Urban drone dataset. The flight planning software eMotion was used.

Urban drone dataset Fixed high-resolution cameras were installed at tall buildings (around 100 m) in a few Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have found numerous applications in civil disciplines such as construction inspection, infrastructure planning, precision agriculture, real-time monitoring of road traffic, and overhead power line inspection. Abstract. In this article, a method for creating highly realistic synthetic drone micro-Doppler spectrograms is presented and its effectiveness of training a bird-drone classifier for real scenario classification is shown via comparisons to a real benchmark. V al: 1500 (class 23 \n. These drones are equipped with sophisticated sensors, for example high-resolution cameras, that enable While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images spanning 7 classes. Additionally, the proposed architecture is validated using two datasets: the prepared NITRDrone dataset and the Urban Drone Dataset (UDD). , tree species, height, DBH, crown). Currently, most intersections in urban areas are equipped with traffic lights. The application of drones for urban traffic management has evolved from simple monitoring tasks, The bounding box, which is commonly used as an annotation method for object detection in datasets of drone imagery, such as VisDrone, UAVDT, and AI-TOD, was selected as the annotation method in this study [43,44,45]. - VisDrone/VisDrone-Dataset. No co-authorship is expected for This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. The flight planning software eMotion was used. However, these techniques heavily rely on the size of the training data, and We conduct experiments on two benchmark datasets [UAV image dataset (UAVid) and urban drone dataset (UDD)] by comparing the proposed EDCPNet with six other competing methods, i. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above ground. Besides, a baseline UAV-Human is a large dataset for human behavior understanding with UAVs. Complex Urban Dataset (KAIST) now includes stereo camera images! (published in Similarly, the pNEUMA [3], inD [80], rounD [81], OpenDD [82], Interaction [83] and CitySim [84] datasets utilize drones or swarms of drones to study complex urban vehicle and pedestrian The authors have made available the VisDrone2019-DET Dataset, a comprehensive collection of drone-captured images tailored for object detection tasks. Urban drone stations siting optimization based on hybrid algorithm of MILP and machine learning Meng You [9] proposed a dynamic iterative partial optimization algorithm to handle the challenge of large datasets. This Dataset is only for non-commercial use. Urban proyect 3. 85% on UDD and NITRDrone datasets, respectively. Abstract: In this article, a method for creating highly realistic synthetic drone micro-Doppler spectrograms is presented and its effectiveness of training a bird-drone classifier for real scenario classification is shown via comparisons to a real benchmark. In this work, we present a novel long-range The image acquisition of the whole dataset was done using 4 eBee X - senseFly drones flying simultaneously. Ensuring high accuracy of semantic segmentation models for drones requires access to diverse, large-scale, and high-resolution datasets, which are often scarce in the field of aerial image processing. In this case, users can improve the performance of GNSS positioning via raw data. The development of datasets for autonomous drone navigation is crucial for enhancing the performance of UAVs in urban settings. Official Distributor of SwellPro, QYSea Chasing and other fishing drone accessories. 1: 546: November 5, 2022 pNEUMA is an open large-scale dataset of naturalistic trajectories of half a million vehicles that have been collected by a one-of-a-kind experiment by a swarm of drones in the congested downtown area of Athens, Greece. To the best of our knowledge, there are four datasets, including Aeroscapes [], In this work, we propose CTV-Dataset, a cyclist-focused top-view dataset collected from laboratory experiments using a camera-equipped drone. The Urban Drone dataset (UDD) Chen et al. Drone based Datasets In recent years, some drone-based datasets have been pro-posed in computer vision field. Counter-drones are crucial tools. Other urban datasets [48,37] constructed by 3D modelers are usually clean and complete, but the models inside generally lack geometric and textural details. (taken from 14 different cities separated by thousands of kilometers in China), environment (urban and country), objects (pedestrian, vehicles, bicycles, etc. 2: 604: November 13, 2022 Bani river project. The datasets are divided by their broad topic (natural phenomena, human-driven phenomena, build environment, others), using the same approach as the one The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. Author links open overlay panel Donglian Gu a, Ning Zhang a, Qianwen Shuai a, Zhen Xu a, Yongjia Xu b. Read the arxiv paper and checkout this repo. To download the whole dataset contact the Pix4D Support team (a valid license This study employed over 100 hours of high-altitude drone video data from eight intersections in Hohhot to generate a unique and extensive dataset encompassing high-density urban road intersections in China. AI algorithms are being integrated into various applications, such as drone delivery, 3D search-and-rescue, and disaster risk assessment, allowing drones to perform complex tasks autonomously. UrbanScene3D contains over 128k high-resolution images covering 16 scenes including large The development of drone and computer vision technologies has enabled automated landscape image analysis, unlocking new feature extraction capabilities. Compared to driving studies This repository contains a dataset for studying the accuracy of UWB-aided autonomous flight in UAVs, as well as ROS nodes to interface with Decawave's DWM1001 nodes when they are set as Active Tags or Passive Tags. Raw sensor data for vehicle navigation is presented in a file format. , Sweden [45] vs. The resulting dataset contains more than 11500 road users including vehicles, bicyclists and pedestrians at intersections in Germany and is called inD. This is a collection of drone image Dataset collected at Peking University, Huludao city, Henan University and Cangzhou city. View. 1. However, there is not yet a large-scale, high-quality, publicly available trajectory dataset for signalized intersections. The population risk and airspace availability were defined and measured to reflect complex urban environments using high-resolution datasets. This dataset serves as a valuable resource for benchmarking and training intelligent aerial agents, particularly in high-density urban road intersection dataset, captured by high-altitude drones, enables researchers to study urban traffic patterns in greater detail. For the safe and efficient deployment of unmanned aerial vehicles (UAVs) in complex urban landscapes, robust collision avoidance mechanisms are imperative. Rural and urban pastures from European geographies. The datasets linked from this page are hosted at data repositories such as datadryad. 5–15 m above the ground). The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an The development of computer vision algorithms for Unmanned Aerial Vehicles (UAVs) imagery heavily relies on the availability of annotated high-resolution aerial data. This data facilitates the development of strategies to better predict and manage urban traffic congestion(20). Our DroneVehicle collects 28,439 RGB-Infrared image pairs, covering urban roads, residential areas, Thus, there is an intense demand for trajectory datasets of traffic participants (TPs) in intersections. Request PDF | On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment | The new era of sharing information and “big data” has raised our Unmanned Aerial Vehicle (Rotary Wing Unmanned Aerial Vehicles) Drone corridors can be imagined as three dimensional highways in the sky, and are designed to support UAV operations. Finally, the proposed method provided better quality segmentation on two UAV-based datasets, NITRDrone and UDD, with an IOU of 82% and 71% compared to the baseline architectures of U-Net, FCN-8s, FC_DenseNet Urban Drones is your premier fishing and underwater drone superstore. The dataset for drone based detection and tracking is released, including both image/video, and annotations. UDD-5 + UDD This is a curated list of publicly available urban datasets, gathered over the years. Urban expansion, aging | Find, read and cite all the research you need on ResearchGate. Deep Learning-Based Semantic Segmentation of Urban Areas Using Heterogeneous Unmanned Aerial Vehicle Datasets. Note the dataset is available through the AWS Open-Data Program for free download; Understanding the RarePlanes Dataset and Building an Aircraft Detection Model-> blog post; Read this article from NVIDIA which discusses fine This repository contains datasets where a flying drone (hexacopter) is captured with multiple consumer-grade cameras (smartphones, compact cameras, gopro,) with highly accurate 3D drone trajectory ground truth recorderd by a This is the introductions of UrbanScene3D: A Large Scale Urban Scene Dataset and Simulator. ), and density (sparse and Although existing drone datasets have already provided high-quality vehicle trajectory data, This result suggests that the drone data is a better source for urban traffic forecasting, and the loop detector data can still provide complementary information when the drone data is only partially available. The drone surveys were conducted along Rheinstrasse in Munich, Germany, between Bonner Platz and Leopoldstrasse. The example showcases the variety and complexity of the data in the Figure 1: The MONET dataset is captured with a thermal camera mounted on a multirotor drone. This paper presents an integrated framework leveraging aerial drone data and machine learning for landscape imaging. An ML-processed dataset from six reforestation sites in Ecuador for estimating aboveground biomass Deep learning techniques have recently shown remarkable efficacy in the semantic segmentation of natural and remote sensing (RS) images. Information about drone imagery sets collected by the American Red Cross is available here. An automated CityNav Dataset: A pivotal resource for benchmarking and training intelligent aerial agents, the CityNav dataset enhances navigation performance by utilizing human-generated geo-aware trajectories. While existing datasets typically focus on urban scenes and are relatively small, our Varied Drone Dataset (VDD) addresses these limitations by offering a large-scale, densely labeled collection of 400 high-resolution images The benchmark dataset consists of 288 video clips formed by 261,908 frames and 10,209 static images, captured by various drone-mounted cameras, covering a wide range of aspects including location (taken from 14 To fill this gap, a few drone-based aerial datasets like Aeroscapes , urban drone dataset (UDD) , Semantic-DD have been developed and made public in the interest to work on the field of semantic segmentation. UrbanScene3D contains over 128k high-resolution images covering 16 scenes including large-scale The dataset was released in 2020 and main prize is for best open-source semantic segmentation model of building footprints from drone imagery that can generalize across a diverse range of African Positioning of unoccupied aerial systems (UAS, drones) is predominantly based on Global Navigation Satellite Systems (GNSS). Drone Photogrammetry-based Wind Field Simulation for Climate Adaptation in Urban Environments. We provide registered color, depth, and LiDAR data allowing the development of multi In this paper, we propose a multimodal synthetic dataset containing both images and 3D data taken at multiple flying heights to address these limitations. It is designed as a synthetic version of the Drone-vs-Bird dataset, which contains images of drones with birds acting as distractors. These are the Stanford Drone Dataset (SDD), the Vision Meets Drone (VisDrone) and Unmanned Aerial Vehicles Benchmark Object Detection and Tracking The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. In short, the UrbanNav dataset pose a special focus The inD dataset (Bock et al. The dataset contains images of variable resolution: 3000 × 4000 or 4096 × 2160. This article Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. The authors have used DJI Phantom 4 to collect remote sensing images by operating at 60 meters to 100 meters altitude. The proposed architecture is evaluated on the two UAV image datasets: Urban Drone Dataset (UDD) and NITRDrone Dataset. We present a large scale urban scene dataset associated with a handy simulator based on A raw dataset from urban California, USA, with ≈4k trees with forest structure variables (e. Six locations were covered by two drones each, as shown in Figure 1. A unique Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. In this paper, we We benchmarked KDP-Net on two publicly available UAV semantic segmentation datasets: the Urban Drone Dataset (UDD, ) and the Semantic Drone Dataset (SDD, ). The imagery depicts more than 20 houses from nadir views acquired at an altitude of 5 to 30 meters above the ground. The presented dataset captures features in urban environments (e. For the safe and efficient deployment of In the context of urban drones, we extended the above definition to also include persons involved with drones on a professional level, including (1) private sector members, such as aviation or robotics industry, (2) public sector institutions, such as governmental and nongovernmental organizations, and (3) academia, such as research institutions and However, there are only a few semantic segmentation datasets available for drone images captured at low altitudes (no higher than 120 meters). A high resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). This repository is the collection of SLAM-related datasets. One of the most complete databases for traffic research was created by the Next Generation SIMulation (NGSIM) initiative almost 15 years ago, with an objective of the development of algorithms and models for driver behavior at microscopic levels (NGSIM, 2006). e. Download scientific diagram | Publicly Available Drone based Image Datasets from publication: Aerial Data Aiding Smart Societal Reformation: Current Applications and Path Ahead | Nowadays, most of Drones with Birds (SDR) shown in figure LABEL:fig:methodology:drones_with_birds, is a variation of the drones-only dataset, with the addition of animated birds flying alongside the drones. With the rapid Automated vehicles rely heavily on data-driven methods, especially for complex urban environments. In this study, we propose a data-centric framework for a comprehensive assessment of airspaces for drone operations in urban environments. Skip to content. Author links open overlay panel Yuntao Wei, Xiujia Wang The effectiveness of the ESMS-YOLOv7 algorithm is validated through its performance on the DUT Anti-UAV dataset, where it exhibits superior capabilities relative to other leading The frequent illegal use of drones poses a serious threat to public security and property. We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61. The prerequisite for an effective counter-drone is to detect drones accurately. 93% and 73. Location Figure 1 – The six observation locations in the North of Munich (based on Google Earth). In addition to object-level annotations, In this paper, we propose a novel method to generate synthetic large-scale dataset for geometric and semantic urban scene understanding from bird’s eye view. The effect of drone motor speed sampling used when simulating drone micro-Doppler is shown to have a significant impact on VisDrone is a large-scale benchmark with carefully annotated ground-truth for various important computer vision tasks, to make vision meet drones. This seamless integration streamlines workflows for applications such as land surveys, urban planning, agriculture, and environmental monitoring. Moreover, drones equipped with cameras have quickly been deployed to a wide range of applications, starting from border security applications to Drones dataset. The UDD represents high-altitude scenarios during AI Studio是基于百度深度学习平台飞桨的人工智能学习与实训社区，提供在线编程环境、免费GPU算力、海量开源算法和开放数据，帮助开发者快速创建和部署模型。 In total, our dataset contains 72k labeled samples that allow for effective training of deep architectures showing promising results in synthetic-to-real adaptation. The imagery modal dataset providing drone imagery with a high-sampling rate at variable altitudes and view angles. This benchmark is released to seek for better solutions for UDD dataset, OA refers to Overall Accuracy (or pixelwise accuracy) and mIoU refers to mean Intersection over Union. It is important to note that existing drone datasets focus on urban scenes, while agricultural and industrial zones, mountains, and water are neglected. However, the scarcity of large-scale real datasets with pixel-level annotations poses a significant challenge to researchers as the limited number of images in existing datasets hinders the effectiveness of deep learning The proposed architecture is evaluated on the two UAV image datasets: Urban Drone Dataset (UDD) and NITRDrone Dataset. metropolis areas, complex buildings and residential areas). By contrast, the highway drone dataset (highD) has recently shown that drones are an efficient method for acquiring naturalistic road user trajectories. This dataset encapsulates a broad spectrum of backgrounds, camera angles, drone appearances, and weather conditions, Kaggle is the world’s largest data science community with powerful tools and resources to help you achieve your data science goals. com sales, support, repair parts and the fastest shipping of in The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. We are ardent supporters of open-science and release our datasets and code into public domain, except when restricted by source. Key benefits of combining GIS and drones: The encoder module here involves extracting the crucial scale-invariant features that are upsampled at the decoder stage to generate the predicted segmented images. The semantic labeling of the urban area is an essential but challenging task for a wide variety of applications such as mapping, navigation, and monitoring. In the VDD-Varied Drone Dataset, This paper introduces Toronto-3D, a large-scale urban outdoor point cloud dataset acquired by a MLS system in Toronto, Canada for semantic segmentation. The dataset, titled NU-AIR, features 70. 70Gb, 594 photos. The resolution is either 4k (4096 * Free road traffic datasets by TU Dresden – 800 minutes of drone video for each of the 3 data sets Brand new #Free and very unique #Datasets containing trajectory data from our #TrafficSurvey have been published by our Therefore, we propose the creation of a comprehensive, large-scale urban intersection dataset with naturalistic road user behavior using camera-equipped drones as successor of the highD dataset. This repository contains all the code and tools needed to build the "Syndrone" generated using the CARLA simulator. org, ieee Drone, I/Q Dataset, Propagation Measurements, Rural Urban, 2023. g. The effect of drone motor speed sampling used when simulating drone micro-Doppler is shown to have a significant impact on the accuracy of Our dataset contains drone images at different altitudes, with both low-altitude urban scenes and high-altitude field scenes. Our dataset contains: 33,763 simulated drone-view images, from multiple altitudes (80-650m), multiple attitudes, multiple scenes Urban Drone Dataset(UDD) for "Large-scale Structure from Motion with Semantic Constraints of Aerial Images", PRCV2018. The urban street views in these datasets exhibit substantial differences from VVI, Urban drone technology is rapidly evolving, driven by advancements in artificial intelligence (AI) that enhance operational efficiency and effectiveness. These datasets provide annotated images and videos captured from unmanned aerial vehicles (UAVs), enabling the training and evaluation of algorithms The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The presented data set captures features in urban environments Dependable visual drone detection is crucial for the secure integration of drones into the airspace. Thumbnail Figures from Complex Urban, NCLT, Oxford robotcar, KiTTi, Cityscapes datasets. It is extracted from 10 video sequences taken in 4 different cities in China. large. T rain: 3300. example of UDD： Class Definition. Towards a High-resolution Drone-based 3d Mapping Dataset to Optimise Flood Hazard Modelling. Object-level UAV datasets for urban scenarios Object-level drone datasets have a fundamental signif-icance in advancing research and development in various computer vision tasks. , 2021). [], [Project pageFigure 1: Overview of UrbanScene3D. However, in complex environments, GNSS signals are prone to interference, leading to flight instability. It succeeds to achieve an intersection over union (IoU) of and on the UDD and NITRDrone datasets, Drones have the potential to significantly improve the speed and efficiency of last-mile delivery by bypassing traffic and other ground-based logistics challenges. 2. In addition to object The Urban Drone dataset (UDD) Chen et al. , U-Net, PSPNet, Deelabv3+, SegNet, ESNet, and ERFNet, and validate the effectiveness of the proposed EDCP module via extensive ablation studies. The gap between the proposed datasets and real-world scenarios remains large. The dataset will be made publicly available to support the development of Detection and Semantic Segmentation of vehicles in drone aerial orthomosaics has applications in a variety of fields such as security, traffic and parking management, urban planning, logistics, and transportation, among It is important to note that existing drone datasets focus on urban scenes, while agricultural and industrial zones, mountains, and water are neglected. Three types of objects are annotated with bounding boxes: green is person, magenta is vehicle, and blue is We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction. \n. Curate this topic Add Different from the existing dataset, such as Waymo, KITTI, UrbanNav provide raw GNSS RINEX data. Due to potential signal disruptions, redundant positioning systems are needed for DDOS: The Drone Depth and Obstacle Segmentation Dataset. In this paper, we propose a multimodal synthetic dataset containing both images and 3D data taken at multiple flying heights to address these limitations. The dataset provides a wide variety of images taken from different locations, environments, and densities to facilitate the development of models for this task. Although several methodologies exist for drone detection, current solutions are suboptimal for long-range detection, primarily due to the scarcity of comprehensive training datasets. THE DATASET 3. The SinD dataset is collected by a drone at a typical signalized intersection and contains 13248 trajectories from vehicles and vulnerable road users (VRUs), such as pedestrians, cyclists, and The Drone Detection Dataset consists of 51446 train and 5375 test 640x480 RGB images presenting drones in different types, sizes, scales, positions, environments, times-of-day with corresponding XML labels set, prepared for Drones equipped with high-resolution cameras and advanced sensors can capture aerial imagery and convert it into georeferenced datasets for GIS platforms. A diverse dataset of drone-captured scenery spanning urban and rural A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. In contrast, remote sensing datasets like Loveda, GID, and MiniFrance [ 36 , 34 , 2 ] focus on land resource identification and utilization but lack the resolution and detail provided by low-altitude drones. Finally, we also provide firmware for reprogramming the DWM1001 DEV boards in order to obtain fast and accurate autopositioning of anchors. The Stanford Drone Dataset comprises of more than 100 different top-view scenes for a total of 20,000 targets engaged in various types of interactions. The symbols (does not apply) and (applies) indicate the presence of image sequences (4th column from the right), the inclusion of diverse drone models (3rd column from the right), and the incorporation of distractor objects (2nd The Urban Drone Dataset (UDD) is also a UAV image dataset, proposed by Chen et al. Explore the top 18 3D point cloud datasets for drone mapping and aerial surveying. The rapid advance in Light Detection and Ranging (LiDAR) systems provides this UrbanScene3D is a large scale urban scene dataset associated with a handy simulator based on Unreal Engine 4 and AirSim, which consists of both man-made and real-world reconstruction scenes in different scales, referred to as The primary goal of the Semantic Drone Dataset is to enhance the safety of autonomous drone flight and landing procedures through improved semantic comprehension of urban environments. RarePlanes-> incorporates both real and synthetically generated satellite imagery including aircraft. Multi-scenario traffic Simulator, which aims to provide a continuous simulation capability under the complex urban road network. GTA-UAV dataset provides a large continuous area dataset (covering 81. Our dataset, Urban Drone Dataset (UDD) Footnote 1, is collected by a professional-grade UAV (DJI-Phantom 4) at altitudes between 60 and 100 m. This paper presents a dataset recorded on-board a camera-equipped micro aerial vehicle flying within the urban streets of Zurich, Switzerland, at low altitudes (i. includes images over Beijing, Huludao, Zhengzhou, and Cangzhou (China) collected from images of a DJI Phantom 4 drone at flying heights of 60–100 m with resolutions of 4 K (4096 by 2160 pixels) and 12 M 2. The proposed model outperforms the considered state-of-the-art methods with an intersection of union of 76. Multiview Aerial Visual Recognition (MAVREC): Can Multi-view Improve Aerial Visual Dominant colors in sample frames of other state-of-the-art drone datasets MAVREC Toy Table 1: Comprehensive information on publicly available datasets for image-based drone detection, encompassing both real and synthetic data. Generating data for drone routing may support the optimization of drone delivery operations that could result to more efficient routes and thus impact positively parcel delivery process. Ozgur Ozdemir, North Carolina State University. This research has enhanced the YOLOUAV model to enable precise target recognition on unmanned aerial vehicle (UAV) datasets. To address these challenges, we present the DrIFT dataset, specifically developed for visual drone detection Therefore, we propose the creation of a comprehensive, large-scale urban intersection dataset with naturalistic road user behavior using camera-equipped drones as successor of the highD dataset. 65%, respectively. The scenarios have been specifically designed to fill the missing scenarios in literature: cyclists' lateral, bilateral, and crossing interactions with multiple road users, such as pedestrians, cyclists, and cars. Discover datasets optimized for terrain modeling, urban planning, disaster response, and more, with diverse formats and resolutions to suit every project. One of the most notable datasets is the CityNav dataset, which provides human-generated geo-aware trajectories that significantly improve navigation capabilities. This dataset is an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is five times the number of labeled points than the existing largest point cloud dataset. Everything reported in this repository refers to the work done in the paper: "SynDrone--Multi-modal UAV of views and routing choices. Drone Dataset [2] 3,611 door environments such as urban landscapes, forests, farmland, airports, and coastal areas across differ-ent regions around the globe (e. 3km 2 ) for UAV visual geo-localization, expanding the previously aligned drone-satellite pairs to arbitrary drone-satellite pairs to better align with real-world application scenarios. It contains 67,428 multi-modal video sequences and 119 subjects for action recognition, 22,476 frames for pose estimation, 41,290 frames and 1,144 . Help us build a catalog of datasets for testing OpenDroneMap. 39% and 86. The UAVid dataset To tackle this problem, we construct a large-scale drone-based RGB-Infrared vehicle detection dataset, termed DroneVehicle. Our dataset consists of The stable flight of drones relies on Global Navigation Satellite Systems (GNSS). also contains a few With the continuous advancement of the construction of smart cities, the availability of large-scale and semantically enriched datasets is essential for enhancing the machine’s ability to understand urban scenes. Inspired by cross-view machine learning, this paper introduces the VDUAV dataset and designs the VRLM network architecture, opening new avenues for cross-view geolocation. , 2020) is collected by drones with high-definition cameras at four recording urban intersections of various types in Aachen, Germany, from 2017 to 2019. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at Two UAV-image-based aerial image datasets: 1) NITRDrone dataset and 2) urban drone dataset (UDD), are considered to perform the experiment. Our dataset also contains flight heading angle information, which benefits subsequent related studies. Previous datasets included mostly single target but this dataset includes pedestrians, bikers, skaters, cars, and carts, all interacting based on common sense and social etiquette rules. UrbanDrones. With images captured at altitudes between mid and high altitudes, UDD provides a variety of urban scenes from four different cities in China. This dataset comprises This paper presents a novel long-range drone detection dataset, encompassing a set of different UAV types, flight patterns, and environmental conditions, and trained a state-of-the-art YOLO object detection algorithm, demonstrating the ability to identify drones at distances up to 60 meters with a high mean average precision. Diversity: The images encompass a wide range of scenarios, including urban and rural settings, The benchmark dataset consists of 288 video clips composed of 261,908 frames and 10,209 static photos collected by several drone-mounted cameras, encompassing a wide variety of features such as location (taken from 14 Our USC drone dataset comprises 30 video clips, all captured on the USC campus using a single drone model. IQ Data for a A. You are welcomed to contribute new results! Access the Drone Classification Dataset with high-resolution images of DJI Inspire, Mavic, Phantom drones, and non-drone scenes. 75 minutes of event footage acquired with a 640 x 480 resolution neuromorphic sensor mounted on a quadrotor operating in an urban environment. We present UrbanScene3D, a large-scale data platform for research of urban scene perception and reconstruction. Spanning various urban and suburban locales across 14 different cities Small target drone algorithm in low-altitude complex urban scenarios based on ESMS-YOLOv7. However, under the full The CityNav dataset is designed to enhance the performance of AI in drone navigation by providing human-generated geo-aware trajectories. A repository that contains a dataset contains a combination of 3 other datasets. Among various SLAM datasets, we've selected the datasets provide In this study, we propose a data-centric framework for a comprehensive assessment of airspaces for drone operations in urban environments. [50]. drone dataset (SDD) UA V bird-view 5–30 m. includes images over Beijing, Huludao, Zhengzhou, and Cangzhou (China) collected from images of a DJI Phantom 4 drone We demonstrate the effectiveness of this architecture on UAVid and Urban Drone datasets, where we achieved mIoU of 61. Researchers from other countries can use this dataset to study CitySim: A Drone-Based Vehicle Trajectory Dataset for Safety Oriented Research and Digital Twins. This dataset contains channel matrices and signal strength distribution in such a drone corridor located in the dense Semantic segmentation of drone images is critical for various aerial vision tasks as it provides essential semantic details to understand scenes on the ground. Still, it Munich dataset, which is described below. Mundhenk etal:[14] proposed a dataset named COWC (The Cars Overhead With Context) collected by drones, which includes 32,716 unique annotated cars and 58,247 unique negative examples. Discover how generative AI chatbots are transforming healthcare by improving patient care, enhancing efficiency, and Urban Drone Dataset (UDD) specializes in aiding 3D reconstruction tasks using an improved Structure From Motion (SFM) method. The VisDrone2019 dataset is collected by the AISKYEYE team at Lab of Machine The ICG Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures (Sun et al. Anomaly detection plays an increasingly important role in video surveillance and is one of the issues that have attracted various communities, such as computer vision, machine learning, and data mining in recent years. Add a description, image, and links to the drone-dataset topic page so that developers can more easily learn about it. Large datasets of real world measurement data in the form of road user trajectories are crucial for several tasks like road user prediction models or scenario-based safety validation. We add a new UAV dataset, UZH-FPV Drone Racing Dataset, which aims high speed state estimation using RGB, Event, and IMU. To tackle these problems, we present a large-scale urban scene dataset, Ur- They used the UAV-based image segmentation dataset NITRDrone Dataset and Urban Drone dataset (UDD) to evaluate the proposed approach. However, drone detection accuracy is significantly affected by domain shifts due to environmental changes, varied points of view, and background shifts. Jean-Thomas Camino The highD Dataset: A Drone Dataset of Naturalistic Vehicle Trajectories on German Highways for Validation of and KITTI mainly focus on urban traffic scenes, while KITTI . So far, though, this demand is unmet as no public dataset of urban road user This dataset provides LiDAR and stereo images with various position sensors targeting a highly complex urban environment. This dataset is essential for developing algorithms that can navigate urban landscapes effectively. This is a collection of drone image Dataset collected at Peking University, Huludao city, Henan University and Cangzhou city. no code yet • 19 Dec 2023 The advancement of autonomous drones, essential for sectors such as remote sensing and emergency services, is hindered by the absence of training datasets that fully capture the environmental challenges present in real-world scenarios, particularly operations in non though, this demand is unmet as no public dataset of urban road user trajectories is available in an appropriate size, quality and variety. 3. While Download Citation | On Oct 2, 2023, Giulia Rizzoli and others published SynDrone – Multi-modal UAV Dataset for Urban Scenarios | Find, read and cite all the research you need on ResearchGate The performance of the proposed method was evaluated on two publicly available Unmanned Aerial Vehicles (UAVs) semantic segmentation datasets; UAVid and Urban Drone Dataset (UDD-6) . As researchers, we stand on the shoulders of the community. example of UDD：\n \n This data set provides Light Detection and Ranging (LiDAR) data and stereo image with various position sensors targeting a highly complex urban environment. It succeeds to achieve an intersection over union (IoU) of 74 % and 84 % on the UDD and NITRDrone datasets, respectively, thus demonstrating better performance accuracy than the state-of-the-art methods. xatsn abrvv hmmos eyios wjnet tcid udmvcql eovy soq lvtx