The development of computer vision algorithms for Unmanned Aerial Vehicle (UAV) applications in urban environments heavily relies on the availability of large-scale datasets with accurate annotations. However, collecting and annotating real-world UAV data is extremely challenging and costly. To address this limitation, we present FlyAwareV2, a novel multimodal dataset encompassing both real and synthetic UAV imagery tailored for urban scene understanding tasks. Building upon the recently introduced SynDrone and FlyAware datasets, FlyAwareV2 introduces several new key contributions: 1) Multimodal data (RGB, depth, semantic labels) across diverse environmental conditions including varying weather and daytime; 2) Depth maps for real samples computed via state-of-the-art monocular depth estimation; 3) Benchmarks for RGB and multimodal semantic segmentation on standard architectures; 4) Studies on synthetic-to-real domain adaptation to assess the generalization capabilities of models trained on the synthetic data. With its rich set of annotations and environmental diversity, FlyAwareV2 provides a valuable resource for research on UAV-based 3D urban scene understanding.
Beihang University（北京航空航天大学）；Beijing Electronic Science and Technology Institute（北京电子科技学院）；Peng Cheng Laboratory（鹏城实验室）
Despite the growing number of unmanned aerial vehicles (UAVs) and aerial videos, there is a paucity of studies focusing on the aesthetics of aerial videos that can provide valuable information for improving the aesthetic quality of aerial photography. In this article, we present a method of deep multimodality learning for UAV video aesthetic quality assessment. More specifically, a multistream framework is designed to exploit aesthetic attributes from multiple modalities, including spatial appearance, drone camera motion, and scene structure. A novel specially designed motion stream network is…
MM-UAVBench: How Well Do Multimodal Large Language Models See, Think, and Plan in Low-Altitude UAV Scenarios?
Tsinghua University（清华大学）；Nankai University（南开大学）；Northwest Polytechnical University（西北工业大学）；Chinese Academy of Sciences（中国科学院）；Harbin Institute of Technology（哈尔滨工业大学）
Microscopic Vehicle Trajectory Datasets from UAV-collected Video for Heterogeneous, Area-Based Urban Traffic
Transportation Research & Injury Prevention Centre (TRIPC), Indian Institute of Technology Delhi（印度理工学院德里分校交通研究与伤害预防中心（TRIPC））；Indian Institute of Technology Delhi（印度理工学院德里分校）；Queensland University of Technology（昆士兰科技大学）
PixDLM: A Dual-Path Multimodal Language Model for UAV Reasoning Segmentation
Key Laboratory of Multimedia Trusted Perception and Efficient Computing, Ministry of Education of China, Xiamen University（中国教育部多媒体可信感知与高效计算重点实验室，厦门大学）
UAV-CB: A Complex-Background RGB-T Dataset and Local Frequency Bridge Network for UAV Detection
Pengcheng Laboratory（鹏城实验室）；South China University of Technology（南方科技大学）；Peking University（北京大学）；Xinjiang University（新疆大学）；Harbin Institute of Technology（哈尔滨工业大学）
Multimodal Urban Tree Detection from Satellite and Street-Level Imagery via Annotation-Efficient Deep Learning Strategies
Department of Computer Science, University of California, Davis（加州大学戴维斯分校计算机科学系）；Department of Biological and Agricultural Engineering, University of California, Davis（加州大学戴维斯分校生物与农业工程系）
Wuhan University（武汉大学）；University of Arkansas（阿肯色大学）；Leador Spatial Information Technology Corporation（立得空间信息技术股份有限公司）
In a complex urban scene, observation from a single sensor unavoidably leads to voids in observations, failing to describe urban objects in a comprehensive manner. In this paper, we propose a spatio-temporal-spectral-angular observation model to integrate observations from UAV and mobile mapping vehicle platform, realizing a joint, coordinated observation operation from both air and ground. We develop a multi-source remote sensing data acquisition system to effectively acquire multi-angle data of complex urban scenes. Multi-source data fusion solves the missing data problem caused by occlusion…
Unmanned Aerial Vehicles (UAVs) have the potential to be used for many applications in urban environments. However, allowing UAVs to fly above densely populated areas raises concerns regarding safety. One of the main safety issues is the possibility for a failure to cause the loss of navigation capabilities, which can result in the UAV falling/landing in hazardous areas such as busy roads, where it can cause fatal accidents. Current standards, such as the SORA published in 2019, do not consider applicable mitigation techniques to handle this kind of hazardous situations. Consequently, certifyi…
UAUTrack: Towards Unified Multimodal Anti-UAV Visual Tracking
College of Intelligent Robotics and Advanced Manufacturing（智能机器人与先进制造学院）；Fudan University（复旦大学）；School of Computer Science and Technology（计算机科学与技术学院）；Zhejiang Normal University（浙江师范大学）；Department of Mechanical Engineering（机械工程系）；The Hong Kong Polytechnic University（香港理工大学）
UniRef-UAV: A Multimodal Benchmark for Universal Referring in UAV Imagery
School of Automation, Northwestern Polytechnical University（自动化学院，西北工业大学）；College of Missile Engineering, Rocket Force University of Engineering（导弹工程学院，火箭兵工程大学）；School of Artificial Intelligence, Chongqing University of Posts and Telecommunications（人工智能学院，重庆邮电大学）
UAVD-Mamba: Deformable Token Fusion Vision Mamba for Multimodal UAV Detection
College of Mechanical and Vehicle Engineering, Hunan University（湖南大学机械与车辆工程学院）；School of Electronic Information and Communications, Huazhong University of Science and Technology（华中科技大学电子信息与通信学院）
University of Twente（特文特大学）；Wuhan University（武汉大学）；Ohio State University（俄亥俄州立大学）
Semantic segmentation has been one of the leading research interests in computer vision recently. It serves as a perception foundation for many fields, such as robotics and autonomous driving. The fast development of semantic segmentation attributes enormously to the large scale datasets, especially for the deep learning related methods. There already exist several semantic segmentation datasets for comparison among semantic segmentation methods in complex urban scenes, such as the Cityscapes and CamVid datasets, where the side views of the objects are captured with a camera mounted on the dri…
Shenzhen University（深圳大学）；Guangdong Laboratory of Artificial Intelligence and Digital Economy (Shenzhen)（广东省人工智能与数字经济实验室（深圳））；China University of Geosciences（中国地质大学）
UAVs have become one of the widely used remote sensing platforms and played a critical role in the construction of smart cities. However, due to the complex environment in urban scenes, secure and accurate data acquisition brings great challenges to 3D modeling and scene updating. Optimal trajectory planning of UAVs and accurate data collection of onboard cameras are non-trivial issues in urban modeling. This study presents the principle of optimized views photogrammetry and verifies its precision and potential in large-scale 3D modeling. Different from oblique photogrammetry, optimized views …
NavAgent: Multi-scale Urban Street View Fusion For UAV Embodied Vision-and-Language Navigation
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）
DynFly: Dynamic-Aware Continuous Trajectory Generation for UAV Vision-Language Navigation in Urban Environments
Beijing Institute of Technology（北京理工大学）；Tsinghua University（清华大学）；Dalian University of Technology（大连理工大学）；Xidian University（西安电子科技大学）；Huazhong University of Science and Technology（华中科技大学）
Wageningen University & Research（瓦赫宁根大学及研究中心）；University of Campinas（坎皮纳斯大学）
Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive. Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities). We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban…
A Multimodal Learning-based Approach for Autonomous Landing of UAV
Faculty of Engineering of University of Porto (FEUP)（波尔图大学工程学院）；Institute for Systems and Computer Engineering - Technology and Science (INESC TEC)（系统与计算机工程 - 技术与科学研究所）
EGM-Det: Entropy-Guided Multimodal Adaptive Fusion for UAV RGB-IR Object Detection
School of Cybersecurity, Northwestern Polytechnical University（西北工业大学网络空间安全学院）；School of Automation and Software Engineering, Shanxi University（山西大学自动化与软件学院）
PRI-Net: A Lightweight Multimodal Framework for 3D UAV Localization
College of Information Science and Electronic Engineering, Zhejiang University（浙江大学信息与电子工程学院）；Zhejiang Key Laboratory of Multimodal Communication Networks and Intelligent Information Processing（浙江省多模态通信网络与智能信息处理重点实验室）
Korea University（高丽大学）；Hallym University（翰林大学）；Ajou University（亚洲大学）；UC-Irvine（加州大学尔湾分校）
Autonomous surveillance unmanned aerial vehicles (UAVs) are deployed to observe the streets of the city for any suspicious activities. This paper utilizes surveillance UAVs for the purpose of detecting the presence of a fire in the streets. An extensive database is collected from UAV surveillance drones. With the aid of artificial intelligence (AI), fire stations can swiftly identify the presence of a fire emerging in the neighborhood. Spatio-temporal split learning is applied to this scenario to preserve privacy and globally train a fire classification model. Fires are hazardous natural disas…
Large Language Model Guided Progressive Feature Alignment for Multimodal UAV Object Detection
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）；University of Science and Technology of China（中国科学技术大学）
Fudan University（复旦大学）；China University of Petroleum（中国石油大学）
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate overfitting in fusing multimodal features and develop some new features such as fractal measure of road complexity in satellite images, taxi flows, POIs, and road width and connectivity in OpenStreetMap. The solution is more promising in performance than the baseline methods and the single-modality data based solutions. After visualization from a micro view…
In recent years, unmanned aerial vehicle (UAV) imaging is a suitable solution for real-time monitoring different vehicles on the urban scale. Real-time vehicle detection with the use of uncertainty estimation in deep meta-learning for the portable platforms (e.g., UAV) potentially improves video understanding in real-world applications with a small training dataset, while many vehicle monitoring approaches appear to understand single-time detection with a big training dataset. The purpose of real-time vehicle detection from oblique UAV images is to locate the vehicle on the time series UAV ima…
A Multimodal Large Language Model-Driven Framework for Context-Aware UAV Emergency Landing Site Selection
School of Information Science and Engineering, Southeast University（信息科学与工程学院，东南大学）；LASER, International Digital Economy Academy（LASER，国际数字经济学院）；Department of Electronic Engineering, East China Normal University（电子工程系，华东师范大学）
UrbanGraphEmbeddings: Learning and Evaluating Spatially Grounded Multimodal Embeddings for Urban Science
National University of Singapore Department of Architecture Singapore；The Chinese University of Hong Kong Dept of Systems Eng. \& Eng. Mgmt. China；Singapore Management University School of Computing \& Info. Systems Singapore；National University of Singapore；Department of Architecture；The Chinese University of Hong Kong；Singapore Management University
Visual-based Safe Landing for UAVs in Populated Areas: Real-time Validation in Virtual Environments
Universidad Autónoma de Ciudad Juárez UACJ（华雷斯自治大学）；Center for Research in Mathematics CIMAT AC（数学研究中心CIMAT AC）；University of Texas at El Paso（德克萨斯大学埃尔帕索分校）
BuildingView: Constructing Urban Building Exteriors Databases with Street View Imagery and Multimodal Large Language Mode
Thrust of Urban Governance and Design, The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州）城市治理与设计中心）；Spatial Sciences Institute, University of Southern California（南加州大学空间科学学院）
Sharif University of Technology（谢里夫理工大学）；University of Tehran（德黑兰大学）
Single-task learning in artificial neural networks will be able to learn the model very well, and the benefits brought by transferring knowledge thus become limited. In this regard, when the number of tasks increases (e.g., semantic segmentation, panoptic segmentation, monocular depth estimation, and 3D point cloud), duplicate information may exist across tasks, and the improvement becomes less significant. Multi-task learning has emerged as a solution to knowledge-transfer issues and is an approach to scene understanding which involves multiple related tasks each with potentially limited trai…
