCT. The first one contains triplets of images from the same geographic coordinate with different perspectives of view extracted from various places around the world. Each triplet is composed of an aerial RGB image, a ground-level perspective image, and a Sentinel-2 sample. The second dataset contains pairs of aerial and street-level images extracted from southeast Brazil. We design an extensive set of experiments concerning multi-view scene classification, using early and late fusion. Such experiments were conducted to show that image classification can be enhanced using multi-view data.