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.

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.

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

This Web Map is included in the Mitigating Marshes Against Sea Level Rise: Thin Layer Placement Experiment application.The National Estuarine Research Reserve (NERR) System Science Collaborative funded a two-year experiment at 8 different NERR sites to provide broad geographic scale, including Chesapeake Bay NERR in Virginia. The three core research questions they aim to answer include: “Is sediment addition an effective adaptation strategy for marshes in the face of sea level rise? How does marsh resilience respond to different levels of sediment addition? How do low versus high marsh habitats differ in their response to this restoration strategy?”.This Story Map is a tool for 6th-12th grade teachers to help teach students about marshes and thin layer placement restoration techniques by exploring maps, videos, and images. Students will analyze how vegetation has changed in the Chesapeake Bay National Estuarine Research Reserve in Virginia (CBNERR-VA) marsh experiment plots in the first year of monitoring. They will evaluate images and graphs different treatments and determine which could be used as a possible restoration technique to combat sea level rise in marshes.Data: https://www.vims.edu/cbnerr/resources/gis-data-layers/index.php

Human life is precious and in the event of any unfortunate occurrence, highest efforts are made to safeguard it. To provide timely aid or undertake extraction of humans in distress, it is critical to accurately locate them. There has been an increased usage of drones to detect and track humans in such situations. Drones are used to capture high resolution images during search and rescue purposes. It is possible to find survivors from drone feed, but that requires manual analysis. This is a time taking process and is prone to human errors. This model can detect humans by looking at drone imagery and can draw bounding boxes around the location. This model is trained on IPSAR and SARD datasets where humans are on macadam roads, in quarries, low and high grass, forest shade, and Mediterranean and Sub-Mediterranean landscapes. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of detection, reducing the time and effort required significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution (1-5 cm) individual drone images or an orthomosaic.OutputFeature class containing detected humans.Applicable geographiesThe model is expected to work well in Mediterranean and Sub-Mediterranean landscapes but can also be tried in other areas.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 82.2 percent for human class.Training dataThis model is trained on search and rescue dataset provided by IPSAR and SARD.LimitationsThis model has a tendency to maximize detection of humans and errors towards producing false positives in rocky areas.Sample resultsHere are a few results from the model.

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.

Creation 18 march 2015, that example take the regulation of the FAA rulesRevised at each 6 month for the Aeronautical dataNo drone near: Airport large medium small, heliport, seaplane, Air Space Risk, Class B C D, NEW LAYERS:Nuclear Power, Prisons, World Urban add Near-Real-Time Surface In-Sit from NOAA for the Weather and Wind!TRY THAT ONE (ICAO app with App Builder): Where you can fly drones!Get a better understanding of where you can fly drones!Study about the DRONE where we have the right to fly and it will be safe...Waiting for clarification and rules...If you go to http://gis.icao.int/drone You have the nearby function very cool to fly your DRONE more safety...The Drone fly tools APP work on all Device an is simple to useCreation 18 march 2015, Get a better understanding of where you can fly drones! Try the Widget SUASLP RISK Special Use Airspace, USE SPACIAL FILTER: Select spacial filter, user defined area to select the zone where you want to fly; click to apply and see the result of areas that you don't have the right to go! Try Class B C D tool widget, Try Class A B C D E F G tool widget and Class G tool widget. Other widget like Query No Drone near Airports... Study about the DRONE where we have the right to fly and it will be safe...Waiting for clarification and rules...Get a better understanding of where you can fly drones! 9631 Airports with 5 miles buffer, 46325 Airports with 5 miles buffer from ourports.com, SUAS lower with all CLASS, many other layers like: ROUTES, CTA, CTR, SCTR, World Wind from NOAA, RESTRICTED AREA and more...working on Heliport databaseOther one (ICAO app with App Builder): Where you can fly drones! More tools!

Drone products captured by CDFW staff for Hope Valley Wildlife Area.

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

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

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.

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

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.

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of September - October 2018 is based on NPM Site Assessment 12 (as of 10 October) and NPM most updated drone imagery (as of 26 September).

Here below, the complete package by camp:

• SW Map package
• KMZ file
• Drone image

The full image and shapefiles are available at this link.

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.

Snapshot img

GIS In Telecom Sector Market Size 2024-2028

The GIS in telecom sector market size is forecast to increase by USD 1.91 billion at a CAGR of 14.68% between 2023 and 2028.

Geographic Information Systems (GIS) have gained significant traction In the telecom sector due to the increasing adoption of advanced technologies such as big data, sensors, drones, and LiDAR. The use of GIS enables telecom companies to effectively manage and analyze large volumes of digital data, including satellite and GPS information, to optimize infrastructure monitoring and antenna placement. In the context of smart cities, GIS plays a crucial role in enabling efficient communication between developers and end-users by providing real-time data on construction progress and infrastructure status. Moreover, the integration of LiDAR technology with drones offers enhanced capabilities for surveying and mapping telecom infrastructure, leading to improved accuracy and efficiency.
However, the implementation of GIS In the telecom sector also presents challenges, including data security concerns and the need for servers and computers to handle the large volumes of data generated by these technologies. In summary, the telecom sector's growing reliance on digital technologies such as GIS, big data, sensors, drones, and LiDAR is driving market growth, while the need for effective data management and security solutions presents challenges that must be addressed.

What will be the Size of the GIS In Telecom Sector Market During the Forecast Period?

Request Free Sample

The Geographic Information System (GIS) market In the telecom sector is experiencing significant growth due to the increasing demand for electronic information and visual representation of data in various industries. This market encompasses a range of hardware and software solutions, including GNSS/GPS antennas, Lidar, GIS collectors, total stations, imaging sensors, and more. Major industries such as agriculture, oil & gas, architecture, and infrastructure monitoring are leveraging GIS technology for data analysis and decision-making. The adoption rate of GIS In the telecom sector is driven by the need for efficient data management and analysis, as well as the integration of real-time data from various sources.
Data formats and sources vary widely, from satellite and aerial imagery to ground-based sensors and IoT devices. The market is also witnessing innovation from startups and established players, leading to advancements in data processing capabilities and integration with other technologies like 5G networks and AI. Applications of GIS In the telecom sector include smart urban planning, smart utilities, and smart public works, among others.

How is this GIS In Telecom Sector Industry segmented and which is the largest segment?

The GIS in telecom sector industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Product

  Software
  Data
  Services


Deployment

  On-premises
  Cloud


Geography

  APAC

    China


  North America

    Canada
    US


  Europe

    UK
    Italy


  South America



  Middle East and Africa

By Product Insights

The software segment is estimated to witness significant growth during the forecast period. The telecom sector's Global GIS market encompasses software solutions for desktops, mobiles, cloud, and servers, along with developers' platforms. companies provide industry-specific GIS software, expanding the growth potential of this segment. Telecom companies heavily utilize intelligent maps generated by GIS for informed decisions on capacity planning and enhancements, such as improved service and next-generation networks. This drives significant growth In the software segment. Commercial entities offer open-source GIS software to counteract the threat of counterfeit products.
GIS technologies are integral to telecom network management, spatial data analysis, infrastructure planning, location-based services, network coverage mapping, data visualization, asset management, real-time network monitoring, design, wireless network mapping, integration, maintenance, optimization, and geospatial intelligence. Key applications include 5G network planning, network visualization, outage management, geolocation, mobile network optimization, and smart infrastructure planning. The GIS industry caters to major industries, including agriculture, oil & gas, architecture, engineering, construction, mining, utilities, retail, healthcare, government, and smart city planning. GIS solutions facilitate real-time data management, spatial information, and non-spatial information, offering enterprise solutions and transportation applications.

Get a glance at the market report of share of variou

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.

The drone imagery services market is experiencing significant growth, driven by advancements in drone technology, increasing demand for high-resolution imagery across various sectors, and the decreasing cost of drone acquisition and operation. The market size, currently valued at XXX million, is projected to exhibit a Compound Annual Growth Rate (CAGR) of XX% from 2025 to 2033, reaching a substantial market value by the end of the forecast period. Key drivers include the rising adoption of drones for precision agriculture (monitoring crop health, optimizing irrigation), infrastructure inspection (bridges, pipelines, power lines), surveying and mapping (creating detailed 3D models), and security and surveillance applications. Emerging trends such as artificial intelligence (AI)-powered image analysis, the development of more sophisticated drone sensors (e.g., hyperspectral, thermal), and the integration of drone imagery with Geographic Information Systems (GIS) are further fueling market expansion. However, restraining factors include stringent regulations concerning drone operations, concerns about data privacy and security, and the potential for technological malfunctions and accidents. Market segmentation reveals a significant contribution from the civilian drone segment, which finds wide application across various sectors including government agencies, energy (particularly oil & gas infrastructure monitoring), agriculture & forestry (precision farming, deforestation monitoring), civil engineering (construction site surveying), and commercial enterprises (real estate, marketing). The military drone segment, though smaller in overall volume, commands a higher value per service due to specialized applications and high-end sensor technology. Geographically, North America and Europe currently dominate the market, fueled by high technological advancements and regulatory frameworks, but the Asia-Pacific region is anticipated to showcase significant growth potential in the coming years due to rapid industrialization and increasing infrastructure development. Key players like Blom ASA, EagleView Technology, and Fugro are leading the market through technological innovation and strategic partnerships. The competitive landscape is characterized by both established players and emerging companies, leading to continuous innovation and diversification of services. The market is witnessing the emergence of specialized service providers focusing on niche applications, further strengthening the competitive dynamics. Future growth will depend on overcoming regulatory hurdles, promoting user confidence, and developing more sophisticated analytics capabilities to extract actionable insights from drone imagery data. The successful integration of AI and machine learning into image processing and analysis will be a key differentiator for market participants. The increasing demand for sustainable and cost-effective solutions across various sectors will further propel the market's growth. Regional variations in regulations and technological adoption will continue to influence market dynamics, with regions experiencing rapid economic growth and urbanization expected to demonstrate higher growth rates. The continued development of robust data security protocols and addressing concerns around data privacy will be critical for ensuring market expansion and acceptance. Finally, advancements in drone technology, such as longer flight times, increased payload capacity, and improved sensor technology, will shape the future of the drone imagery services market, ensuring its sustained growth trajectory over the forecast period.

Flight paths of drone surveys used to capture imagery for the July 6, 2021, Saint Joachim, ON downburst. Ground survey conducted July 7, 2021. DJI Air 2S performed 3 flights. Please note that drones are also used for scouting the initial area of interest using a live view on the controller, meaning that some flight paths may not be associated with any imagery. Does not include flights where drone mapping was performed.View event map here

NPM Bangladesh has produced a number of tools based on its regular data collection activities and drone flights. The package of May 2018 is based on NPM Site Assessment 10 (as of 20 May) and NPM drone imagery (as of 23 May).

Here below, the complete package by camp:

• SW Map package
• KMZ file
• Drone image

The full image and shapefiles are available at this link.