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, 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 multi-scene 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 multi-scene 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.
Multiple instance dense connected convolution neural network for aerial image scene classification
School of Remote Sensing and Information Engineering, Wuhan University（武汉大学遥感信息工程学院）；Faculty of Information Engineering, China University of Geosciences（中国地质大学（武汉）信息工程学院）
Cross-Task Transfer for Geotagged Audiovisual Aerial Scene Recognition
Baidu Research（百度研究院）；Technical University of Munich（慕尼黑工业大学）；German Aerospace Center（德国航空航天中心）；Beijing Jiaotong University（北京交通大学）
空中景物识别是遥感领域的一项基本任务，近年来受到越来越多的关注。虽然航拍图像中的视觉信息具有强大的模型和高效的算法，在场景识别上取得了相当的性能，但它仍然受到地面目标，光照条件等变化的影响。本文受认知科学中多通道感知理论的启发，为了提高航拍场景识别的性能，我们探索了一种新颖的视听空中场景识别任务，使用图像和声音作为输入。基于观察到一些特定的声音事件更有可能在给定的地理位置被听到，我们建议利用声音事件的知识，以提高空中场景识别的性能。为此，我们构建了一个新的数据集，名为Audio Visual Aerial sceNe reCognition Scene Et（ADVANCE）。在此数据集的帮助下，我们评估了三种将声音事件知识转移到多模态学习框架中的空中场景识别任务的方法，并显示了利用音频信息进行空中场景识别的好处。源代码是公开可复制的目的。
RefAerial: A Benchmark and Approach for Referring Detection in Aerial Images
School of Artificial Intelligence, Anhui University（安徽大学人工智能学院）；School of Computer Science and Technology, Anhui University（安徽大学计算机科学与技术学院）；State Key Laboratory of Opto-Electronic Information Acquisition and Protection Technology, Anhui University（安徽大学光电信息采集与防护技术国家重点实验室）；Anhui Provincial Key Laboratory of Security Artificial Intelligence, Anhui University（安徽省安全人工智能重点实验室）；Anhui Provincial Key Laboratory of Multimodal Cognitive Computation, Anhui University（安徽省多模态认知计算重点实验室）
Binary Patterns Encoded Convolutional Neural Networks for Texture Recognition and Remote Sensing Scene Classification
Aalto University School of Science（阿尔托大学科学学院）；Linköping University（林雪平大学）；Universitat Autonoma de Barcelona（巴塞罗那自治大学）；VTT Technical Research Centre of Finland Ltd（芬兰VTT技术研究中心有限公司）
Joint Inference of Groups, Events and Human Roles in Aerial Videos
University of California, Los Angeles（加州大学洛杉矶分校）；Jet Propulsion Laboratory, California Institute of Technology（加州理工学院喷气推进实验室）；Oregon State University（俄勒冈州立大学）
RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction
NKIARI, Shenzhen Futian（深圳福田NKIARI）；VCIP, CS, Nankai University（南开大学VCIP研究所）；AAIS, Nankai University（南开大学AAIS）；College of Electronic Engineering, National University of Defense Technology, Changsha, China（国防科技大学电子工程学院，长沙，中国）
Horizon-GS: Unified 3D Gaussian Splatting for Large-Scale Aerial-to-Ground Scenes
University of Science and Technology of China（中国科学技术大学）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；Shanghai Jiao Tong University（上海交通大学）；The Chinese University of Hong Kong（香港中文大学）；Brown University（布朗大学）；The University of Hong Kong（香港大学）
THYME: Temporal Hierarchical-Cyclic Interactivity Modeling for Video Scene Graphs in Aerial Footage
University of Arkansas（亚拉巴马大学）；University of Science, VNU-HCM（科学大学，VNU-HCM）；Vietnam National University, Ho Chi Minh（越南国家大学，胡志明市）；Ohio State University（俄亥俄州立大学）
Scene Clustering Based Pseudo-labeling Strategy for Multi-modal Aerial View Object Classification
University of Science and Technology of China（中国科学技术大学）；Ping An Technology Co., Ltd（平安科技有限公司）；Hefei ZhanDa Intelligence Technology Co., Ltd（合肥展大智能科技有限公司）
TAFormer: A Unified Target-Aware Transformer for Video and Motion Joint Prediction in Aerial Scenes
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；University of Chinese Academy of Sciences（中国科学院大学）；School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences（中国科学院大学电子电气与通信工程学院）
Aerial-Ground Image Feature Matching via 3D Gaussian Splatting-based Intermediate View Rendering
organization= School of Computer Science , addressline= China University of Geosciences , city= Wuhan , postcode= 430074 , country= China；organization= Guangdong Key Laboratory of Urban Informatics , addressline= Shenzhen University , city= Guangdong Shenzhen , postcode= 518060 , country= China；organization= MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area , addressline= Shenzhen University , city= Guangdong Shenzhen , postcode= 518060 , country= China；organization= Engineering Research Center of Natural Resource Information Management
