Aerial View Vehicle Detection

## Overview

Aerial View Vehicle Detection is a dataset for object detection tasks - it contains Car Bus Mot Tren Arac Dnztasit annotations for 1,200 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Aerial Multi-Vehicle Detection Dataset: Efficient road traffic monitoring is playing a fundamental role in successfully resolving traffic congestion in cities. Unmanned Aerial Vehicles (UAVs) or drones equipped with cameras are an attractive proposition to provide flexible and infrastructure-free traffic monitoring. Due to the affordability of such drones, computer vision solutions for traffic monitoring have been widely used. Therefore, this dataset provide images that can be used for either training or evaluating Traffic Monitoring applications. More specifically, it can be used for training an aerial vehicle detection algorithm, benchmark an already trained vehicle detection algorithm, enhance an existing dataset and aid in traffic monitoring and analysis of road segments.

The dataset construction involved manually collecting all aerial images of vehicles using UAV drones and manually annotated into three classes 'Car', 'Bus', and ''Truck'.The aerial images were collected through manual flights in road segments in Nicosia or Limassol, Cyprus, during busy hours. The images are in High Quality, Full HD (1080p) to 4k (2160p) but are usually resized before training. All images were manually annotated and inspected afterward with the vehicles that indicate 'Car' for small to medium sized vehicles, 'Bus' for busses, and 'Truck' for large sized vehicles and trucks. All annotations were converted into VOC and COCO formats for training in numerous frameworks. The data collection took part in different periods, covering busy road segments in the cities of Nicosia and Limassol in Cyprus. The altitude of the flights varies between 150 to 250 meters high, with a top view perspective. Some of the images found in this dataset are taken from Harpy Data dataset [1]

The dataset includes a total of 9048 images of which 904 are split for validation, 905 for testing, and the rest 7239 for training.

    Subset
    Images
    Car
    Bus
    Truck


    Training
    7239
    200301
    1601
    6247


    Validation
    904
    23397 
    193 
    727


    Testing
    905
    24715
    208
    770

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

[1] Makrigiorgis, R., 2021. Harpy Data Dataset. [online] Kios.ucy.ac.cy. Available at: [Accessed 22 September 2022].

NOTE If you use this dataset in your research/publication please cite us using the following :

Rafael Makrigiorgis, Panayiotis Kolios, & Christos Kyrkou. (2022). Aerial Multi-Vehicle Detection Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7053442

Vehicle Detection From Satellite

## Overview

Vehicle Detection From Satellite is a dataset for object detection tasks - it contains Cars Trucks Vans annotations for 2,059 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Fernández-Andrés, J.; Sánchez-Soriano, J. Dataset: Roundabout Aerial Images for Vehicle Detection. Data 2022, 7, 47. https://doi.org/10.3390/data7040047

This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flows and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Roundabout (scenes)	Frames	Car	Truck	Cycle	Bus	Empty
1 (00001)	1,996	34,558	0	4229	0	0
2 (00002)	514	743	0	0	0	157
3 (00003-00017)	1,795	4822	58	0	0	0
4 (00018-00033)	1,027	6615	0	0	0	0
5 (00034-00049)	1,261	2248	0	550	0	81
6 (00050-00052)	5,501	180,342	1420	120	1376	0
7 (00053)	2,036	5,789	562	0	226	92
8 (00054)	1,344	1,733	222	0	150	222
Total	15,474	236,850	2,262	4,899	1,752	552
Data augmentation	x4	x4	x4	x4	x4	x4
Total	61,896	947,400	9048	19,596	7,008	2,208

Vehicle Detection In Aerial Images

## Overview

Vehicle Detection In Aerial Images is a dataset for object detection tasks - it contains Vehicles annotations for 2,758 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This repository has VME dataset (images and annotations files). Also, it has the script for constructing CDSI dataset. VME is a satellite imagery dataset built for vehicle detection in the Middle East. VME images (satellite_images folder) are under CC BY-NC-ND 4.0 license, whereas the rest of folders (annotations_HBB, annotations_OBB, CDSI_construction_scripts) are under CC BY 4.0 license. VME_CDSI_datasets.zip has four components: annotations_OBB: It holds TXT files in YOLO format with Oriented Bounding Box (OBB) annotations. Each annotation file is named after the corresponding image name annotations_HBB: This component contains HBB annotation files in JSON file formatted in MS-COCO format defined by four values in pixels (x_min, y_min, width, height) of training, validation, and test splits satellite_images: This folder consists of VME images of size 512x512 in PNG format CDSI_construction_scripts: This directory comprises all instructions needed to build the CDSI dataset in detail: a) instructions for downloading each dataset from its repository, b) The conversion to MS-COCO format script for each dataset is under the dataset name folder, c) The combination instructions. The training, validation, and test splits are available under "CDSI_construction_scripts/data_utils" folder. annotations_HBB, annotations_OBB, CDSI_construction_scripts, are available in our GitHub repository Please cite our dataset & paper with the preferred format as shown in the "Citation" section @article{al-emadi_vme_2025, title = {{VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond}}, volume = {12}, issn = {2052-4463}, url = {https://doi.org/10.1038/s41597-025-04567-y}, doi = {10.1038/s41597-025-04567-y}, pages = {500}, number = {1}, journal = {Scientific Data}, author = {Al-Emadi, Noora and Weber, Ingmar and Yang, Yin and Ofli, Ferda}, date = {2025-03-25}, publisher={Spring Nature}, year={2025} }

Unmanned Aerial Vehicles Dataset:

The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):

    Subset
    Images
    Drone


    Training
    768
    818


    Validation
    384
    402


    Testing
    383
    400

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

NOTE If you use this dataset in your research/publication please cite us using the following

Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569

This deep learning model is used to detect cars in high resolution drone or aerial imagery. Car detection can be used for applications such as traffic management and analysis, parking lot utilization, urban planning, etc. It can also be used as a proxy for deriving economic indicators and estimating retail sales. High resolution aerial and drone imagery can be used for car detection due to its high spatio-temporal coverage.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution RGB imagery (5 - 20 centimeter spatial resolution).OutputFeature class containing detected cars.Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.81.Training dataThis model has been trained on an Esri proprietary car detection dataset.Sample resultsHere are a few results from the model. To view more, see this story.

Aerial Car

## Overview

Aerial Car is a dataset for object detection tasks - it contains Car U2wA annotations for 1,584 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

VEDAI dataset categories information.

This is an object detection dataset for small object detection from aerial images. All the annotations have been pre-processed to YOLO-format.

There are 3 classes in this dataset: airplane, ship, vehicle.

Here's the dataset split:

• Train: 2095 images
• Test: 450 images
• Valid: 450 images

Small Object Aerial Person Detection Dataset:

The aerial dataset publication comprises a collection of frames captured from unmanned aerial vehicles (UAVs) during flights over the University of Cyprus campus and Civil Defense exercises. The dataset is primarily intended for people detection, with a focus on detecting small objects due to the top-view perspective of the images. The dataset includes annotations generated in popular formats such as YOLO, COCO, and VOC, making it highly versatile and accessible for a wide range of applications. Overall, this aerial dataset publication represents a valuable resource for researchers and practitioners working in the field of computer vision and machine learning, particularly those focused on people detection and related applications.

Subset	Images	People
Training	2092	40687
Validation	523	10589
Testing	521	10432

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

Vehicle Segmentation In Aerial Images

## Overview

Vehicle Segmentation In Aerial Images is a dataset for semantic segmentation tasks - it contains Vehicles annotations for 2,662 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Context

As a base to create object detection models, this dataset focus on aerial images of urban areas. It contains 187.138 images of 4 object class and over 4.112.482 annotated objects, being that a object annotation is on a YOLOv5 type.

Content

The dataset is a combination of 3 others dataset, being them Stanford Drone Dataset, Vision Meets Drones, and Umanned Unmanned Aerial Vehicles Benchmark Object Detection and Tracking.

Object Classes

The dataset has the following objects of interest: person, small vehicle, medium vehicle and large vehicle.

Acknowledgements

@inproceedings{robicquet2016learning, title={Learning social etiquette: Human trajectory understanding in crowded scenes}, author={Robicquet, Alexandre and Sadeghian, Amir and Alahi, Alexandre and Savarese, Silvio}, booktitle={European conference on computer vision}, pages={549--565}, year={2016}, organization={Springer} }

@inproceedings{du2018unmanned, title={The unmanned aerial vehicle benchmark: Object detection and tracking}, author={Du, Dawei and Qi, Yuankai and Yu, Hongyang and Yang, Yifan and Duan, Kaiwen and Li, Guorong and Zhang, Weigang and Huang, Qingming and Tian, Qi}, booktitle={Proceedings of the European Conference on Computer Vision (ECCV)}, pages={370--386}, year={2018} }

@article{zhu2020vision, title={Vision meets drones: Past, present and future}, author={Zhu, Pengfei and Wen, Longyin and Du, Dawei and Bian, Xiao and Hu, Qinghua and Ling, Haibin}, journal={arXiv preprint arXiv:2001.06303}, year={2020} }

Unmanned Aerial Vehicles (UAV) provide increased access to unique types of urban imagery traditionally not available. Advanced machine learning and computer vision techniques when applied to UAV RGB image data can be used for automated extraction of building asset information and if applied to UAV thermal imagery data can detect potential thermal anomalies. However, these UAV datasets are not easily available to researchers, thereby creating a barrier to accelerating research in this area.

To assist researchers with added data to develop machine learning algorithms, we present UAVID3D (Unmanned Aerial Vehicle (UAV) Image Dataset of the Built Environment for 3D reconstruction). The raw images for our dataset were recorded with a Zenmuse XT2 visual (RGB) and a FLIR Tau 2 (thermal, https://flir.netx.net/file/asset/15598/original/) camera on a DJI Mavic 2 pro drone (https://www.dji.com/matrice-200-series). The thermal camera is factory calibrated. All data is organized and structured to comply with FAIR principles, i.e. being findable, accessible, interoperable, and reusable. It is publicly available and can be downloaded from the Zenodo data repository.

RGB images were recorded during UAV fly-overs of two different commercial buildings in Northern California. In addition, thermographic images were recorded during 2 subsequent UAV fly-overs of the same two buildings. UAV flights were recorded at flight heights between 60–80 m above ground with a flight speed of 1 m s and contain GPS information. All images were recorded during drone flights on May 10, 2021 between 8:45 am and 10:30 am and on May 19, 2021 between 2:15 pm and 4:30 pm. Outdoor air temperatures on these two days during the flights were between 78 and 83 degree fahrenheit and between 58 and 65 degree fahrenheit respectively.

For the RGB flights, UAV path was planned and captured using an orbital flight plan in PIX4D capture at normal flight speed and overlap angle of 10 degree. Thermal images were captured by manual flights approximately 5 m away from each building facade. Due to the high overlap of images, similarities from feature points identified in each image can be extracted to conduct photogrammetry. Photogrammetry allows estimation of the three-dimensional coordinates of points on an object in a generated 3D space involving measurements made on images taken with a high overlap rate. Photogrammetry can be used to create a 3D point cloud model of the recorded region. UAVID3D dataset is a series of compressed archive files totaling 21GB. Useful pipelines to process these images can be found at these two repositories https://github.com/LBNL-ETA/a3dbr, and https://github.com/LBNL-ETA/AutoBFE

This work was supported by the Assistant Secretary for Energy Efficiency and Renewable Energy, Building Technologies Program, of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231.

The PaCaBa (Parking Cars Barcelona) dataset is a WorldView-3 stereo satellite image dataset with labeled parking cars. It consists of three parts:

Raw geotiff images with polygon annotations of cars.

Image patches of size 540x540 with rotated bounding box annotations of parking cars. This part is suitable for training and testing of a parking cars detector.

Image patches of size 540x540 with rotated bounding box annotations of cars (either moving or static) in both images. This part is again suitable for training and testing, but for the task of general car detection in satellite images.

The image data is available for four connected regions in the city of Barcelona covering roughly 5 km2. The four regions of interest include all street areas whereas other areas are masked out in the images. Annotation of these areas has given 12088 and 12248 cars in the individual stereo images, respectively, and 7303 parking cars.

DOTA dataset categories information.

Satellite Car Detection

## Overview

Satellite Car Detection is a dataset for object detection tasks - it contains Car annotations for 499 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

F1-Measure on VEDAI.

Custom Multi-Altitude Aerial Vehicles Dataset:

Created for publishing results for ICUAS 2023 paper "How High can you Detect? Improved accuracy and efficiency at varying altitudes for Aerial Vehicle Detection", following the abstract of the paper.

Abstract—Object detection in aerial images is a challenging task mainly because of two factors, the objects of interest being really small, e.g. people or vehicles, making them indistinguishable from the background; and the features of objects being quite different at various altitudes. Especially, when utilizing Unmanned Aerial Vehicles (UAVs) to capture footage, the need for increased altitude to capture a larger field of view is quite high. In this paper, we investigate how to find the best solution for detecting vehicles in various altitudes, while utilizing a single CNN model. The conditions for choosing the best solution are the following; higher accuracy for most of the altitudes and real-time processing ( > 20 Frames per second (FPS) ) on an Nvidia Jetson Xavier NX embedded device. We collected footage of moving vehicles from altitudes of 50-500 meters with a 50-meter interval, including a roundabout and rooftop objects as noise for high altitude challenges. Then, a YoloV7 model was trained on each dataset of each altitude along with a dataset including all the images from all the altitudes. Finally, by conducting several training and evaluation experiments and image resizes we have chosen the best method of training objects on multiple altitudes to be the mixup dataset with all the altitudes, trained on a higher image size resolution, and then performing the detection using a smaller image resize to reduce the inference performance. The main results

The creation of a custom dataset was necessary for altitude evaluation as no other datasets were available. To fulfill the requirements, the footage was captured using a small UAV hovering above a roundabout near the University of Cyprus campus, where several structures and buildings with solar panels and water tanks were visible at varying altitudes. The data were captured during a sunny day, ensuring bright and shadowless images. Images were extracted from the footage, and all data were annotated with a single class labeled as 'Car'. The dataset covered altitudes ranging from 50 to 500 meters with a 50-meter step, and all images were kept at their original high resolution of 3840x2160, presenting challenges for object detection. The data were split into 3 sets for training, validation, and testing, with the number of vehicles increasing as altitude increased, which was expected due to the larger field of view of the camera. Each folder consists of an aerial vehicle dataset captured at the corresponding altitude. For each altitude, the dataset annotations are generated in YOLO, COCO, and VOC formats. The dataset consists of the following images and detection objects:

    Data
    Subset
    Images
    Cars


    50m
    Train
    130
    269


    50m
    Test
    32
    66


    50m
    Valid
    33
    73


    100m
    Train
    246
    937


    100m
    Test
    61
    226


    100m
    Valid
    62
    250


    150m
    Train
    244
    1691


    150m
    Test
    61
    453


    150m
    Valid
    61
    426


    200m
    Train
    246
    1753


    200m
    Test
    61
    445


    200m
    Valid
    62
    424


    250m
    Train
    245
    3326


    250m
    Test
    61
    821


    250m
    Valid
    61
    823


    300m
    Train
    246
    6250


    300m
    Test
    61
    1553


    300m
    Valid
    62
    1585


    350m
    Train
    246
    10741


    350m
    Test
    61
    2591


    350m
    Valid
    62
    2687


    400m
    Train
    245
    20072


    400m
    Test
    61
    4974


    400m
    Valid
    61
    4924


    450m
    Train
    246
    31794


    450m
    Test
    61
    7887


    450m
    Valid
    61
    7880


    500m
    Train
    270
    49782


    500m
    Test
    67
    12426


    500m
    Valid
    68
    12541


    mix_alt
    Train
    2364
    126615


    mix_alt
    Test
    587
    31442


    mix_alt
    Valid
    593
    31613

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).