MultiScene: A Large-Scale Dataset and Benchmark for Multiscene Recognition in Single Aerial Images

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 article, we investigate a more practical yet underexplored task-multiscene recognition in single images. To this end, we create a large-scale dataset, called MultiScene, composed of 100 000 unconstrained high-resolution aerial images. Considering that manually labeling such images is extremely arduous, we resort to low-cost annotations from crowdsourcing platforms, e.g., OpenStreetMap (OSM). However, OSM data might suffer from incompleteness and incorrectness, which introduce noise into image labels. To address this issue, we visually inspect 14 000 images and correct their scene labels, yielding a subset of cleanly annotated images, named MultiScene-Clean. With it, we can develop and evaluate deep networks for multiscene recognition using clean data. Moreover, we provide crowdsourced annotations of all images for the purpose of studying network learning with noisy labels. We conduct experiments with extensive baseline models on both MultiScene-Clean and MultiScene to offer benchmarks for multiscene recognition in single images and learning from noisy labels for this task, respectively. To facilitate progress, we make our dataset and trained models available on https://gitlab.lrz.de/ai4eo/reasoning/multiscene.

Remote Sensing Image Retrieval in the Past Decade: Achievements, Challenges, and Future Directions

“…Thanks to the literature's efforts, several publicly available datasets available for MLRSIR have been collected and opened in the past five years. The existing MLRSIR datasets include DLRSD [114], WHDLD [121], MLRSNet [171], ML-AID [172], Mul-tiScene [173], and BigEarthNet [174]. Among these datasets, three representative archives, i.e.…”

CNN and Transformer Fusion for Remote Sensing Image Semantic Segmentation

“…Convolutional neural networks (CNNs) have exceptionally performed in RS image segmentation [18][19][20][21][22][23]. Remarkably, the fully convolutional network (FCN) method [24] enables end-to-end training and pixel-level classification, thereby propelling the advancement of CNNs in image segmentation.…”