Unconstrained Aerial Scene Recognition with Deep Neural Networks and a New Dataset

Abstract

Aerial scene recognition is a fundamental research problem in interpreting high-resolution aerial imagery. Over the past few years, most studies focus on classifying an image into one scene category, while in real-world scenarios, it is more often that a single image contains multiple scenes. Therefore, in this paper, we investigate a more practical yet underexplored task---multi-scene recognition in single images. To this end, we create a large-scale dataset, called MultiScene dataset, composed of 100,000 unconstrained images each with multiple labels from 36 different scenes. Among these images, 14,000 of them are manually interpreted and assigned ground-truth labels, while the remaining images are provided with crowdsourced labels, which are generated from low-cost but noisy OpenStreetMap (OSM) data. By doing so, our dataset allows two branches of studies: 1) developing novel CNNs for multi-scene recognition and 2) learning with noisy labels. We experiment with extensive baseline models on our dataset to offer a benchmark for multi-scene recognition in single images. Aiming to expedite further researches, we will make our dataset and pre-trained models available