University-1652: A Multi-view Multi-source Benchmark for Drone-based Geo-localization
SUSTech-UTS Joint Centre of CIS, Southern University of Science and Technology（南方科技大学 SUSTech-UTS 计算机科学与工程联合中心）；ReLER, AAII, University of Technology Sydney（悉尼科技大学 AAII ReLER）
We consider the problem of cross-view geo-localization. The primary challenge of this task is to learn the robust feature against large viewpoint changes. Existing benchmarks can help, but are limited in the number of viewpoints. Image pairs, containing two viewpoints, e.g., satellite and ground, are usually provided, which may compromise the feature learning. Besides phone cameras and satellites, in this paper, we argue that drones could serve as the third platform to deal with the geo-localization problem. In contrast to the traditional ground-view images, drone-view images meet fewer obstacles, e.g., trees, and could provide a comprehensive view when flying around the target place. To verify the effectiveness of the drone platform, we introduce a new multi-view multi-source benchmark for drone-based geo-localization, named University-1652. University-1652 contains data from three platforms, i.e., synthetic drones, satellites and ground cameras of 1,652 university buildings around the world. To our knowledge, University-1652 is the first drone-based geo-localization dataset and enables two new tasks, i.e., drone-view target localization and drone navigation. As the name implies, drone-view target localization intends to predict the location of the target place via drone-view images. On the other hand, given a satellite-view query image, drone navigation is to drive the drone to the area of interest in the query. We use this dataset to analyze a variety of off-the-shelf CNN features and propose a strong CNN baseline on this challenging dataset. The experiments show that University-1652 helps the model to learn the viewpoint-invariant features and also has good generalization ability in the real-world scenario.
MobileGeo: Exploring Hierarchical Knowledge Distillation for Resource-Efficient Cross-view Drone Geo-Localization
Department of Computer and Information Science, University of Macau（计算机与信息科学系，澳门大学）；School of Computer Science and Engineering, South China University of Technology and Pazhou Lab（华南理工大学计算机科学与工程学院及琶洲实验室）；Faculty of Intelligent Manufacturing, Wuyi University（翁源大学智能制造学院）；Unmanned System Research Institute, Northwestern Polytechnical University（无人系统研究院，西北工业大学）
Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；FST and ICI, University of Macau（澳门大学科技学院与当代计算机与软件研究所）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）
Without Paired Labeled Data: End-to-End Self-Supervised Learning for Drone-view Geo-Localization
State Key Laboratory for Strength and Vibration of Mechanical Structures（强度与振动机械结构国家重点实验室）；Shaanxi Key Laboratory of Environment and Control for Flight Vehicle（陕西省飞行器环境与控制重点实验室）；School of Aerospace Engineering（航空航天工程学院）；Xi’an Jiaotong University（西安交通大学）；Institute of Automation（自动化研究所）；Chinese Academy of Sciences（中国科学院）；School of Artificial Intelligence（人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）
Real-Time Assessment of Bystander Situation Awareness in Drone-Assisted First Aid
Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA（韦尔登生物医学工程学院，普渡大学，西拉法叶，印第安纳州，美国）；Edward P. Fitts Department of Industrial and Systems Engineering, North Carolina State University, Raleigh, NC, USA（埃德华·P·菲茨工业与系统工程系，北卡罗来纳州立大学，拉格兰，NC，美国）；Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, Indiana, USA（雷根斯特里夫医疗工程中心，普渡大学，西拉法叶，印第安纳州，美国）；Edwardson School of Industrial Engineering, Purdue University, West Lafayette, Indiana, USA（埃德华森工业工程学院，普渡大学，西拉法叶，印第安纳州，美国）
Wuhan University（武汉大学）；The University of Tokyo（东京大学）；National Institute of Informatics（国立信息学研究所）
The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, the challenge comes from the gap between ground-view and satellite-view, which includes not only large viewpoint changes (some parts of the landmark may be invisible from front view to top view) but also highly irrelevant background (the tar…
Prince Sultan University（苏丹王子大学）；CISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto（波尔图理工学院ISEP的INESC-TEC旗下CISTER（注：因原文为层级关联机构，按要求提取到学院/研究院级别，整合为：波尔图理工学院）
In this paper, we propose a deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images in a palm tree Farm in the Kharj region, in Riyadh Saudi Arabia, using DJI drones, and we built a dataset of around 10,000 instances of palms trees. Then, we developed a convolutional neural network model using the state-of-the-art, Faster R-CNN algorithm. Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to detect the ge…
Video2BEV: Transforming Drone Videos to BEVs for Video-based Geo-localization
Faculty of Science and Technology and Institute of Collaborative Innovation, University of Macau（澳门大学科学与技术学院及协同创新研究所）；Institute of Artificial Intelligence, Beihang University（北航人工智能研究所）
U.S. Army Research Laboratory（美国陆军研究实验室）；University of Southern California - Institute for Creative Technologies（南加州大学创新技术研究所）；University of Texas - El Paso（德克萨斯大学埃尔帕索分校）；University of California - Santa Barbara（加州大学圣塔芭芭拉分校）；Headwall Photonics（Headwall Photonics公司）
Following an initiative formalized in April 2016 formally known as ARL West between the U.S. Army Research Laboratory (ARL) and University of Southern California's Institute for Creative Technologies (USC ICT), a field experiment was coordinated and executed in the summer of 2016 by ARL, USC ICT, and Headwall Photonics. The purpose was to image part of the USC main campus in Los Angeles, USA, using two portable COTS (commercial off the shelf) aerial drone solutions for data acquisition, for photogrammetry (3D reconstruction from images), and fusion of hyperspectral data with the recovered set …
UniGeo: A Multi-modal Large Language Model for Text-Guided Cross-View Geo-Localization
School of Computer Engineering and Science, Shanghai University（上海大学计算机工程与科学学院）；Institute of Collaborative Innovation, University of Macau（澳门大学协同创新研究院）
Scale-adaptive UAV Geo-localization via Height-aware Partition Learning
School of Automation, Hangzhou Dianzi University（杭州电子科技大学自动化学院）；School of Communication Engineering, Hangzhou Dianzi University（杭州电子科技大学通信工程学院）；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）；Institute of Collaborative Innovation, University of Macau（澳门大学协同创新研究院）
Efficient Spike-driven Transformer for High-performance Drone-View Geo-Localization
State Key Laboratory for Strength and Vibration of Mechanical Structures, Shaanxi Key Laboratory of Environment and Control for Flight Vehicle, School of Aerospace Engineering, Xi’an Jiaotong University（强度与振动机械结构国家重点实验室、飞行器环境与控制陕西省重点实验室、航空航天工程学院）
VAGeo: View-specific Attention for Cross-View Object Geo-Localization
School of Computer Science and Technology, Wuhan University of Science and Technology（武汉科技大学计算机科学与技术学院）；Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System（湖北省智能信息处理与实时工业系统重点实验室）
In this paper, we focus on the task of multi-view multi-source geo-localization, which serves as an important auxiliary method of GPS positioning by matching drone-view image and satellite-view image with pre-annotated GPS tag. To solve this problem, most existing methods adopt metric loss with an weighted classification block to force the generation of common feature space shared by different view points and view sources. However, these methods fail to pay sufficient attention to spatial information (especially viewpoint variances). To address this drawback, we propose an elegant orientation-…
WeatherPrompt: Multi-modality Representation Learning for All-Weather Drone Visual Geo-Localization
School of Computer Engineering and Science, Shanghai University, China（上海大学计算机工程与科学学院）；Faculty of Science and Technology and Institute of Collaborative Innovation, University of Macau, China（澳门大学科技学院和协同创新研究院）
University of South Florida（南佛罗里达大学）；Center for Urban Transportation Research（城市交通研究中心）；Florida Department of Transportation（佛罗里达州交通部）
There is a rapid growth of applications of Unmanned Aerial Vehicles (UAVs) in traffic management, such as traffic surveillance, monitoring, and incident detection. However, the existing literature lacks solutions to real-time incident detection while addressing privacy issues in practice. This study explored real-time vehicle detection algorithms on both visual and infrared cameras and conducted experiments comparing their performance. Red Green Blue (RGB) videos and thermal images were collected from a UAS platform along highways in the Tampa, Florida, area. Experiments were designed to quant…
Bristol Vision Institute（布里斯托尔视觉研究所）；University of Bristol（布里斯托大学）
In this paper, we present a workflow for the simulation of drone operations exploiting realistic background environments constructed within Unreal Engine 4 (UE4). Methods for environmental image capture, 3D reconstruction (photogrammetry) and the creation of foreground assets are presented along with a flexible and user-friendly simulation interface. Given the geographical location of the selected area and the camera parameters employed, the scanning strategy and its associated flight parameters are first determined for image capture. Source imagery can be extracted from virtual globe software…
AirSim360: A Panoramic Simulation Platform within Drone View
Insta360 Research（Insta360研究机构）；Wuhan University（武汉大学）；University of California, San Diego（加州大学圣地亚哥分校）；Nanyang Technological University（南洋理工大学）；University of California, Merced（加州大学默塞德分校）；Shenzhen University（深圳大学）
Drone racing is a recreational sport in which the goal is to pass through a sequence of gates in a minimum amount of time while avoiding collisions. In autonomous drone racing, one must accomplish this task by flying fully autonomously in an unknown environment by relying only on computer vision methods for detecting the target gates. Due to the challenges such as background objects and varying lighting conditions, traditional object detection algorithms based on colour or geometry tend to fail. Convolutional neural networks offer impressive advances in computer vision but require an immense a…
From Limited Labels to Open Domains:An Efficient Learning Method for Drone-view Geo-Localization
State Key Laboratory for Strength and Vibration of Mechanical Structures（强度与振动机械结构国家重点实验室）；Shaanxi Key Laboratory of Environment and Control for Flight Vehicle（飞行器环境与控制陕西省重点实验室）；School of Aerospace Engineering（航空工程学院）；Xi’an Jiaotong University（西安交通大学）；Institute of Automation（自动化研究所）；Chinese Academy of Sciences（中国科学院）；School of Artificial Intelligence（人工智能学院）；University of Chinese Academy of Sciences（中国科学院大学）；Peng Cheng Laboratory（鹏城实验室）
SMGeo: Cross-View Object Geo-Localization with Grid-Level Mixture-of-Experts
School of Information Science and Technology（信息科学与技术学院）；Interdisciplinary Research Center for Artificial Intelligence（人工智能交叉研究中心）；Beijing University of Chemical Technology（北京化工大学）
GRASP: Granularity-Aware Region Alignment and Semantic Prototype Learning for Fine-Grained Cross-Modal Understanding in Drone Views
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）
A Parameter-Efficient Mixture-of-Experts Framework for Cross-Modal Geo-Localization
The Institute of Artificial Intelligence (TeleAI), China Telecom（人工智能研究所（TeleAI），中国电信）；East China Normal University（东华师范大学）；National Tsing Hua University（国立清华大学）
