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

TL;DR

This paper introduces two new multi-view datasets combining aerial, ground-level, and satellite images to improve scene classification, demonstrating that multi-view data enhances classification accuracy.

Contribution

The paper provides two novel publicly available datasets with multi-view images and explores their use in multi-view scene classification tasks.

Findings

Multi-view data improves scene classification accuracy.

Fusion methods enhance the benefits of multi-view imagery.

Datasets facilitate research on multi-view scene understanding.

Abstract

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.