The global UAV Aerial Survey Services market is experiencing robust growth, driven by increasing demand across diverse sectors. Technological advancements in drone technology, offering higher resolution imagery and improved data processing capabilities, are significantly contributing to this expansion. The market's versatility, providing cost-effective and efficient solutions for various applications, further fuels its growth. Specific sectors like construction, agriculture, and energy are key drivers, utilizing UAV surveys for site mapping, precision agriculture, pipeline inspections, and environmental monitoring. While regulatory hurdles and data security concerns present challenges, the market is overcoming these limitations through the development of standardized operating procedures and robust data encryption techniques. Assuming a conservative CAGR of 15% (a reasonable estimate given the rapid technological advancements and increasing adoption rates in this sector), and a 2025 market size of $2 billion, the market is projected to reach approximately $4.2 Billion by 2033. This substantial growth is further fueled by the increasing affordability and accessibility of UAV technology, enabling more businesses to leverage aerial survey services. The segmentation of the UAV Aerial Survey Services market reveals that orthophoto and oblique image services are widely utilized, catering to diverse application needs. Forestry and agriculture are dominant sectors, with construction, power and energy, and oil & gas industries rapidly adopting this technology. Regional analysis highlights strong growth in North America and Asia-Pacific, driven by significant investments in infrastructure development and agricultural modernization. Europe follows closely, spurred by government initiatives promoting sustainable development and environmental monitoring. The competitive landscape includes both established players like Kokusai Kogyo and Zenrin, and emerging specialized companies, indicating a dynamic and competitive market with potential for further consolidation and innovation. The continued development of advanced data analytics capabilities, integrated with UAV imagery, will create new opportunities and drive market expansion.

This dataset contains data used to test the protocol for high-resolution mapping and monitoring of recreational impacts in protected natural areas (PNAs) using unmanned aerial vehicle (UAV) surveys, Structure-from-Motion (SfM) data processing and geographic information systems (GIS) analysis to derive spatially coherent information about trail conditions (Tomczyk et al., 2023). Dataset includes the following folders:

Cocora_raster_data (~3GB) and Vinicunca_raster_data (~32GB) - a very high-resolution (cm-scale) dataset derived from UAV-generated images. Data covers selected recreational trails in Colombia (Valle de Cocora) and Peru (Vinicunca). UAV-captured images were processed using the structure-from-motion approach in Agisoft Metashape software. Data are available as GeoTIFF files in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru). Individual files are named as follows [location]_[year]_[product]_[raster cell size].tif, where:

[location] is the place of data collection (e.g., Cocora, Vinicucna)

[year] is the year of data collection (e.g., 2023)

[product] is the tape of files: DEM = digital elevation model; ortho = orthomosaic; hs = hillshade

[raster cell size] is the dimension of individual raster cell in mm (e.g., 15mm)

Cocora_vector_data. and Vinicunca_vector_data – mapping of trail tread and conditions in GIS environment (ArcPro). Data are available as shp files. Data are in the UTM projected coordinate system (UTM 18N for Colombia, UTM 19S for Peru).

Structure-from-motio n processing was performed in Agisoft Metashape (https://www.agisoft.com/, Agisoft, 2023). Mapping was performed in ArcGIS Pro (https://www.esri.com/en-us/arcgis/about-arcgis/overview, Esri, 2022). Data can be used in any GIS software, including commercial (e.g. ArcGIS) or open source (e.g. QGIS).

Tomczyk, A. M., Ewertowski, M. W., Creany, N., Monz, C. A., & Ancin-Murguzur, F. J. (2023). The application of unmanned aerial vehicle (UAV) surveys and GIS to the analysis and monitoring of recreational trail conditions. International Journal of Applied Earth Observations and Geoinformation, 103474. doi: https://doi.org/10.1016/j.jag.2023.103474

Two detailed geomorphological maps (1:2000) depicting landscape changes as a result of a glacial lake outburst flood were produced for the 2.1-km-long section of the Zackenberg river, NE Greenland. The maps document the riverscape before the flood (5 August 2017) and immediately after the flood (8 August 2017), illustrating changes to the riverbanks and morphology of the channel. A series of additional maps (1:800) represent case studies of different types of riverbank responses, emphasising the importance of the lateral thermo-erosion and bank collapsing as significant immediate effects of the flood. The average channel width increased from 40.75 m pre-flood to 44.59 m post-flood, whereas the length of active riverbanks decreased from 1729 to 1657 m. The new deposits related to 2017 flood covered 93,702 m2. The developed maps demonstrated the applicability of small Unmanned Aerial Vehicles (UAVs) for investigating the direct effects of floods, even in the harsh Arctic environment.

Hilltop Arboretum Dataset for GRASS GIS

This geospatial dataset contains raster data for the landform at Hilltop Arboretum, Baton Rouge, Louisiana, USA. This data was collected in an aerial survey with a DJI Phantom 4 Pro drone over Hilltop Arboretum on 12/31/2019 by Brendan Harmon and Josef Horacek. The aerial photographs were processed in Agisoft Metashape using Structure from Motion (SfM) to generate a point cloud, orthophotograph, and digital surface model. The point cloud was processed in CloudCompare to generate a bare earth point cloud. The orthophoto, digital surface model, and bare earth point cloud were imported into GRASS GIS. The bare earth point cloud was interpolated as a digital elevation model using the Regularized Spline with Tension method. The top level directory lousiana_s_spm_hilltop is a GRASS GIS location for the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Meters with EPSG code 26982. Inside the location there are the PERMANENT mapset, a license file, and readme file.

Survey

• Location: LSU Hilltop Arboretum, Baton Rouge, Louisiana, USA.
• Drone: DJI Phantom 4 Pro
• Software: Agisoft Metashape, CloudCompare, GRASS GIS
• Team: Brendan Harmon and Josef Horacek
• Date: 12/31/2019
• GCPs: 10 AeroPoints

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database directory. If you are new to GRASS GIS read the first time users guide.

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

The UAS Facility Maps are designed to identify permissible altitudes (above ground level) at which UAS, operating under the Small UAS Rule (14 CFR 107), can be authorized to fly within the surface areas of controlled airspace. These altitude parameters, provided by the respective air traffic control facilities, are criteria used to evaluate airspace authorization requests (14 CFR 107.41), submitted via FAA.GOV/UAS. Airspace authorization requests for altitudes in excess of the predetermined map parameters will require a lengthy coordination process. This dataset will be continually updated and expanded to include UAS Facility Maps for all controlled airspace by Fall 2017. This map is not updated in real time. Neither the map nor the information provided herein is guaranteed to be current or accurate. Reliance on this map constitutes neither FAA authorization to operate nor evidence of compliance with applicable aviation regulations in or during enforcement proceedings before the National Transportation Safety Board or any other forum. Disclaimer of Liability. The United States government will not be liable to you in respect of any claim, demand, or action—irrespective of the nature or cause of the claim, demand, or action—alleging any loss, injury, or damages, direct or indirect, that may result from the use or possession of any of the information in this draft map or any loss of profit, revenue, contracts, or savings or any other direct, indirect, incidental, special, or consequential damages arising out of any use of or reliance upon any of the information in this draft map, whether in an action in contract or tort or based on a warranty, even if the FAA has been advised of the possibility of such damages. The FAA’s total aggregate liability with respect to its obligations under this agreement or otherwise with respect to the use of this draft map or any information herein will not exceed $0. Some States, Territories, and Countries do not allow certain liability exclusions or damages limitations; to the extent of such disallowance and only to that extent, the paragraph above may not apply to you. In the event that you reside in a State, Territory, or Country that does not allow certain liability exclusions or damages limitations, you assume all risks attendant to the use of any of the information in this draft map in consideration for the provision of such information. Export Control. You agree not to export from anywhere any of the information in this draft map except in compliance with, and with all licenses and approvals required under, applicable export laws, rules, and regulations. Indemnity. You agree to indemnify, defend, and hold free and harmless the United States government from and against any liability, loss, injury (including injuries resulting in death), demand, action, cost, expense, or claim of any kind or character, including but not limited to attorney’s fees, arising out of or in connection with any use or possession by you of this draft map or the information herein. Governing Law. The above terms and conditions will be governed by the laws of each and every state within the United States, without giving effect to that state’s conflict-of-laws provisions. You agree to submit to the jurisdiction of the state or territory in which the relevant use of any of the information in this draft map occurred for any and all disputes, claims, and actions arising from or in connection with this draft map or the information herein.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of March 2019 is based on NPM Site Assessment 14 (as of 13 February 2019) and NPM UAV imagery (as of 23 January 2019).

Here below, the complete package by camp:

SW Map package KMZ file Drone image

The full image and shapefiles are available at this link.

This graph displays the projected size of the global market for unmanned aerial systems in 2020, with a breakdown by area of application. The commercial segment is expected to be sized at around 6.4 billion U.S. dollars. Commercial use of drones will likely kick off in the following areas: cinematography and photography, agriculture, inspection and maintenance, as well as geographic information systems (GIS); Amazon and DHL have already begun testing drone delivery, and so has Switzerland's postal service.

This map shows locations where STRI drones have flown. For each of these polygons, we have orthophotos, digital surface model (DSM) and cloud points. At the Smithsonian Tropical Research Institute (STRI) Panama, we have DJI Phantom 4 pro, Sense Fly eBee, 3DR Solo among other drones ready to flight, depending of the area, coverage and parts availability.If you need access to any of the byproducts, please, send us an email requesting the data.

"This dataset was collected by an Unmanned Aerial Vehicle in Aldabra gigi, Seychelles, on 20221024

Underwater or aerial images collected by scientists or citizens can have a wide variety of uses for science, ecosystems management, or conservation. These images can be annotated and shared to train IA models which can in turn predict the objects on such images. We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

The depot consists of the following elements: - 00_: Image preview panel - DCIM.zip: RAW images from UAV - GPS.zip: GIS file (Geopackage) containing the overflight area as well as the geolocation of the images accompanied by their thumbnails in the base64 attribute table. - METADATA.zip: Exif metadata in CSV format, OGC metadata in ISO19115 / 39 XML format, flight reports with thumbnails of drone images (tb folder) and flight statistics (html or pdf files).

Original tree structure: │ └─ 20221024_SYC-aldabra-gigi_UAV-02_29 │-------- └─ DCIM │-------- └─ GPS │-------- └─ METADATA

Flight survey information: - Camera model and parameters: Make: Hasselblad Model: L1D-20c Width: 5472 Height: 3648 Focal: 28 WhiteBalance: Manual ExposureMode: Auto Exposure ColoSpace: sRGB EV: -0.7 MeteringMode: CenterWeightedAverage Camera Pitch: -90.00

• Survey informations: No Images: 325 Median height: 151 meters Survey area: 51.44 hectares Survey from: 2022:10:24 15:21:26 to: 2022:10:24 15:40:13

More recent versions of this repository are available. These include data processed with OpenDroneMap"

NPM Bangladesh has produced a number of tools based on its regular data collection activities. The package of July 2019 is based on NPM Site Assessment 15 (as of 24 June 2019) and NPM most updated drone imagery (as of 23 January 2019).

Here below, the complete package by camp:

SW Map package KMZ file Drone image

The full image and shapefiles are available at this link.

A photogrammetry dataset was collected using an Unmanned Aerial Vehicle (quadcopter) over a river stretch of the Black Volta at Bamboi Bridge - Ghana. Also Ground Control Points (GCPs) were collected that represent black-and-white markers in the landscape. These can be used to better geographically constrain the photogrammetric solution. GCPs have been associated with row and column pixel location in each photo in which they appear.

The raw data was processed into a 3D point cloud using the open-source software platform WebOpenDroneMap (WebODM). The point cloud was analysed for removal of vegetation using spatial filtering techniques with the intent to make a bare-earth topographical map of the dry part of the riverbed. Both filtered and unfiltered point clouds were further processed into a Digital Surface Model (unfiltered) and Digital Terrain Model (DTM). The unfiltered dataset was also processed into an RGB orthophoto. In the thesis work of Hoogendoorn (2023) further research was done on combining the results of these datasets and analyses with wet bathymetry points collected using a fishfinder equipped with Real-Time-Kinematics GNSS, and using the outcoming full bathymetry for hydraulic modelling and understanding of relationships between wetted geometry and river discharge. For more information, we refer to the MSc thesis work of Hoogendoorn (2023)

The data files consist of three (3) .zip files. Unzip these to get access to all underlying files. For a quick overview, a .qgs file can be opened in QGIS. This will display all layers in a simple GIS project. The point cloud is also visualized but may take significant time before rendered, as points first need to be cached.

References: Hoogendoorn, N. J.: 3D River Discharge Modelling using UAV photogrammetry | TU Delft Repository, Delft University of Technology, Delft, The Netherlands, 2023.

Link: https://repository.tudelft.nl/record/uuid:d4088a50-3590-4675-9600-d715800841a3

The Airborne Photography System (APS) market is experiencing robust growth, projected to reach $289.1 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 6.2% from 2025 to 2033. This expansion is driven by several key factors. The increasing demand for high-resolution imagery across diverse sectors, including agriculture (precision farming), infrastructure development (construction monitoring and surveying), and environmental monitoring (disaster assessment and resource management), fuels market growth. Technological advancements in sensor technology, drone capabilities, and data processing software are significantly enhancing image quality, acquisition speed, and analytical capabilities, further boosting market adoption. Government initiatives promoting the use of advanced surveying technologies in infrastructure projects, especially in developed nations, contribute significantly to market expansion. The rise of 3D modeling and mapping applications also contributes to the increasing need for high-quality airborne imagery. Furthermore, the cost-effectiveness of APS compared to traditional methods like ground surveys, particularly for large-scale projects, makes it a compelling solution. Market segmentation reveals significant opportunities across various applications. The military and defense sector remains a major consumer of APS for intelligence gathering and surveillance. However, the civil engineering and agricultural sectors are experiencing rapid growth, driven by the increasing demand for efficient land management and precise monitoring of infrastructure projects. The choice of aerial platforms – unmanned aerial vehicles (UAVs or drones), helicopters, and fixed-wing aircraft – varies based on application, budget, and project scope, creating a diversified market landscape. North America and Europe are currently the leading regions, benefiting from robust infrastructure and early adoption of advanced technologies; however, the Asia-Pacific region is expected to show strong growth in the coming years due to rapid urbanization and economic development.

Airborne platforms used for testing the app and the various sites where flight tests were performed.

The global Digital Mapping Cameras (DMC) market is experiencing steady growth, projected to reach $230.5 million in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 3.2% from 2025 to 2033. This growth is fueled by increasing demand for high-resolution imagery across various sectors, including surveying, mapping, agriculture, and infrastructure development. The rising adoption of unmanned aircraft systems (UAS) or drones for aerial photography significantly contributes to market expansion, as they offer cost-effective and efficient data acquisition compared to traditional manned aircraft methods. Technological advancements, such as improved sensor technologies and enhanced image processing capabilities, further drive market expansion by enabling more accurate and detailed mapping solutions. Market segmentation reveals a strong preference for linear array scanners (pushbroom) due to their ability to capture high-quality imagery quickly and efficiently. The application of DMCs in manned aircraft remains significant, although the UAS segment is expected to witness faster growth due to its flexibility and lower operational costs. Competition within the market is robust, with established players such as Vexcel Imaging, Leica Geosystems, and Teledyne Optech alongside newer entrants continually innovating to enhance product offerings and cater to diverse customer needs. The North American market currently holds a dominant share, driven by robust technological advancements and substantial investments in infrastructure projects. However, the Asia-Pacific region is poised for significant growth in the coming years, fueled by rapid urbanization, infrastructure development, and increasing adoption of advanced mapping technologies. While factors like the high initial investment costs of DMCs and potential regulatory hurdles related to drone usage could act as restraints, the overall market outlook for digital mapping cameras remains positive, indicating considerable potential for growth and innovation over the forecast period. The market's evolution will likely see an increased emphasis on data analytics capabilities integrated with DMCs, enabling users to derive actionable insights from the acquired imagery, expanding the application scope beyond basic mapping and into areas like precision agriculture and environmental monitoring.

Flight Areas flown by STRI Drones Program

All information taken fromhttp://www.gsmchoice.co.uk/ [accessed 4 August 2015].

Example metadata records from the two handsets tested in the study.

GIS2DJI is a Python 3 program created to exports GIS files to a simple kml compatible with DJI pilot. The software is provided with a GUI. GIS2DJI has been tested with the following file formats: gpkg, shp, mif, tab, geojson, gml, kml and kmz. GIS_2_DJI will scan every file, every layer and every geometry collection (ie: MultiPoints) and create one output kml or kmz for each object found. It will import points, lines and polygons, and converted each object into a compatible DJI kml file. Lines and polygons will be exported as kml files. Points will be converted as PseudoPoints.kml. A PseudoPoints fools DJI to import a point as it thinks it's a line with 0 length. This allows you to import points in mapping missions. Points will also be exported as Point.kmz because PseudoPoints are not visible in a GIS or in Google Earth. The .kmz file format should make points compatible with some DJI mission software.

The original orthophoto and a Digital Surface Model (DSM), both based on the Horizontal Datum ITRF2000, were created by Dr Arko Lucieer of TerraLuma (http://www.terraluma.net/) and the University of Tasmania for the Terrestrial and Nearshore Ecosystems research group at the Australian Antarctic Division (TNE/AAD). These products are described by the metadata records: 'Orthophoto of the biopiles and nearby at Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 10 February 2013' with ID 'casey_biopiles_ortho_2013' and 'Digital Surface Model of the biopiles and nearby area at Casey, derived from aerial photographs taken with an Unmanned Aerial Vehicle (UAV), 10 February 2013' with ID 'casey_biopiles_DSM_2013'.

The products were requested for Australian Antarctic Science Project 4036: Remediation of petroleum contaminants in the Antarctic and subantarctic. The products were created from digital photos taken on the 10th February, 2013, with a Canon EOS 550D from a Mikrokopter Oktokopter piloted by Arko Lucieer and Zybnek Malenovsky. The products were georeferenced to ground control points surveyed using differential GPS by Dr Daniel Wilkins of TNE/AAD. Raw photo metadata: ISO-400, Focal Length 20mm, f/6.3 Exposure Time 1/1250 sec.

This metadata record describes a version of the orthophoto resulting from the georeferencing of the orthophoto to the WGS84 horizontal datum of the Australian Antarctic Data Centre's Casey GIS dataset. This was done by the Australian Antarctic Data Centre.

The 1 cm resolution digital surface model (DSM) was created from unmanned aerial vehicle (UAV) imagery acquired from a single day survey, July 28th 2016, in Cambridge Bay, Nunavut. Five control points taken from a Global Differential Positioning System were positioned in the corners and the center of the vegetation survey. The DSM covering 525m2 was produced by Canada Centre for Remote Sensing /Canada Centre for Mapping and Earth Observation. The UAV survey was completed in collaboration with the Canadian High Arctic Research Station (CHARS) for northern vegetation monitoring research. For more information, refer to our current Arctic vegetation research: Fraser et al; "UAV photogrammetry for mapping vegetation in the low-Arctic" Arctic Science, 2016, 2(3): 79-102. http://www.nrcresearchpress.com/doi/abs/10.1139/AS-2016-0008