This database gives an overview of the specifications of 93 unmanned aerial vehicles (UAVs). This database can be used to characterize different types of UAVs and their properties. The database contains the following information for each UAV: - Maximum takeoff weight (MTOW) - Payload - Wingspan - Lenght - Cruise speed - Maximum speed - Stall speed - Range - Endurance - Altitude - Airfoil - Aspect ratio - Chord length (estimated) - Propeller diameter - UAV type

This experiment was performed in order to empirically measure the energy use of small, electric Unmanned Aerial Vehicles (UAVs). We autonomously direct a DJI ® Matrice 100 (M100) drone to take off, carry a range of payload weights on a triangular flight pattern, and land. Between flights, we varied specified parameters through a set of discrete options, payload of 0 , 250 g and 500 g; altitude during cruise of 25 m, 50 m, 75 m and 100 m; and speed during cruise of 4 m/s, 6 m/s, 8 m/s, 10 m/s and 12 m/s. We simultaneously collect data from a broad array of on-board sensors. The onboard sensors used to collect these data are* Wind sensor: FT Technologies FT205 UAV-mountable, pre-calibrated ultrasonic wind sensor with accuracy of ± 0.1 m/s and refresh rate of 10 Hz.;* Position: 3DM-GX5-45 GNSS/INS sensor pack. These sensors use a built-in Kalman filtering system to fuse the GPS and IMU data. The sensor has a maximum output rate of 10Hz with accuracy of ± 2 m$ RMS horizontal, ± 5 m$ RMS vertical.* Current and Voltage: Mauch Electronics PL-200 sensor. This sensor can record currents up to 200 A and voltages up to 33 V. Analogue readings from the sensor were converted into a digital format using an 8 channel 17 bit analogue-to-digital converter (ADC).Data syncing and recording was handled using the Robot Operating System (ROS) running on a low-power Raspberry Pi Zero W. Data was recorded on the Raspberry Pi's microSD card. The data provided by each sensor were synchronized to a frequency of approximately 5Hz using the ApproximateTime message filter policy of Robot Operating System (ROS). The number of flights performed varying operational parameters (payload, altitude, speed) was 196. In addition, 13 recordings were done to assess the drone’s ancillary power and hover conditions.

STAQS_Drone_Data is the PM 2.5 data collected by the BlueHalo E900 UAV during the Synergistic TEMPO Air Quality Science (STAQS) mission. Data collection for this product is complete.Launched in April 2023, NASA’s Tropospheric Emissions: Monitoring of Pollution (TEMPO) satellite monitors major air pollutants across North America every daylight hour at high spatial resolution at a geostationary orbit (GEO). With these measurements, NASA’s STAQS mission seeks to integrate TEMPO satellite observations with traditional air quality monitoring to improve understanding of air quality science. STAQS is being conducted during summer 2023, targeting urban areas, including Los Angeles, New York City, and Chicago. As part of the mission two aircraft will be outfitted with various remote sensing payloads. The Johnson Space Center (JSC) Gulfstream-V (G-V) aircraft will feature the GeoCAPE Airborne Simulator (GCAS) and combined High Spectral Resolution Lidar-2 (HSRL-2) and Ozone Differential Absorption Lidar (DIAL). This payload provides repeated high-resolution mapping of NO2, HCHO, ozone, and aerosols up to 3x per day over targeted cities. NASA Langley Research Center’s (LaRC’s) Gulfstream-III will measure city-scale emissions 2x per day over the targeted cities with the High-Altitude Lidar Observatory (HALO) and Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRS-NG). STAQS will also incorporate ground-based tropospheric ozone profiles from the NASA Tropospheric Ozone Lidar Network (TOLNet), NO2, HCHO, and ozone measurements from Pandora spectrometers, and will leverage existing networks operated by the EPA and state air quality agencies. The primary goal of STAQS is to improve our current understanding of air quality science under the TEMPO field of regard. Further goals include evaluating TEMPO level 2 data products, interpreting the temporal and spatial evolution of air quality events tracked by TEMPO, improving temporal estimates of anthropogenic, biogenic, and greenhouse gas emissions, and assessing the benefit of assimilating TEMPO data into chemical transport models.

Drone Data Management Market Outlook

The global drone data management market size is poised to grow significantly, with estimations projecting a rise from $1.5 billion in 2023 to approximately $10.2 billion by 2032, reflecting a robust CAGR of 24.5%. This impressive growth can be attributed to the increasing adoption of drones across various industries for data collection and analysis, underscoring their transformative impact on operational efficiency and decision-making processes.

One of the primary growth factors driving the drone data management market is the rapid advancement in drone technology. The integration of sophisticated sensors, high-resolution cameras, and advanced navigation systems has significantly enhanced the capabilities of drones, making them indispensable tools for data acquisition in sectors such as agriculture, construction, and environmental monitoring. Additionally, the advent of machine learning algorithms and artificial intelligence has further amplified the utility of drones, enabling more accurate data analysis and predictive insights.

Another key driver of market growth is the rising demand for real-time data analytics. In today's fast-paced world, industries are increasingly relying on timely and precise data to make informed decisions. Drones, equipped with state-of-the-art data management software, can provide real-time analytics, which is crucial for applications such as disaster management, precision farming, and infrastructure inspection. This capability not only enhances operational efficiency but also reduces costs associated with manual data collection and analysis.

The growing focus on sustainability and environmental conservation is also propelling the drone data management market. Drones are being extensively used for environmental monitoring, helping to track changes in ecosystems, wildlife populations, and natural resources. By providing accurate and comprehensive data, drones enable researchers and policymakers to devise effective conservation strategies. Additionally, the use of drones in sectors like agriculture and utilities contributes to more sustainable practices by optimizing resource use and minimizing environmental impact.

The concept of Drones As A Service (DaaS) is gaining traction as businesses seek to leverage drone technology without the burden of ownership and maintenance. This model allows companies to access the latest drone technologies and services on a subscription basis, enabling them to focus on their core operations while benefiting from the data and insights provided by drones. DaaS providers offer a range of services, from data collection and analysis to drone operation and maintenance, catering to the specific needs of different industries. This approach not only reduces the upfront costs associated with drone acquisition but also ensures that businesses have access to the most advanced and up-to-date drone technologies. As a result, DaaS is becoming an attractive option for organizations looking to integrate drone technology into their operations efficiently and cost-effectively.

Regionally, North America dominates the drone data management market, driven by the presence of leading technology companies and substantial investments in research and development. The region's robust regulatory framework and widespread adoption of drones across various sectors further contribute to its market leadership. However, the Asia Pacific region is expected to witness the highest growth rate, owing to the increasing adoption of drones in agriculture, construction, and infrastructure development. Countries like China and India are investing heavily in drone technology to enhance productivity and address various socio-economic challenges.

Component Analysis

The drone data management market is segmented into software, hardware, and services, each playing a crucial role in the ecosystem. The software segment encompasses a wide range of applications, including data processing, analysis, and visualization tools. These software solutions are essential for transforming raw data collected by drones into actionable insights. With the continuous advancements in artificial intelligence and machine learning, software solutions are becoming more sophisticated, providing users with predictive analytics and real-time decision-making capabilities. Companies are increasingly investing in developing custom software solutions tailored to specific industry needs, further driving growth in this segment

This is RF drone database.

The global Drone Data Services market size was estimated to be USD XXX million in 2025 and is projected to grow at a CAGR of XX% from 2025 to 2033. Rising demand for drones for aerial data collection and processing in various industries, including construction, agriculture, and mining, is driving market expansion. Additionally, advancements in drone technology, such as improved camera capabilities, longer flight times, and automated data analysis, are enhancing the efficiency and accuracy of data collection. The market is segmented by application and type. In terms of application, the market is divided into mapping and surveying, infrastructure inspection, agriculture, and others. In terms of type, the market is segmented into fixed-wing drones, multi-rotor drones, and hybrid drones. North America currently holds a significant market share, but emerging markets in Asia-Pacific and Latin America are expected to exhibit strong growth in the coming years. Key players in the industry include PrecisionHawk, DroneDeploy, DroneCloud, 4DMapper, Sentera, Pix4D, Skycatch, Dronifi, Airware, and Agribotix.

Drone Data Collection Service Market Outlook

The global market size for drone data collection services was valued at approximately USD 5.5 billion in 2023 and is projected to reach USD 21.4 billion by 2032, growing at a robust CAGR of 16.1% during the forecast period. This significant growth can be attributed to the increasing demand for advanced data analytics and the need for efficient data collection methods across various industries.

One of the major growth factors driving this market is the rapid advancement in drone technology. Innovations in drone hardware and software have significantly enhanced the capabilities of drones, making them more versatile and efficient in data collection tasks. Drones are now equipped with high-resolution cameras, LIDAR, and other advanced sensors that provide accurate and detailed data, which is invaluable for many industries. Additionally, improvements in battery life and flight stability have extended the operational range and endurance of drones, making them more practical for prolonged and large-scale data collection missions.

Another critical factor fueling the market's growth is the increasing adoption of drones in various applications such as agriculture, construction, mining, and oil & gas. In agriculture, drones are used for precision farming, crop monitoring, and soil analysis, which help in optimizing yields and reducing costs. Similarly, in construction, drones are utilized for site surveying, progress monitoring, and safety inspections, which enhance project efficiency and safety. The mining industry also benefits from drone data collection for exploration, mapping, and monitoring of mining operations, ensuring better resource management and operational safety.

The regulatory environment is another significant driver of market growth. Many countries are developing and implementing regulations that facilitate the integration of drones into commercial operations. These regulations are aimed at ensuring the safe and efficient use of drones while addressing privacy and security concerns. For instance, the Federal Aviation Administration (FAA) in the United States has established comprehensive guidelines for commercial drone operations, which have encouraged businesses to adopt drone technology for various data collection purposes.

Regionally, the North American market is expected to dominate the global drone data collection service market, followed by Europe and Asia Pacific. North America’s dominance can be attributed to the presence of major drone technology companies, a favorable regulatory environment, and high adoption rates across various industries. The Asia Pacific region, with its rapidly growing economies and increasing investments in drone technology, is projected to witness the highest growth rate during the forecast period. Europe is also expected to see significant growth, driven by technological advancements and increasing demand for efficient data collection methods in industries such as agriculture and construction.

Service Type Analysis

The drone data collection service market can be segmented by service type into aerial photography, mapping & surveying, inspection & monitoring, and others. Aerial photography is one of the most commonly used services in this market. High-resolution aerial photographs captured by drones are utilized in various industries, including real estate, tourism, and media. These photographs provide detailed and accurate visual data that can be used for marketing, planning, and documentation purposes. The advancements in camera technology and drone stability have further enhanced the quality and reliability of aerial photography.

Mapping & surveying is another critical segment in the drone data collection service market. Drones equipped with LIDAR, photogrammetry, and other advanced sensors are used to create detailed and accurate maps and surveys of large areas. This service is particularly beneficial in industries such as construction, mining, and agriculture, where precise data is crucial for planning and operational efficiency. The use of drones in mapping & surveying reduces the time and cost associated with traditional ground-based survey methods while providing high-quality and comprehensive data.

Inspection & monitoring services provided by drones are increasingly being adopted in industries such as utilities, oil & gas, and infrastructure. Drones are used to inspect and monitor assets such as power lines, pipelines, and bridges, ensuring their integrity and safety. The ability of drones to acce

Global Drone Data Services Market was valued at USD 1.48 Billion in 2023 and is anticipated to project robust growth in the forecast period with a CAGR of 17.5% through 2029F.

DroneRF dataset: a radio frequency (RF) based dataset of drones functioning in different modes, including off, on and connected, hovering, flying, and video recording. The dataset contains recordings of RF activities, composed of 227 recorded segments collected from 3 different drones, as well as recordings of background RF activities with no drones. The data has been collected by RF receivers that intercepts the drone’s communications with the flight control module. The receivers are connected to two laptops, via PCIe cables, that runs a program responsible for fetching, processing and storing the sensed RF data in a database. The dataset can be used in drone detection, drone identification and drone tracking.

Global Drone Data Services Market will grow at a CAGR of 30.30% during the forecast period, with an estimated size and share crossing USD 15,386.50 million by 2032.

The data was collected with a Wingtra Gen II drone and a Sony RX1R II sensor. In total, 10 flights were conducted at different dates, both in summer and winter. A DSM, an orthophoto, a snow depth raster and the original drone images from every flight are available at a high resolution (10cm and 3cm, respectively).

The global Agricultural Drone Data Analytics Solutions Market is projected to reach USD 4361.51 million by 2034 With at a CAGR of 25.0% during the forecast period.

The drone data management market is experiencing robust growth, driven by the increasing adoption of drones across diverse sectors like agriculture, construction, and infrastructure monitoring. The market's expansion is fueled by the need for efficient and secure solutions to process, analyze, and store the massive amounts of data generated by these unmanned aerial vehicles (UAVs). This data, comprising high-resolution imagery, LiDAR scans, and sensor readings, offers invaluable insights for informed decision-making, optimizing operations, and improving overall efficiency. The market is witnessing a shift towards cloud-based solutions, facilitating easier data sharing and collaborative workflows. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are empowering more sophisticated data analysis, leading to the development of automated reporting and predictive analytics capabilities. We estimate the market size to be approximately $2.5 billion in 2025, with a Compound Annual Growth Rate (CAGR) of 18% projected through 2033. This growth is attributed to factors such as increasing affordability of drones, improved data processing technologies, and expanding regulatory frameworks promoting drone usage. However, challenges remain. Data security and privacy concerns pose significant hurdles, particularly for applications handling sensitive information. The need for robust data management infrastructure, including high-bandwidth connectivity and scalable storage solutions, adds to the cost of implementation. Furthermore, the lack of standardized data formats and interoperability issues can complicate data integration and analysis. Nevertheless, ongoing technological advancements and growing industry collaboration are steadily addressing these challenges, paving the way for sustained market expansion. Key players like Remote GeoSystems, DroneDeploy, and Pix4Dcapture are actively shaping the market through innovative software and service offerings, fostering competition and accelerating the adoption of drone data management solutions. The market segmentation reflects the diverse applications of drone data across various industries, further illustrating its potential for future growth.

The drone data collection service market is experiencing robust growth, driven by increasing demand across various sectors. The market's expansion is fueled by technological advancements leading to higher-resolution imagery, improved data processing capabilities, and more affordable drone technology. Industries like construction, agriculture, and infrastructure are increasingly adopting drone-based data collection for tasks such as site surveying, progress monitoring, crop health assessment, and pipeline inspection. This shift towards efficient and cost-effective data acquisition methods is a primary driver. The market is segmented by application (e.g., surveying, mapping, inspection), drone type (e.g., fixed-wing, rotary-wing), and end-user industry. While the initial investment in drones and specialized software can be a barrier for some, the long-term cost savings and efficiency gains are significant, overcoming this hurdle for many organizations. Competition is intensifying among established players and emerging companies, leading to innovation in data processing algorithms and service offerings. Looking ahead, the market is poised for continued expansion. Factors contributing to future growth include the increasing integration of AI and machine learning in data analysis, the development of more autonomous drone systems, and regulatory developments facilitating broader drone usage. The global adoption of 5G and improved communication infrastructure will further enhance real-time data transfer and processing capabilities. Although potential restraints such as stringent regulations in certain regions and concerns about data security and privacy could moderate growth, the overall market trajectory remains strongly positive. The presence of numerous companies, including both established players like Atkins and emerging specialists like Hivemapper, reflects the vibrant and competitive nature of this rapidly evolving market. The market is expected to see continued consolidation as larger companies acquire smaller, specialized firms to expand their service portfolios.

The Drone Data Link System market is experiencing robust growth, driven by increasing demand for enhanced communication capabilities in both military and civil applications. The market's expansion is fueled by several key factors. Firstly, the proliferation of drones across various sectors – from surveillance and delivery to agriculture and infrastructure inspection – necessitates reliable and secure data transmission. Secondly, technological advancements in data link technologies, including higher bandwidth, improved range, and enhanced security features, are driving adoption. Thirdly, government initiatives promoting drone technology and infrastructure development are further stimulating market growth. The market is segmented by application (military and civil) and type (hardware and software systems). While the military segment currently holds a larger share, the civil segment is exhibiting faster growth, driven by expanding commercial applications. Leading companies like AeroVironment, Elbit Systems, and DJI are actively investing in R&D and strategic partnerships to consolidate their market positions. The North American and European regions currently dominate the market, though the Asia-Pacific region is expected to witness significant growth in the coming years due to increasing drone adoption and supportive government policies in countries like China and India. Challenges such as regulatory hurdles, cybersecurity concerns, and the need for interoperability among different systems represent potential restraints on market expansion. Despite these restraints, the long-term outlook for the Drone Data Link System market remains positive. The projected Compound Annual Growth Rate (CAGR) suggests substantial market expansion throughout the forecast period. The increasing sophistication of drone technologies and their integration into various aspects of life will continue to drive demand for advanced data link solutions. Companies are focusing on developing more robust, secure, and cost-effective systems to meet the evolving needs of their clients. The market's continued growth is further underpinned by the ongoing development of 5G and beyond 5G network technologies, which are expected to provide even greater bandwidth and speed for drone data transmission. Furthermore, the rise of artificial intelligence and machine learning is improving the efficiency and capabilities of data link management systems.

Description Welcome to the Drone-Based Malware Detection dataset! This dataset is designed to aid researchers and practitioners in exploring innovative cybersecurity solutions using drone-collected data. The dataset contains detailed information on network traffic, drone sensor readings, malware detection indicators, and environmental conditions. It offers a unique perspective by integrating data from drones with traditional network security metrics to enhance malware detection capabilities.

Dataset Overview The dataset comprises four main categories:

Network Traffic Data: Captures network traffic attributes including IP addresses, ports, protocols, packet sizes, and various derived metrics. Drone Sensor Data: Includes GPS coordinates, altitude, speed, heading, battery level, and other sensor readings from drones. Malware Detection Data: Contains indicators and scores relevant to detecting malware, such as anomaly scores, suspicious IP counts, reputation scores, and attack types. Environmental Data: Provides context through environmental conditions like location type, noise level, weather conditions, and more. Files and Features The dataset is divided into four separate CSV files:

network_traffic_data.csv

timestamp: Date and time of the traffic event. source_ip: Source IP address. destination_ip: Destination IP address. source_port: Source port number. destination_port: Destination port number. protocol: Network protocol (TCP, UDP, ICMP). packet_length: Length of the network packet. payload_data: Content of the packet payload. flag: Network flag (SYN, ACK, FIN, RST). traffic_volume: Volume of traffic in bytes. flow_duration: Duration of the network flow. flow_bytes_per_s: Bytes per second for the flow. flow_packets_per_s: Packets per second for the flow. packet_count: Number of packets in the flow. average_packet_size: Average size of packets. min_packet_size: Minimum packet size. max_packet_size: Maximum packet size. packet_size_variance: Variance in packet sizes. header_length: Length of the packet header. payload_length: Length of the packet payload. ip_ttl: Time to live for the IP packet. tcp_window_size: TCP window size. icmp_type: ICMP type (echo_request, echo_reply, destination_unreachable). dns_query_count: Number of DNS queries. dns_response_count: Number of DNS responses. http_method: HTTP method (GET, POST, PUT, DELETE). http_status_code: HTTP status code (200, 404, 500, 301). content_type: Content type (text/html, application/json, image/png). ssl_tls_version: SSL/TLS version. ssl_tls_cipher_suite: SSL/TLS cipher suite. drone_data.csv

latitude: Latitude of the drone. longitude: Longitude of the drone. altitude: Altitude of the drone. speed: Speed of the drone. heading: Heading of the drone. battery_level: Battery level of the drone. drone_id: Unique identifier for the drone. flight_time: Total flight time. signal_strength: Strength of the drone's signal. temperature: Temperature at the drone's location. humidity: Humidity at the drone's location. pressure: Atmospheric pressure at the drone's location. wind_speed: Wind speed at the drone's location. wind_direction: Wind direction at the drone's location. gps_accuracy: Accuracy of the GPS signal. malware_detection_data.csv

anomaly_score: Score indicating the level of anomaly detected. suspicious_ip_count: Number of suspicious IP addresses detected. malicious_payload_indicator: Indicator for malicious payload (0 or 1). reputation_score: Reputation score for the network entity. behavioral_score: Behavioral score indicating potential malicious activity. attack_type: Type of attack (DDoS, phishing, malware). signature_match: Indicator for signature match (0 or 1). sandbox_result: Result from sandbox analysis (clean, infected). heuristic_score: Heuristic score for potential threats. traffic_pattern: Pattern of the traffic (burst, steady). environmental_data.csv

location_type: Type of location (urban, rural). nearby_devices: Number of nearby devices. signal_interference: Level of signal interference. noise_level: Noise level in the environment. time_of_day: Time of day (morning, afternoon, evening, night). day_of_week: Day of the week. weather_conditions: Weather conditions (sunny, rainy, cloudy, stormy). Usage and Applications This dataset can be used for:

Cybersecurity Research: Developing and testing algorithms for malware detection using drone data. Machine Learning: Training models to identify malicious activity based on network traffic and drone sensor readings. Data Analysis: Exploring the relationships between environmental conditions, drone sensor data, and network traffic anomalies. Educational Purposes: Teaching data science, machine learning, and cybersecurity concepts using a comprehensive and multi-faceted dataset.

Acknowledgements This dataset is based on real-world data collected from drone sensors and network traffic monitoring s...

The Drone Data Services Market size was valued at USD 1.50 billion in 2023 and is projected to reach USD 15.04 billion by 2032, exhibiting a CAGR of 39.0 % during the forecasts period. The Drone Data Services Market relates to the employment of UAVs to obtain, process, and supply information to numerous areas. These services utilise high resolution imaging, LiDAR and GPS technologies to obtain actual data in real time thus helping organisational decision-making. Some of the uses are crop and field mapping, construction planning and progress check, check on infrastructures (bridges, power lines), and checking on wildlife and natural disasters. Recent trends include the use of artificial intelligence and machine learning in the monitoring and analysis of data, improvement in the flight duration of drones, improvement in the sensors of the drones, and the proliferation of norms that approve the use of commercial drones. Arguably, this is an indication of the fact that as industries look for ways and means of achieving efficiency and innovation in their operations, drone data service is equally being sought after. Recent developments include: In April 2023, AZUR DRONES, a European provider of automated drone solutions, is expanding its product portfolio and embracing new possibilities by introducing SKEYETECH E2. This innovative platform offers various applications and is designed to deliver precise and consistent aerial data through artificial intelligence. , In March 2023, a collaboration between Estonian Air Navigation Services (EANS) and Frequentis resulted in the launch of a new drone platform. This platform aims to streamline operations by reducing manual tasks, specifically focusing on pre-flight authorization. Integrating all airspace users onto a single platform offers a centralized source of accurate information and real-time situational awareness for drone operators, air traffic controllers, and service providers. , In March 2023, Vodafone Group Plc, a telecommunications company based in the UK, collaborated with Dimetor to introduce DroNet, a digital data service to assess the risk of commercial drone flights in Germany. This innovative solution enables the company to provide mobile phone data to expedite and enhance the evaluation of ground risk associated with drone operations. By leveraging this service, the assessment process becomes faster, more efficient, and more secure than ever. , In March 2022, Asteria Aerospace, an Indian drone manufacturer and solution provider, introduced SkyDeck, an all-in-one drone operations platform. SkyDeck is a cloud-based software solution that delivers Drone-as-a-Service (DaaS) to various industry verticals, including surveying, industrial inspections, agriculture, and surveillance and security. , In February 2021, Delta Drone International, a drone-based data services and technology solutions company, extended its operations into Zambia. The expansion aimed to provide a specialized agricultural project for Syngenta, an agricultural science and technology provider. Leveraging its existing partnership with Syngenta since 2018, Delta Drone International's subsidiary, Rocketfarm, will broaden its scope and utilize advanced data capabilities to visualize and analyze crops virtually. .

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License
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The DPJAIT dataset is made available under the CC BY 4.0 license https://creativecommons.org/licenses/by/4.0/

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Summary
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DPJAIT DATASET – MULTIMODAL DATASET FOR INDOOR 3D DRONE TRACKING
The DPJAIT dataset has been designed for research on vision-based 3D drone tracking. The dataset consists of real measurements registered by a Vicon system containing a synchronized RGB multicamera set and motion capture acquisition, as well as simulated sequences obtained from a similar but virtual camera system created in Unreal Engine and AirSim simulator. The scene for the simulation sequences was prepared using a model of the Human Motion Lab (HML) at the Polish-Japanese Academy of Information Technology (PJAIT) in Bytom, Poland, in which real sequences were registered.

It is obligatory to cite the following paper in every work that uses the dataset:
J. Rosner, T. Krzeszowski, A. Świtoński, H. Josiński, W. Lindenheim-Locher, M. Zielinski, G. Paleta, M. Paszkuta, K. Wojciechowski: Multimodal dataset for indoor 3D drone tracking challenge, Scientific Data 12, 257 (2025). https://doi.org/10.1038/s41597-025-04521-y

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Data description
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The dataset consists of 13 simulated and 18 real sequences, which differ in the number of drones and their pattern of moving on scene.
The sequences were prepared in such a way that they could be used for various types of research. Some sequences contain a larger amount of drones but with limited motion or a smaller amount with a bigger degree of freedom. Additionally, some simulated sequences were generated based on measurements performed in a real laboratory, so they can be used to compare the results obtained for simulation and real sequences.

The simulated sequences were created using an environment based on the Unreal Engine and the AirSim plugin. It is an open-source project created by Microsoft to provide high-fidelity simulation of a variety of autonomous vehicles. Inside the environment, a scene based on the laboratory where real-life recordings took place was created. At the simulation scene, eight different cameras were placed. For some sequences, the stage size was enlarged twice the size of the HML laboratory to accommodate more flying drones without an issue of potential collisions between each of them. This allowed the generation of sequences with a large number of drones (up to 10), which was not possible to achieve in real conditions. Five different drone models were used in the simulations.
Most sequences contain data from eight cameras, except three sequences generated based on real sequences (S11_D4, S12_D3, S13_D3), which contain only data from four cameras. In addition, sequences S01_D2_A, S02_D4_A, and S03_D10_A contain images from the drone camera (First Person View, FPV), and ArUco markers placed on walls.

In real data scenarios, drones are manually controlled by skilled operators and tracked by a multi-modal acquisition system. Videos are registered by a set of four RGB cameras -- cam_1, cam_2, cam_3, and cam_4 -- with 1924x1082 resolution, located in the corners of the lab. Moreover, motion capture measurements are used to provide reference locations and orientations. It is achieved by tracking four markers -- A, B, C, and D -- attached to the top of the drones and forming an asymmetrical cross (see files MarkersCross_1.jpg, MarkersCross_2.jpg, and MarkersCrosses.pdf in "Additional_Files" folder). Details on how to establish the location and orientation in case of the known 3D coordinates of the markers are described by Lindenheim-Locher, W. et al. (Lindenheim-Locher, W. et al. YOLOv5 Drone Detection Using Multimodal Data Registered by the Vicon System. Sensors 2023, 23, 6396. https://doi.org/10.3390/s23146396).
Moreover, to distinguish different drones visible at the same time instant, various lengths of the cross arms are applied (see MarkersCrosses.pdf in the "Additional_Files" folder). Ground truth data were acquired using a Vicon motion capture system. Synchronization and calibration of the motion capture system and video cameras were carried out using software and hardware provided by Vicon.

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Dataset structure
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* Additional_Files - directory with additional files
* lab_hml_map.pdf - scene diagram with camera placement
* MarkersCross_1.jpg - placement of markers on the drone
* MarkersCross_2.jpg - placement of markers on the drone
* MarkersCrosses.pdf - diagrams with dimensions of crosses with markers
* dl_data-ReadMe.txt - description of files with drones detections using the YOLOv5 model
* Real_Data_ArUco - additional files for sequences with ArUco markers
* ArUco-ReadMe.txt - file structure description
* images with the arrangement of markers on the walls
* Real_Data - 18 video sequences recorded in HML at the PJAIT.
* 4 recordings from cameras placed on the scene
* cameras_calibration.csv - cameras calibration data for OpenCV camera model
* .c3d - 3D coordinates of markers on crosses mounted on drones recorded by the Vicon system (see files MarkersCross_1.jpg, MarkersCross_2.jpg, and MarkersCrosses.pdf)
* dl_data - drones detections using the YOLOv5 model
* sequences with ArUco markers (_A in the name) additionally:
* FPV recordings from drones camera
* fpv_camera_data.csv - FPV camera parameters
* ArUco_3D.xlsx - data of ArUco markers placed on the scene
* _REF_ORI.csv - the drone's reference orientation corresponding to the data from the drone's camera
* _REF_POS.csv - the drone's reference position corresponding to the data from the drone's camera
* cameras_specification.csv - parameters of the cameras used
* Simulated_Data - 13 simulation video sequences.
* 4 to 8 recordings from cameras placed on the scene
* cameras_calibrationm.csv - cameras calibration data for OpenCV camera model
* _pos_25.csv - position and orientation of the drone
* _cam_25.csv (only sequences with ArUco markers - _A in the name) - position, orientation, and parameters of the drone's camera
* drone_masks.zip - extracted drone masks
* dl_data - drones detections using the YOLOv5 model
* sequences with ArUco markers (_A in the name) additionally:
* FPV recordings from the drone's camera
* markersAruco.csv - data of ArUco markers placed on the scene
* cameras_specification.csv - parameters of the cameras used

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Project participants
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Jakub Rosner
Tomasz Krzeszowski

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Acknowledgments
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This work has been supported by the National Centre for Research and Development within the research project "Innovative technology for creating multimedia events based on drone combat with synergy between the VR, AR and physical levels" in the years 2020–2023, Project No. POIR.01.02.00-00-0160/20.

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Further information
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For any questions, comments or other issues please contact Tomasz Krzeszowski

1615 Global import shipment records of Drone with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The global drone data management software market size was valued at USD 824.9 million in 2025 and is projected to reach USD 5,506.9 million by 2033, exhibiting a CAGR of 27.2% during the forecast period. Rising adoption of drones in various industries, growing demand for real-time data analysis, and increasing need for efficient data management solutions are major factors driving market growth. Cloud-based deployment is gaining traction due to its scalability, cost-effectiveness, and ease of access. Large enterprises heavily invest in drone data management software to manage large volumes of data generated by drones. North America dominates the market due to early adoption of drone technology and stringent regulations. Asia Pacific is expected to witness significant growth owing to urbanization, infrastructure development, and increasing demand for drones in agriculture and construction. Key industry players include SafetyCulture, DroneDeploy, Optelos, DroneDeck, Azuga, Airdata UAV, Avision, DroneLogbook, FlytBase, Dronelink, Datumate, and GeoNadir.