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. .

Drone Data Link System Market Outlook

The global Drone Data Link System market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach an estimated USD 4.2 billion by 2032, exhibiting a robust CAGR of 12.1% over the forecast period. This growth trajectory is largely driven by the increasing adoption of drones in various sectors, heightened demand for real-time data communication, and advancements in wireless communication technologies. The enhancement in drone capabilities, coupled with the growing need for efficient and secure data transmission, has catalyzed the market's expansion, making drone data link systems indispensable for both military and commercial applications.

The primary growth factor propelling the Drone Data Link System market is the burgeoning use of drones across various industries for surveillance, mapping, agriculture, and logistics. These applications demand a reliable and efficient communication system to transmit data back and forth between the drone and the control station. The widespread adoption of drones in sectors like defense and government for surveillance purposes requires robust data link systems to ensure uninterrupted communication and precise data transmission. Additionally, the rapid advancements in drone technology have led to the development of drones capable of long-range operations, which further necessitates the enhancement of data link systems to support these capabilities.

Another significant factor contributing to the market growth is the increasing investment in urban air mobility and the proliferation of drone delivery services. As logistics companies explore drone technology to optimize last-mile delivery and reduce delivery times, the demand for sophisticated data link systems is expected to rise. These systems are critical in ensuring the successful navigation and operation of drones, transmitting vital data regarding location, speed, and environmental conditions in real-time. Moreover, the growing focus on utilizing drones for agricultural applications, such as precision farming, wherein drones are used for crop monitoring and management, is also fueling the demand for advanced data link systems.

Technological advancements in communication systems, such as the development of 5G networks, are another pivotal growth driver for the Drone Data Link System market. The integration of 5G technology into drone data link systems can significantly enhance the speed and reliability of data transmission. This technological leap is expected to revolutionize drone operations by enabling high-definition video transmission and real-time data analysis, which are essential for applications like surveillance and mapping. Furthermore, governments worldwide are increasingly drafting regulations that support the incorporation of drones in various sectors, thereby encouraging the adoption of sophisticated data link systems to comply with operational requirements.

Regionally, North America is anticipated to hold a significant share of the Drone Data Link System market, driven by the extensive utilization of drones in defense and commercial sectors. The region's well-established aviation infrastructure, coupled with ongoing advancements in drone technology, positions it as a pivotal market for drone data link systems. Additionally, the Asia Pacific region is expected to witness substantial growth, underpinned by the increasing adoption of drones in countries like China and India for agricultural and logistic purposes. Europe is also expected to experience considerable growth due to the rising demand for drones in surveying and mapping applications. Overall, the regional outlook remains optimistic, with each region contributing significantly to the market's overall expansion.

Component Analysis

In the Drone Data Link System market, the component segment is categorized into hardware, software, and services. The hardware segment encompasses physical components such as antennas, transmitters, and receivers, which are crucial for establishing and maintaining a steady communication link between drones and ground stations. The evolving technology in these components, aimed at enhancing range and reducing latency, is a key factor driving the growth of the hardware segment. Moreover, manufacturers are increasingly focusing on developing lightweight and compact hardware solutions to meet the operational demands of modern drones, thus boosting the segment's growth.

The software component is equally critical, as it includes the programs and algorithms responsible for processing and managing the data tran

Drone onboard multi-modal sensor dataset :

This dataset contains timeseries data from numerous drone flights. Each flight record has a unique identifier (uid) and a timestamp indicating when the flight occurred. The drone's position is represented by the coordinates (position_x, position_y, position_z) and altitude. The orientation of the drone is represented by the quaternion (orientation_x, orientation_y, orientation_z, orientation_w). The drone's velocity and angular velocity are represented by (velocity_x, velocity_y, velocity_z) and (angular_x, angular_y, angular_z) respectively. The linear acceleration of the drone is represented by (linear_acceleration_x, linear_acceleration_y, linear_acceleration_z).

In addition to the above, the dataset also contains information about the battery voltage (battery_voltage) and current (battery_current) and the payload attached. The payload information indicates if the drone operated with an embdded device attached (nvidia jetson), various sensors, and a solid-state weather station (trisonica).

The dataset also includes annotations for the current state of the drone, including IDLE_HOVER, ASCEND, TURN, HMSL and DESCEND. These states can be used for classification to identify the current state of the drone. Furthermore, the labeled dataset can be used for predicting the trajectory of the drone using multi-task learning.

For the annotation, we look at the change in position_x, position_y, position_z and yaw. Specifically, if the position_x,
position_y changes, it means that the drone moves in a horizontal straight line, if the position_z changes, it means that the drone performs ascending or descending (depends on whether it increases or decreases), if the yaw changes, it means that the drone performs a turn and finally if any of the above features do not change, it means the drone is in idle or hover mode.

In addition to the features already mentioned, this dataset also includes data from various sensors including a weather station and an Inertial Measurement Unit (IMU). The weather station provides information about the weather conditions during the flight. This information includes, wind speed, and wind angle. These weather variables could be important factors that could influence the flight of the drone and battery consumption. The IMU is a sensor that measures the drone's acceleration, angular velocity, and magnetic field. The accelerometer provides information about the drone's linear acceleration, while the gyroscope provides information about the drone's angular velocity. The magnetometer measures the Earth's magnetic field, which can be used to determine the drone's orientation.

Field deployments were performed in order to collect empirical data using a specific type of drone, specifically a DJI Matrice 300 (M300). The M300 is equipped with advanced sensors and flight control systems, which can provide high-precision flight data. The flights were designed to cover a range of flight patterns, which include triangular flight patterns, square flight patterns, polygonal flight pattern, and random flight patterns. These flight patterns were chosen to represent a variety of different flight scenarios that could be encountered in real-world applications. The triangular flight pattern consists of the drone flying in a triangular path with a fixed altitude. The square flight pattern involves the drone flying in a square path with a fixed altitude. The polygonal flight pattern consists of the drone flying in a polygonal path with a fixed altitude, and the random flight pattern involves the drone flying in a random path with a fixed altitude. Overall, this dataset contains a rich set of flight data that can be used for various research purposes, including developing and testing algorithms for drone control, trajectory planning, and machine learning.

Aerial Survey Drone Market Outlook

The global aerial survey drone market size is projected to reach USD 7.2 billion by 2032, growing from USD 1.8 billion in 2023, at a compound annual growth rate (CAGR) of 16.7% during the forecast period. This remarkable growth can be attributed to several factors, including advancements in drone technology, increasing demand for precise and high-resolution geographic data, and the expanding applications of aerial survey drones in various sectors.

One of the prominent growth factors driving the aerial survey drone market is the rapid technological advancements in drone hardware and software. The continuous innovation in drone capabilities, such as enhanced payload capacity, longer flight times, and improved data processing, has significantly widened the range of applications for these devices. Additionally, the integration of AI and machine learning in drone operations has further enhanced the accuracy and efficiency of aerial surveys, making them indispensable tools in industries like agriculture, construction, and environmental monitoring.

Another crucial growth factor is the increasing need for high-resolution and precise geographic data in various sectors. Industries such as agriculture, mining, and construction rely heavily on accurate geographic information for decision-making and operational efficiency. Aerial survey drones offer a cost-effective and efficient means to acquire this data, enabling businesses to optimize their processes and reduce operational costs. Furthermore, the ability of drones to access hard-to-reach areas and provide real-time data has revolutionized traditional survey methods, making them more timely and effective.

Moreover, government regulations and policies favoring the use of drones for commercial applications have also contributed to market growth. Several countries have recognized the potential benefits of drones in enhancing productivity and safety and have implemented supportive regulations to facilitate their adoption. For instance, the Federal Aviation Administration (FAA) in the United States has established rules for commercial drone operations, which has encouraged the use of drones in various industries. This regulatory support is expected to further drive the growth of the aerial survey drone market.

The integration of advanced Drone Sensor technology has played a pivotal role in enhancing the capabilities of aerial survey drones. These sensors are crucial for capturing high-resolution images and collecting precise geographic data, which are essential for various applications such as agriculture, construction, and environmental monitoring. The evolution of sensor technology has enabled drones to perform complex tasks with greater accuracy and reliability. As sensors become more sophisticated, they contribute to the overall efficiency and effectiveness of drone operations, making them indispensable tools in modern surveying practices. The continuous advancements in sensor technology are expected to drive further innovations in the aerial survey drone market, opening up new possibilities for data collection and analysis.

From a regional perspective, North America holds a significant share of the aerial survey drone market, driven by the presence of major drone manufacturers and the early adoption of advanced technologies in the United States and Canada. Furthermore, the region's favorable regulatory environment and substantial investments in drone technology are expected to sustain its market leadership. Meanwhile, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, owing to the increasing adoption of drones in agriculture and infrastructure development projects in countries like China and India. The European market is also poised for substantial growth, supported by technological advancements and the presence of key industry players.

Type Analysis

The aerial survey drone market can be segmented by type into fixed-wing, rotary-wing, and hybrid drones. Fixed-wing drones are known for their extended flight range and endurance, making them suitable for large-scale surveys and mapping projects. These drones are particularly favored in applications that require covering vast areas, such as agriculture and environmental monitoring. The ability of fixed-wing drones to remain airborne for extended periods allows for comprehensive data collection over large territories, enhancing the efficiency and accuracy of survey operations.<

arxiv : https://arxiv.org/abs/2304.11708

Accepted at 29th International Congress on Sound and Vibration (ICSV29).

The drone has been used for various purposes including military applications, aerial photography, and pesticide spraying. However, the drone is vulnerable to external disturbances, and malfunction in propellers and motors can easily occur. To improve the safety of drone operations, early detection of mechanical faults should be made in real-time. In this paper, we propose a sound-based deep neural network (DNN) fault classifier and drone sound dataset. The dataset was constructed by collecting the operating sounds of drones from microphones mounted on three different drones in an anechoic chamber. The dataset includes various operating conditions of drones, such as flight directions (front, back, right, left, clockwise, counter clockwise) and faults on propellers and motors. The drone sounds were then mixed with noises recorded in five different spots on the university campus, with a signal-to-noise ratio (SNR) varying from 10 dB to 15 dB. Using the acquired dataset, we train a DNN classifier, 1DCNN-ResNet, that classifies the types of mechanical faults and their locations from short-time input waveforms. We employ multitask learning (MTL) and incorporate the direction classification task as an auxiliary task to make the classifier learn more general audio features. The test over unseen data reveals that the proposed multitask model can successfully classify faults in drones and outperforms single-task models even with less training data.

please reorganize the file directory like below

drone

ㄴA

ㄴB

ㄴC

For each drone type A, B, and C have 540002 files. (Here, 2 means stereo channel, you can find mic1 and mic2 in subdirectory) They are divided into train, valid, and test by a 6:2:2 ratio. For each file, recording information is labeled below.

{model_type}{maneuvering_direction}{fault}{drone_file_index}{background}{background_file_index}{SNR}

model_type: A, B, C

maneuvering_direction: F(Front), B(Back), R(Right), L(Left), C(Clockwise), CC(Counter-clockwise)

fault: N (Normal), MF1~4 (Moter Failure), PC1~4 (Propeller Cut) -> 1~4 means each motor/propeller of the quadcopter.

Dataset available under below "Homepage" ↓

Dataset

The Man OverBoard Drone (MOBDrone) dataset is a large-scale collection of aerial footage images. It contains 126,170 frames extracted from 66 video clips gathered from one UAV flying at an altitude of 10 to 60 meters above the mean sea level. Images are manually annotated with more than 180K bounding boxes localizing objects belonging to 5 categories --- person, boat, lifebuoy, surfboard, wood. More than 113K of these bounding boxes belong to the person category and localize people in the water simulating the need to be rescued.

In this repository, we provide:

• 66 Full HD video clips (total size: 5.5 GB)

• 126,170 images extracted from the videos at a rate of 30 FPS (total size: 243 GB)

• 3 annotation files for the extracted images that follow the MS COCO data format (for more info see https://cocodataset.org/#format-data):

  • annotations_5_custom_classes.json: this file contains annotations concerning all five categories; please note that class ids do not correspond with the ones provided by the MS COCO standard since we account for two new classes not previously considered in the MS COCO dataset --- lifebuoy and wood

  • annotations_3_coco_classes.json: this file contains annotations concerning the three classes also accounted by the MS COCO dataset --- person, boat, surfboard. Class ids correspond with the ones provided by the MS COCO standard.

  • annotations_person_coco_classes.json: this file contains annotations concerning only the 'person' class. Class id corresponds to the one provided by the MS COCO standard.

The MOBDrone dataset is intended as a test data benchmark. However, for researchers interested in using our data also for training purposes, we provide training and test splits:

• Test set: All the images whose filename starts with "DJI_0804" (total: 37,604 images)
• Training set: All the images whose filename starts with "DJI_0915" (total: 88,568 images)

More details about data generation and the evaluation protocol can be found at our MOBDrone paper: https://arxiv.org/abs/2203.07973
The code to reproduce our results is available at this GitHub Repository: https://github.com/ciampluca/MOBDrone_eval
See also http://aimh.isti.cnr.it/dataset/MOBDrone

Citing the MOBDrone

The MOBDrone is released under a Creative Commons Attribution license, so please cite the MOBDrone if it is used in your work in any form.
Published academic papers should use the academic paper citation for our MOBDrone paper, where we evaluated several pre-trained state-of-the-art object detectors focusing on the detection of the overboard people

@inproceedings{MOBDrone2021,
title={MOBDrone: a Drone Video Dataset for Man OverBoard Rescue},
author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi},
booktitle={ICIAP2021: 21th International Conference on Image Analysis and Processing},
year={2021}
}

and this Zenodo Dataset

@dataset{donato_cafarelli_2022_5996890,
author={Donato Cafarelli and Luca Ciampi and Lucia Vadicamo and Claudio Gennaro and Andrea Berton and Marco Paterni and Chiara Benvenuti and Mirko Passera and Fabrizio Falchi},
 title    = {{MOBDrone: a large-scale drone-view dataset for man overboard detection}},
 month    = feb,
 year     = 2022,
 publisher  = {Zenodo},
 version   = {1.0.0},
 doi     = {10.5281/zenodo.5996890},
 url     = {https://doi.org/10.5281/zenodo.5996890}
}

Personal works, such as machine learning projects/blog posts, should provide a URL to the MOBDrone Zenodo page (https://doi.org/10.5281/zenodo.5996890), though a reference to our MOBDrone paper would also be appreciated.

Contact Information

If you would like further information about the MOBDrone or if you experience any issues downloading files, please contact us at mobdrone[at]isti.cnr.it

Acknowledgements

This work was partially supported by NAUSICAA - "NAUtical Safety by means of Integrated Computer-Assistance Appliances 4.0" project funded by the Tuscany region (CUP D44E20003410009). The data collection was carried out with the collaboration of the Fly&Sense Service of the CNR of Pisa - for the flight operations of remotely piloted aerial systems - and of the Institute of Clinical Physiology (IFC) of the CNR - for the water immersion operations.

The global drone camera market, valued at $10.96 billion in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 18.28% from 2025 to 2033. This expansion is driven by several key factors. The increasing adoption of drones across diverse sectors, including agriculture, construction, filmmaking, and surveillance, fuels the demand for high-quality drone cameras. Technological advancements, such as improved image sensors, enhanced stabilization systems, and advanced features like thermal imaging and AI-powered object recognition, are further driving market growth. Furthermore, the decreasing cost of drone technology is making it accessible to a wider range of users, both professional and hobbyist, contributing to market expansion. The market is segmented by production and consumption analysis, providing insights into manufacturing capacities and end-user demands across various regions. Import and export market analyses, coupled with price trend analysis, offer a comprehensive understanding of global trade dynamics within the sector. Key players like DJI, GoPro, and others are actively competing through innovation and product diversification. Regional analysis reveals significant market variations. North America, with its robust technological infrastructure and early adoption of drone technology, is expected to hold a substantial market share. However, the Asia-Pacific region, particularly China and India, is projected to witness rapid growth due to increasing government initiatives promoting drone technology and a large potential user base. Europe is also anticipated to experience steady growth, driven by investments in various drone-related applications, while other regions will contribute to the overall market expansion. Growth restraints include regulatory hurdles surrounding drone operation, concerns about data privacy and security, and potential safety issues related to drone usage. However, the overall market outlook remains extremely positive, given the immense potential for innovation and applications within various industries. This report provides a detailed analysis of the global drone camera market, covering the period from 2019 to 2033. With a base year of 2025 and an estimated year of 2025, this comprehensive study forecasts market trends until 2033, leveraging historical data from 2019-2024. The report explores key market segments, including production and consumption analysis, import and export market analysis (value & volume), and price trend analysis, offering a granular view of the industry's dynamics. Recent developments include: April 2023: Teledyne FLIR launched development kits for easy integration of the FLIR Hadron 640R thermal and visible dual camera module. The module’s compact radiometric Boson thermal camera grants visibility even in total darkness, smoke, and fog. It also features a 64MP electro-optical (EO) camera for high-definition visible imagery., December 2022: DJI developed a compact, ultra-lightweight camera drone, DJI Mini 3, which weighs less than 249 grams., June 2022: DJI launched DJI RS 3 and DJI RS 3 Pro, incorporating a range of new features. It includes a redesigned axes-locking system making the process of recording videos and capturing high-resolution images automated. By turning on the gimbal, the automated axis locks release and unfold the gimbal, allowing the operator to get started in seconds.. Key drivers for this market are: , Increased Seaborne Threats And Ambiguous Maritime Security Policies; Increasing Adoption Of Security Technologies In Bric Countries. Potential restraints include: , High Risk Rate In Ungoverned Zones; Unstructured Security Standards And Technologies. Notable trends are: Surveillance Segment Will Showcase Remarkable Growth During the Forecast Period.

Drone Mapping Software Market Outlook

The drone mapping software market is poised for significant growth, with a market size estimated at approximately $1.5 billion in 2023 and projected to reach $4.5 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of 12.9%. The driving force behind this growth is the increasing adoption of drones across various industries for mapping and surveying purposes. This widespread acceptance is primarily due to the enhanced efficiency, accuracy, and cost-effectiveness that drone mapping offers compared to traditional methods. The market's expansion is further fueled by advancements in drone technology, which have led to improved data collection, analysis, and visualization capabilities.

One of the major growth factors driving the drone mapping software market is the burgeoning demand from the agriculture sector. Precision agriculture has gained immense popularity, and drones equipped with mapping software play a crucial role in monitoring crop health, assessing soil conditions, and managing irrigation systems. This technology enables farmers to maximize yield and reduce resource wastage. Additionally, government initiatives aimed at promoting smart farming practices are supporting this growth trajectory. Another factor contributing to the market's expansion is the increasing need for urban planning and infrastructure development. Drones offer an efficient means of surveying large areas and gathering detailed data, which is essential for urban planners and construction companies in designing and managing large-scale projects.

The construction industry is another significant driver of the drone mapping software market. Drones are being used to monitor construction sites, track progress, and ensure safety compliance. The ability to create accurate 3D models and maps of construction sites in real-time reduces project delays and costs. Moreover, the integration of drone mapping software with other technologies such as Building Information Modeling (BIM) enhances the overall efficiency of construction processes. The mining sector is also contributing to the market's growth, as drones are increasingly used for surveying mines, mapping mineral deposits, and ensuring worker safety. These applications are not only improving operational efficiency but also reducing environmental impact by providing precise data for responsible resource management.

Regionally, North America is expected to dominate the drone mapping software market throughout the forecast period. The presence of major market players, technological advancements, and supportive regulatory frameworks are some of the key factors supporting this dominance. The Asia Pacific region is anticipated to exhibit the highest growth rate, driven by increasing investments in infrastructure development and the adoption of advanced agricultural practices. Countries like China and India are at the forefront of this growth, as they leverage drone technology to address the challenges of rapid urbanization and food security. Additionally, Europe is witnessing significant adoption of drone mapping solutions in environmental monitoring and urban planning applications, contributing to the overall market growth.

Photogrammetry Software plays a pivotal role in the drone mapping software market, offering advanced capabilities for creating detailed 3D models and maps from aerial imagery. This technology is particularly beneficial in industries such as construction, agriculture, and mining, where precise spatial data is crucial for decision-making. By utilizing photogrammetry software, users can transform raw drone-captured images into accurate, high-resolution maps and models, enhancing the efficiency of surveying and mapping processes. The integration of photogrammetry with Geographic Information Systems (GIS) further amplifies its utility, providing a comprehensive solution for spatial analysis and planning. As the demand for precision mapping continues to grow, photogrammetry software is expected to remain a cornerstone of the drone mapping industry.

Component Analysis

The drone mapping software market is segmented by component into software and services. Software forms the core of this market, providing essential tools for data processing, analysis, and visualization. Advanced software solutions offer features such as 3D modeling, orthomosaic generation, and Geographic Information System (GIS) integration, which are critical for accurate mapping and surveying. These software solutions are

Orthophotos and digital surface models (DSMs) obtained using UAV photogrammetry can be used to determine the water surface level of a river. However, this task is difficult due to disturbances of the water surface on DSMs caused by limitations of photogrammetric algorithms. Machine Learning can be used to correct these disturbances as well as to extract a single water surface elevation value. The presented dataset contains raw photogrammetric orthophotos and DSMs of areas representing parts of a small river and the corresponding DSMs with corrected water surface disturbances. Also a single ground truth value of mean water surface level for each DSM sample is provided. This allows the dataset to be used for supervised training of a neural network performing a denoising or regression task.

Acknowledgement: some of the samples were extracted from photogrammetric data acquired by Bandini et. al (https://doi.org/10.5281/zenodo.3519888)

Drone Imagery Service Market Outlook

The global drone imagery service market size was valued at USD 3.45 billion in 2023 and is projected to reach USD 12.7 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.6% during the forecast period. The burgeoning demand for high-resolution aerial imagery and advancements in drone technology are driving this market's impressive growth.

One of the primary growth factors for the drone imagery service market is the increasing use of drones in various sectors such as agriculture, construction, and real estate. In agriculture, drones equipped with multispectral sensors are providing invaluable data for crop monitoring, pest control, and precision farming. Similarly, in construction and real estate, drones are revolutionizing project management, site inspections, and marketing by offering cost-effective and time-efficient solutions. These applications are significantly contributing to the market's robust expansion.

Another significant factor propelling the market is technological advancements in drone capabilities. Modern drones are equipped with sophisticated sensors, high-definition cameras, and advanced data processing software that enable precise and comprehensive data collection. This technological progress has expanded the scope of drone applications, making them indispensable tools in fields like environmental monitoring, disaster management, and media production. Enhanced drone functionalities are facilitating improved accuracy and efficiency, attracting more industries to adopt these services.

The regulatory landscape also plays a crucial role in the market's growth. As governments worldwide recognize the benefits of drone technology, they are implementing more supportive policies and regulations. For instance, the Federal Aviation Administration (FAA) in the United States has streamlined the process for commercial drone operations, making it easier for businesses to utilize drone services. Similarly, many countries in Europe and Asia are adopting favorable drone policies, further driving the market's growth. These regulatory changes are crucial in removing barriers to drone adoption and fostering a thriving market environment.

The emergence of Smart Drone Services is revolutionizing the way industries leverage drone technology. These services offer enhanced capabilities through the integration of artificial intelligence and machine learning, allowing drones to perform complex tasks autonomously. From precision agriculture to infrastructure inspections, smart drones are providing unprecedented levels of efficiency and accuracy. By analyzing data in real-time, they can make informed decisions, reducing human intervention and operational costs. As industries continue to recognize the potential of smart drones, the demand for these advanced services is expected to surge, further driving the growth of the drone imagery service market.

Regionally, North America is expected to dominate the drone imagery service market, owing to the early adoption of advanced technologies and a strong presence of key market players. The United States is particularly a significant contributor, driven by substantial investments in drone technology for commercial and military applications. However, the Asia Pacific region is also witnessing rapid growth, fueled by increasing drone deployments in agriculture and infrastructure projects. Europe's market is being driven by industries like construction and environmental monitoring, while the Middle East & Africa are gradually catching up due to growing awareness and adoption of drone services.

Service Type Analysis

In the drone imagery service market, the service type segment can be categorized into mapping & surveying, inspection & monitoring, aerial photography & videography, and others. Mapping & surveying services hold a substantial share of the market due to their widespread use in agriculture, construction, and environmental monitoring. These services offer high-precision data for land assessment, topographic mapping, and resource management, making them essential tools across various industries. The accuracy and cost-effectiveness of drone-based mapping and surveying are driving their adoption in both commercial and governmental projects.

Inspection & monitoring services are another critical segment, particularly in industries requiring regular maintenance and sa

The following two bags permit the testing of the vehicle dynamic model-based navigation real-time software VDMc available here.

TOPOPlane2_20221027_STIM14.bag

Data are saved in a rosbag while flying with the TOPOPlane2 drone on October 2nd, 2022. The _tagged suffix name means that the data come from the autopilot and the time is GNSS time-tagged with an internal routine. The bag contains the following topics:

• /GIINAV_POSE - solution of the INS/GNSS software (ros msg type: nav_msgs/Odometry)
• /GPS0 - binary output of the GNSS receiver. These data need to be parsed with the function GNSSReceiver::gpsCallBack in gnssreceiver.cpp (ros msg type: std_msgs/Uint8MultiArray)
• /IMU0 - binary output of the IMU sensor. These data need to be parsed with the function IMUReceiver::imuCallback in imureceiver.cpp (ros msg type: std_msgs/Uint8MultiArray)
• /airData - data from the surrey PitotTube and processed (custom ros msg type: surrey_sensor/AirData, available in AirData.h)
  • GNSS time [s]
  • airSpeed [m/s]
  • baroAltitude [m]
  • density [hPa]
• /airpressure_tagged - dynamic air pressure of the PitotTube (Pa) (ros msg type: sensor_msgs/FluidPressure )
• /airspeed_tagged - transformed airspeed [m/s] and groundspeed [m/s] from Pitotube (ros msg type: mavros_msgs/VFR_HUD)
• /cc_tagged - control commands tagged with GNSS time (ros msg type: mavros_msgs/RCout)
  • header, channels (autopilot value from 1000 to 2000). The conversion is done in the function CCReceiver::ccCallback in ccreceiver.cpp [aileron elevator rpm rudder]
• /mavros/debug_value/debug_vector - This custom vector is used to exchange data from/to the Ground Control Station (GCS). It contains the solution of the INS/GNSS (Giinav) and VDMNav (ros msg type: mavros_msgs/DebugValue)
• /mavros/debug_value/named_value_float - Topic used to trigger the real-time simulated GNSS outage from the GCS (ros msg type: mavros_msgs/DebugValue)
• /mavros/debug_value/send - not used
• /mavros/imu/diff_pressure (ros msg type: sensor_msgs/FluidPressure)
• /mavros/imu/static_pressure (ros msg type: sensor_msgs/FluidPressure)
• /mavros/rc/out - control commands tagged with autopilot (system) time (ros msg type: mavros_msgs/RCout)
• /mavros/wind_estimation - autopilot estimation of the wind (ros msg type: geometry_msgs/TwistWithCovarianceStamped)
• /surrey - raw data from the surrey PitotTube (custom ros msg type: surrey_sensor/Surrey, available in Surrey.h)
• /timeSync - mapping from autopilot time and GNSS time to tag the data autopilot data (*_tagged) with GNSS time (ros msg type: std_msgs/Float32MultiArray)

concordeS_20230601.bag

These data are generated in a formatted version using the recorded flight with the ConcordeS1 drone on June 1st, 2023. The bag contains the following topics:

• /airData - data from the PitotTube (custom ros msg types: surrey_sensor/AirData, available from AirData.h)
  • GNSS time [s]
  • airSpeed [m/s]
  • baroAltitude [m]
  • density [hPa]
• /cc_formatted - control commands (ros msg type: std_msgs/Float64MultiArray)
  • data :[GNSS time [s], rpm [normalized value from autopilot channel 0-1000], LeftDeflection [degree], RightDeflection [degree]]
• /giinav_formatted - INS/GNSS solution, used to initialize the in-flight VDM-based navigation filter (ros msg type: nav_msgs/Odometry)
  • pose:
    • position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]
    • orientation (NED) [x y z w] in quaternion
  • twist:
    • linear (NED) [x [m/s], y [m/s], z [m/s]]
    • angular (body) [x [degree/s], y [degree/s], z [degree/s]]
• /gnss_formatted - GNSS position and velocity (ros msg type: nav_msgs/Odometry)
  • pose:
    • position (WGS84) [x(lat) [rad], y(long) [rad], z(alt) [m]]
    • orientation - null
  • twist:
    • linear (NED) [x [m/s], y [m/s], z [m/s]]
    • angular - null
• /imu_formatted - a triplet of accelerometer and gyroscope (ros msg type: sensors_msgs/IMU)
  • header
  • orientation (NED) (quaternion) [x y z w]
  • orientation_covariance
  • angular_velocity (body) [degree/s] [x y z]
  • angular_velocity_covariance
  • linear_acceleration (body) [m/s2] [x y z]
  • linear_acceleration_covariance
• /toggleOnOffGnss - Simulate the activation of the GNSS outage for testing purposes (ros msg type: std_msgs/Bool)

Aerial surveys are frequently used to estimate the abundance of marine mammals, but their accuracy is dependent upon obtaining a measure of the availability of animals for visual detection. Existing methods for characterizing availability have limitations and do not necessarily reflect true availability. Here, we present a method of using small, vessel‐launched, multi‐rotor Unoccupied Aerial Vehicles (UAVs or drones) to collect video of dolphins to characterize availability and investigate errors surrounding group size estimates. We collected over 20 h of aerial video of dive‐surfacing behaviour across 32 encounters with the Australian humpback dolphin Sousa sahulensis off north‐western Australia. Mean surfacing and dive periods were 7.85 sec (se = 0.26) and 39.27 sec (se = 1.31) respectively. Dolphin encounters were split into 56 focal follows of consistent group composition to which example approaches to estimating availability were applied. Non‐instantaneous availability estimates, assuming a 7-sec observation window, ranged between 0.22 and 0.88, with a mean availability of 0.46 (CV = 0.34). Availability tended to increase with increasing group size. We found a downward bias in group size estimation, with true group size typically one individual more than would have been estimated by a human observer during a standard aerial survey. The variability of availability estimates between focal follows highlights the importance of sampling across a variety of group sizes, compositions, and environmental conditions. Through data re‐sampling exercises, we explored the influence of sample size on availability estimates and their precision, with results providing an indication of target sample sizes to minimize bias in future research. We show that UAVs can provide an effective and relatively inexpensive method of characterizing dolphin availability with several advantages over existing approaches. The example estimates obtained for humpback dolphins are within the range of values obtained for other shallow‐water, small cetaceans, and will directly inform a government‐run program of aerial surveys in the region.

The global drone services market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's Compound Annual Growth Rate (CAGR) exceeding 25% from 2019 to 2024 suggests a significant expansion, projected to continue through 2033. Key drivers include advancements in drone technology, leading to enhanced capabilities in areas like precision agriculture, infrastructure inspection, and delivery services. Furthermore, decreasing drone costs and the rising availability of skilled operators are fueling market expansion. The integration of AI and machine learning into drone operations is enhancing data analysis and automation, further propelling market growth. While regulatory hurdles and safety concerns pose certain restraints, the overall market outlook remains positive, fueled by technological advancements and increasing adoption across various industries. Specific segments within the drone services market, while not explicitly detailed, likely include aerial photography and videography, precision agriculture (crop monitoring, spraying), infrastructure inspection (bridges, pipelines, power lines), surveying and mapping, delivery and logistics, and search and rescue operations. Leading companies like PrecisionHawk, Airobotics, Cyberhawk Innovations, and others are contributing to market growth through technological innovation and service expansion. Regional data, though unavailable, would likely show strong growth in North America and Europe, driven by early adoption and technological advancement, with emerging markets in Asia-Pacific and Latin America showing increasing potential. The base year of 2025 provides a strong foundation for projecting future growth based on the observed CAGR and market dynamics. Considering the current market trends, a logical extrapolation suggests the market will continue its rapid expansion throughout the forecast period. Recent developments include: July 2022: American Robotics announced the addition of new capabilities to its autonomous Scout System drone to allow visual inspections of oil and gas facilities to be carried out autonomously. The high-resolution RGB and thermal camera payloads will enable routine, high-value inspections of upstream and midstream oil and gas assets., October 2022: Volatus, in a strategic partnership with Synergy, is set to gain immediate access to the thriving oil and gas industry in Western Canada. This collaboration will significantly expand Volatus' portfolio of cutting-edge drone hardware, comprehensive services, and specialized pilot training programs. Volatus noted the deal is based on an arms-length agreement, meaning both companies are acting in their respective yet overlapping interests to strengthen their businesses by coming together.. Notable trends are: Drones’ Integration with Artificial Intelligence (AI) Expected to Drive the Market.

3-7 cm resolution color (RGB, red-green-blue) orthomosaics of the 50-ha Smithsonian ForestGEO plot on Barro Colorado Island, Panama, for 47 dates from 2 October 2014 to 28 November 2019.

Orthomosaics were produced from photogrammetry processing of drone-acquired imagery using Agisoft Metashape (previously Agisoft Photoscan) software. Orthomosaics were horizontally aligned to the first set (2 October 2014) using the centers of Attalea palms and other tree crowns as manual control points.

These data are licensed under CC BY, meaning use of the data is allowed so long as attribution is given via citation. These data should be cited either as an individual dataset or as part of the larger collection:

Garcia, Milton, Jonathan P. Dandois, Raquel F. Araujo, Samuel Grubinger, and Helene C. Muller-Landau. 2021. Color orthomosaics of the 50-ha plot on Barro Colorado Island, Panama, for 2014-2019. Smithsonian Figshare DOI: 10.25573/data.16869259

or

Araujo, Raquel F., Samuel Grubinger, Milton Garcia, Jonathan P. Dandois, and Helene C. Muller-Landau. 2021. Collection of datasets: Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Smithsonian Figshare. DOI: 10.25573/data.c.5389043

These datasets were used in the following peer-reviewed journal article:

Araujo, R. F., S. Grubinger, C. H. S. Celes, R. I. Negrón-Juárez, M. Garcia, J. P. Dandois, and H. C. Muller-Landau. 2021. Strong temporal variation in treefall and branchfall rates in a tropical forest is related to extreme rainfall: results from 5 years of monthly drone data for a 50-ha plot. Biogeosciences.

The code used to analyze these data for this article are available in GitHub, at https://github.com/Raquel-Araujo/gap_dynamics_BCI50ha

Author contribution for datasets for 2014-2015: Helene C. Muller-Landau conceived the research, wrote the grant proposal that funded the research, and designed data collection. Jonathan Dandois constructed the drones, led drone data collection, performed photogrammetry processing, and did preliminary horizontal alignment. Samuel Grubinger finalized horizontal and vertical alignment and identified canopy disturbances. Raquel F. Araujo revised canopy disturbances and classified them as branchfalls, treefalls, or standing dead trees.

Author contribution for datasets for 2016-2019: Helene C. Muller-Landau conceived the research and designed the data collection. Milton Garcia led drone data collection and processed drone imagery. Raquel F. Araujo performed horizontal and vertical alignment, identified canopy disturbances, and classified disturbances as branchfalls, treefalls, or standing dead trees.

Acknowledgments: We thank Marino Ramirez, Pablo Ramos, Paulino Villareal and others for assistance with drone data collection; and Milton Solano for assistance with data processing and organization. We gratefully acknowledge the financial support of the Smithsonian Institution Competitive Grants Program for Science; the Next Generation Ecosystem Experiments-Tropics, funded by the U.S. Department of Energy, Office of Science, Office of Biological and Environmental Research; and the Smithsonian Tropical Research Institute fellowship program. Kristina Anderson-Teixeira, Stephanie Bolman, Richard Condit, Stuart Davies, Matteo Detto, Jefferson Hall, Patrick Jansen, Stefan Schnitzer, Edmund Tanner, and S. Joseph Wright were co-PIs on the original Smithsonian proposal, and we thank them for their contributions to the proposal and input on the research.

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Consumer Drones Market Size 2025-2029

The consumer drones market size is forecast to increase by USD 11.03 billion, at a CAGR of 20.6% between 2024 and 2029.

The market is experiencing significant growth, driven by the continuous advancements in sensor technology and the emergence of affordable drones. These developments have made drones increasingly accessible to consumers, expanding their applications beyond hobbyist use. However, the market faces challenges from stringent government regulations regarding consumer drone usage. These regulations, aimed at ensuring safety and privacy, can hinder market growth and require companies to navigate complex regulatory frameworks. To capitalize on the market's potential, businesses must stay informed of technological advancements and adapt to evolving regulatory requirements. By doing so, they can effectively cater to the growing consumer demand for affordable, feature-rich drones while mitigating regulatory risks.

What will be the Size of the Consumer Drones Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe consumer drone market continues to evolve, driven by advancements in technology and expanding applications across various sectors. Data transmission capabilities have become increasingly crucial, enabling real-time image processing and FPV systems for enhanced user experience. Gimbal stabilization and image processing technologies have revolutionized aerial photography and film production, while obstacle avoidance systems ensure safe and autonomous flight. Wi-Fi connectivity and software algorithms have streamlined drone operation and data analytics, making them essential tools for industries such as search and rescue, law enforcement, and environmental monitoring. Delivery services and hobbyist markets have further fueled market growth, with product innovation and competitive landscape analysis shaping pricing strategies and sales volume. Battery technology, including lipo batteries and flight time, remains a critical focus, as does data security and privacy concerns in the face of increasing regulation compliance. Manufacturing processes, distribution networks, and drone insurance are also key considerations for market players. Looking ahead, future trends include advancements in battery technology, payload capacity, and autonomous flight capabilities, as well as the integration of GPS modules, GPS modules, and drone cameras into various applications. The continuous unfolding of market activities and evolving patterns underscores the dynamic nature of the consumer drone market, making it an exciting and ever-evolving space for businesses and consumers alike.

How is this Consumer Drones Industry segmented?

The consumer drones industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductMultirotorFixed wingSingle rotorDistribution ChannelOfflineOnlineTechnologyRemotely operated droneSemi-autonomous droneAutonomous droneApplicationHobbyist and gamingAerial photographyOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACChinaIndiaJapanSouth KoreaSouth AmericaBrazilRest of World (ROW)

By Product Insights

The multirotor segment is estimated to witness significant growth during the forecast period.Multirotor drones, popular among hobbyists and professionals, offer significant return on investment through applications in aerial photography, video surveillance, and surveying. With Wi-Fi connectivity and data transmission capabilities, these drones facilitate real-time image processing and data analytics. Search and rescue missions and emergency response situations benefit from their agility and quick deployment. The competitive landscape is shaped by advancements in gimbal stabilization, obstacle avoidance, and FPV systems, enabling stable footage and safer flights. Autonomous flight and software algorithms contribute to increased productivity and efficiency. Product innovation in battery technology, payload capacity, and flight time enhances drone capabilities. Environmental monitoring, law enforcement, and film production industries rely on multirotor drones for data collection and imagery. Delivery services and broadcast media are also exploring their potential. Regulation compliance and data security are crucial concerns, with manufacturers focusing on privacy and FAA/EASA regulations. Manufacturing processes optimize production, while hobbyist markets cater to a growing user base. Sales volume is driven by pricing strategies, profit margins, and risk assessment. Future trends include advancements in video stabilization, autonomous flight, and environmental monitori

Semantic Drone Dataset

The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above ground. A high resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.

Semantic Annotation

The images are labeled densely using polygons and contain the following 22 classes:

• unlabeled
• paved-area
• dirt
• grass
• gravel
• water
• rocks
• pool
• vegetation
• roof
• wall
• window
• door
• fence
• fence-pole
• person
• dog
• car
• bicycle
• tree
• bald-tree
• ar-marker
• obstacle

Included Data

• 400 training images
• Dense semantic annotations in png format can be found in trainingset/gt/semantic/labelimages/
• Dense semantic annotations as LabelMe xml files can be found in trainingset/gt/semantic/labelme_xml/
• Semantic class definition can be found in trainingset/gt/semantic/classdict.csv
• Bounding boxes of persons as LabelMe xml files found "in trainingset/gt/boundingbox/labelmexml
• Bounding boxes of persons as mask images found in trainingset/gt/boundingbox/masks
• Bounding boxes of individual persons as mask images found in trainingset/gt/boundingbox/masks_instances
• Bounding boxes of persons as python pickle file found in trainingset/gt/boundingbox/bounding_boxes/person/

Contact

aerial@icg.tugraz.at

Citation

If you use this dataset in your research, please cite the following URL: www.dronedataset.icg.tugraz.at

License

The Drone Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

• That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Graz University of Technology) do not accept any responsibility for errors or omissions.
• That you include a reference to the Semantic Drone Dataset in any work that makes use of the dataset. For research papers or other media link to the Semantic Drone Dataset webpage.
• That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
• That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
• That all rights not expressly granted to you are reserved by us (Graz University of Technology).

Column Name,Column Description [Include meaning of any codes or flags used in data column as well as detection limits.],Units of measurement,missing data/no data value

• FID ,Numerical identifier for each polygon,unitless ,
• Shape ,Classification of feature type (all polygons in this case) ,unitless ,
• Area_Pxl ,number of pixels in the polygon ,count,
• Class_name,"Catagories of habitat types: Listed as Mangroves (mangrove vegetation), Spartina (spartina vegetation, equivolent to saltmarsh in the satelite data, Water (open water), Spillbank (Equivolent to man-made) in the satellite data) ",,
• Max_diff,"A Normalized Difference Vegetation Index created using the Red and NIR color bands. This was used as a parameter for species identification as it helps quantify plant health and habitat composition (Broussard et al., 2018).",,
• Mean_Blue,Mean reflectance in blue color band (444 nm) for the polygon ,,
• Mean_DSM ,Mean digital surface model value for the polygon. Note: these values are not corrected and only represent relative elevation. DSM values broadly represent vegetation heigh but should not be interpreted beyond distinguishing vegetation classes,,
• Mean_Green,Mean reflectance in green color band (560 nm) for the polygon ,,
• Mean_NDVI,Mean NDVI value for the polygon,,
• Mean_NIR,Mean reflectance in near-infrared color band (842 nm) for the polygon ,,
• Mean_Red,Mean reflectance in red color band (668 nm) for the polygon ,,
• Mean_Red_E,Mean reflectance in Rededge color band (717 nm) for the polygon ,,

Fire Drone Market Outlook

The global fire drone market size was valued at approximately USD 1.3 billion in 2023 and is expected to reach USD 4.2 billion by 2032, growing at a robust CAGR of 14.2% during the forecast period. This remarkable growth is driven by the increasing need for efficient firefighting solutions, the advancement of drone technology, and the growing awareness of drone applications in emergency services. Fire drones are becoming an integral part of modern firefighting and emergency response operations, as they offer capabilities that traditional methods cannot. Enhanced drone technology allows for real-time data collection and predictive analytics, thereby improving response time and operational efficiency.

The primary growth factor for the fire drone market is the escalating demand for innovative and effective firefighting solutions. Given the increasing frequency and severity of fire incidents globally, there is a pressing need for tools that can improve response times, provide real-time situational awareness, and minimize risk to human life. Fire drones offer these capabilities by functioning in environments that are dangerous or inaccessible to humans. Equipped with high-resolution thermal imaging and real-time data transmission capabilities, modern fire drones can provide a comprehensive view of active fire scenes, enabling more informed decision-making and efficient resource allocation.

Another significant factor contributing to market growth is the advancement in drone technology itself. The evolution of drones from simple, unmanned aerial vehicles to sophisticated tools equipped with AI and machine learning capabilities has opened new avenues for their application in firefighting. This technological progress not only enhances the operational efficiency of fire responses but also reduces the cost of operations by minimizing the need for extensive human intervention. Moreover, the integration of IoT devices with drones enables seamless communication between different firefighting units, further enhancing coordination and efficiency during emergency operations.

The increasing awareness and acceptance of drone applications in various emergency services are also propelling market growth. As governments and fire departments recognize the potential of drones in enhancing firefighting and rescue operations, there is a growing investment in drone technologies and training for emergency response teams. This trend is expected to continue as more success stories and testimonials emerge regarding the effectiveness of drones in saving lives and reducing property damage. Additionally, regulatory frameworks worldwide are evolving to facilitate the integration of drones into emergency response operations, thereby eliminating one of the significant barriers to market expansion.

In the realm of home security, the advent of the Home Security Drone represents a significant leap forward in safeguarding residential properties. These drones are equipped with advanced surveillance technologies, including high-definition cameras and motion sensors, that allow homeowners to monitor their premises in real-time. The ability to patrol large areas autonomously and provide live feeds directly to a smartphone or security system enhances the overall security framework, offering peace of mind to residents. As urban areas continue to expand, the demand for such innovative security solutions is expected to rise, integrating seamlessly with smart home systems to create a comprehensive security network. The Home Security Drone not only acts as a deterrent to potential intruders but also provides valuable data that can be used for emergency response and incident analysis.

Regionally, North America currently leads the fire drone market, attributed to the advanced technological infrastructure and significant investments in research and development. However, Asia Pacific is anticipated to witness the fastest growth during the forecast period, driven by increasing government initiatives towards disaster management and the adoption of cutting-edge technologies. Europe also represents a significant market, as countries in the region are focusing on enhancing their public safety measures through advanced technological integration.

Product Type Analysis

The fire drone market is segmented into three primary product types: fixed-wing, rotary-wing, and hybrid drones. Fixed-wing drones, known for their efficiency over long distances and extended flight times, are of

Detection and monitoring are the first essential step for effective management of sheath blight (ShB), a major disease in rice worldwide. Unmanned aerial systems have a high potential of being utilized to improve this detection process since they can reduce the time needed for scouting for the disease at a field scale, and are affordable and user-friendly in operation. In this study, a commercialized quadrotor unmanned aerial vehicle (UAV), equipped with digital and multispectral cameras, was used to capture imagery data of research plots with 67 rice cultivars and elite lines. Collected imagery data were then processed and analyzed to characterize the development of ShB and quantify different levels of the disease in the field. Through color features extraction and color space transformation of images, it was found that the color transformation could qualitatively detect the infected areas of ShB in the field plots. However, it was less effective to detect different levels of the disease. Five vegetation indices were then calculated from the multispectral images, and ground truths of disease severity and GreenSeeker measured NDVI (Normalized Difference Vegetation Index) were collected. The results of relationship analyses indicate that there was a strong correlation between ground-measured NDVIs and image-extracted NDVIs with the R2 of 0.907 and the root mean square error (RMSE) of 0.0854, and a good correlation between image-extracted NDVIs and disease severity with the R2 of 0.627 and the RMSE of 0.0852. Use of image-based NDVIs extracted from multispectral images could quantify different levels of ShB in the field plots with an accuracy of 63%. These results demonstrate that a customer-grade UAV integrated with digital and multispectral cameras can be an effective tool to detect the ShB disease at a field scale.

Grasslands deliver a range of ecosystem services, including the provision of food and biodiversity, and regulation of soil carbon storage and hydrology. Monitoring schemes are needed to quantify spatial changes in these multiple functions alongside ecosystem degradation. Sward height is widely recognised as a key spatial variable in the provision of these services. Current manual monitoring approaches are labour intensive, and often fail to capture spatial patterns of important features, including sward height. Proximal sensing from small aerial drones carrying lightweight cameras can be transformed into surface height models using image‐based structure‐from‐motion and Multi‐View Stereo‐based approaches; this presents a new opportunity for monitoring the spatial structure of grassland sward height. We combined aerial photographs with field survey data and an open‐source image‐based modelling‐processing workflow to generate sward height measurements for a field comprising mainly Lolium perenne (perennial ryegrass) and Trifolium pratense (red clover). We compared the derived measurements with in situ data captured on the same day using traditional agronomic sward height techniques to determine the quality of the drone‐derived surface model product for sward characterisation. The SfM and Multi‐View Stereo‐based surface model had a mean absolute sward height measurement error of between 3.7 and 4.2 cm. To produce field observations with equivalent quality would require up to 550 sward height measurements for the study site (area: 8,059 m2), which is not feasible over larger extents required for conservation of key species or agronomic purposes. Synthesis and applications. We demonstrate how the collection of precise and detailed information on the spatial structure of grasslands can be made over management‐relevant extents. Aerial digital photographs can be transformed into surface models using an image‐based modelling approach: structure‐from‐motion and Multi‐View Stereo techniques. Image‐based measurements of sward heights were compared with manual sward height data captured on the same day. This novel source of vegetation spatial information could improve sward management for conservation and agronomy applications. The approach supports frequent surveys, at user‐controlled revisit times, and delivers data for spatial monitoring of key grassland functions and services.