This hosted tile layer provides aerial imagery for the City of Tempe. Imagery was taken in September 2017 and originally published May 2018.

LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery

My project based on the dataset, can be found on Github: https://github.com/MortenTabaka/Semantic-segmentation-of-LandCover.ai-dataset The dataset used in this project is the Landcover.ai Dataset, which was originally published with LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands, Water and Roads from Aerial Imagery paper also accessible on PapersWithCode.… See the full description on the dataset page: https://huggingface.co/datasets/MortenTabaka/LandCover-Aerial-Imagery-for-semantic-segmentation.

The Idaho Aerial Imagery Explorer enabled discovery, visualization, exploration, and downloading of publicly available digital georeferenced aerial imagery available from INSIDE Idaho at the University of Idaho Library. Any combination of filters (geographic _location, year, resolution, rectification, image type) can be used to narrow a search. Layers can be visualized and source image footprints with acquisition dates can be displayed. Collection names in the layer list can be hovered revealing a link to follow for additional information about a collection. Clicking on a point on the map results in a list of items available for download.New collections are added to the archive as they are received by the University of Idaho Library.

The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images.

Dataset features

land cover from Poland, Central Europe three spectral bands - RGB 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px) 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px) total area of 216.27 sq. km

Dataset format

rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system masks are single-channel GeoTiffs with EPSG:2180 spatial reference system

Aerial Mapping Market Outlook

The global aerial mapping market size is projected to witness significant growth over the forecast period, with an estimated valuation of USD 2.8 billion in 2023, reaching approximately USD 5.6 billion by 2032, reflecting a compound annual growth rate (CAGR) of around 8%. This robust growth can be attributed to the increasing demand for precise geospatial information across various sectors, driven by technological advancements and the integration of UAVs (Unmanned Aerial Vehicles) and satellite data. Factors such as urbanization, the expansion of smart cities, and the need for efficient environmental monitoring are further propelling the demand for aerial mapping solutions globally.

One of the primary growth factors for the aerial mapping market is the rapid urbanization and the resultant need for detailed urban planning and infrastructure development. With more than half of the global population now residing in urban areas, cities are expanding at an unprecedented rate, necessitating accurate and updated geospatial data for effective planning and management. Aerial mapping provides critical data that aids urban planners in designing and implementing infrastructure projects, optimizing traffic management systems, and ensuring efficient land use planning. Moreover, governments across the world are investing heavily in smart city initiatives, which rely extensively on aerial mapping for data acquisition and analysis, further fueling the market growth.

Another significant driver of the aerial mapping market is the increasing application of these solutions in disaster management and environmental monitoring. The frequency and intensity of natural disasters have risen due to climate change, raising the demand for advanced mapping solutions that can provide real-time data for disaster preparedness and response. Aerial mapping helps in accurately assessing the damage extent, facilitating efficient rescue and recovery operations. Additionally, the growing emphasis on environmental sustainability has led to increased adoption of aerial mapping for monitoring deforestation, tracking changes in land use, and assessing the impacts of climate change on various ecosystems. These applications play a vital role in enabling governments and organizations to devise effective strategies for environmental conservation and disaster risk reduction.

The adoption of advanced technologies, such as artificial intelligence (AI) and machine learning, is also a key factor contributing to the growth of the aerial mapping market. These technologies enhance the capabilities of aerial mapping by enabling automated data analysis, improving accuracy, and reducing the time required for data processing. The integration of AI with aerial mapping solutions allows for the extraction of valuable insights from vast amounts of geospatial data, facilitating better decision-making for various applications. Furthermore, the advent of cost-effective and efficient UAVs has made aerial mapping more accessible, particularly for small and medium enterprises, thereby broadening the market's customer base and driving growth.

The role of UAV Mapping Software has become increasingly prominent in the aerial mapping market, offering significant advantages in terms of data accuracy and processing efficiency. This software enables the seamless integration of data collected by UAVs, facilitating the conversion of raw aerial imagery into detailed and actionable geospatial information. With the ability to process large datasets quickly, UAV Mapping Software is crucial for applications that require real-time data analysis, such as disaster management and infrastructure development. The software's advanced algorithms and machine learning capabilities enhance the precision of data interpretation, making it an indispensable tool for organizations looking to leverage aerial mapping for strategic decision-making. As the demand for high-resolution mapping continues to grow, the development and adoption of sophisticated UAV Mapping Software are expected to play a pivotal role in shaping the future of the aerial mapping industry.

Component Analysis

The aerial mapping market is segmented by component into hardware, software, and services. Each of these components plays a crucial role in the overall functionality and effectiveness of aerial mapping solutions. The hardware segment includes UAVs, cameras, sensors, and other data collection devices that are essential for capturing high-resolution aerial imagery. The

The aerial imagery technology market is experiencing robust growth, driven by increasing demand across various sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the widespread adoption of drones and advanced sensor technologies is significantly lowering the cost and improving the efficiency of data acquisition. Secondly, the increasing need for precise geospatial data in sectors like agriculture (precision farming), construction (site surveying and monitoring), infrastructure development (planning and asset management), and environmental monitoring (disaster response and resource management) is boosting market demand. Furthermore, the development and deployment of sophisticated analytics platforms capable of processing and interpreting the vast amounts of data generated by aerial imagery are enhancing the value proposition for end-users. Significant market trends include the growing integration of Artificial Intelligence (AI) and Machine Learning (ML) for automated image analysis, the rise of cloud-based platforms for data storage and processing, and the increasing use of high-resolution sensors, such as hyperspectral and LiDAR, for more detailed and comprehensive data capture. While data security and privacy concerns pose potential restraints, ongoing technological advancements and regulatory efforts aim to mitigate these risks. Key players like EagleView Technologies, Fugro, and Nearmap are actively shaping the market landscape through innovation and strategic partnerships, solidifying their positions in this rapidly evolving technological space. The market is segmented geographically, with North America and Europe currently holding significant market share, although growth is anticipated in emerging markets in Asia-Pacific and Latin America.

The Aerial Photography Single Frame Records collection is a large and diverse group of imagery acquired by Federal organizations from 1937 to the present. Over 6.4 million frames of photographic images are available for download as medium and high resolution digital products. The high resolution data provide access to photogrammetric quality scans of aerial photographs with sufficient resolution to reveal landscape detail and to facilitate the interpretability of landscape features. Coverage is predominantly over the United States and includes portions of Central America and Puerto Rico. Individual photographs vary in scale, size, film type, quality, and coverage.

Detecting Landscape Objects on Satellite Images with Artificial Intelligence In recent years, there has been a significant increase in the use of artificial intelligence (AI) for image recognition and object detection. This technology has proven to be useful in a wide range of applications, from self-driving cars to facial recognition systems. In this project, the focus lies on using AI to detect landscape objects in satellite images (aerial photography angle) with the goal to create an annotated map of The Netherlands with all the coordinates of the given landscape objects.

Background Information

Problem Statement One of the things that Naturalis does is conducting research into the distribution of wild bees (Naturalis, n.d.). For their research they use a model that predicts whether or not a certain species can occur at a given location. Representing the real world in a digital form, there is at the moment not yet a way to generate an inventory of landscape features such as presence of trees, ponds and hedges, with their precise location on the digital map. The current models rely on species observation data and climate variables, but it is expected that adding detailed physical landscape information could increase the prediction accuracy. Common maps do not contain this level of detail, but high-resolution satellite images do.

Possible opportunities Based on the problem statement, there is at the moment at Naturalis not a map that does contain the level of detail where detection of landscape elements could be made, according to their wishes. The idea emerged that it should be possible to use satellite images to find the locations of small landscape elements and produce an annotated map. Therefore, by refining the accuracy of the current prediction model, researchers can gain a profound understanding of wild bees in the Netherlands with the goal to take effective measurements to protect wild bees and their living environment.

Goal of project The goal of the project is to develop an artificial intelligence model for landscape detection on satellite images to create an annotated map of The Netherlands that would therefore increase the accuracy prediction of the current model that is used at Naturalis. The project aims to address the problem of a lack of detailed maps of landscapes that could revolutionize the way Naturalis conduct their research on wild bees. Therefore, the ultimate aim of the project in the long term is to utilize the comprehensive knowledge to protect both the wild bees population and their natural habitats in the Netherlands.

Data Collection Google Earth One of the main challenges of this project was the difficulty in obtaining a qualified dataset (with or without data annotation). Obtaining high-quality satellite images for the project presents challenges in terms of cost and time. The costs in obtaining high-quality satellite images of the Netherlands is 1,038,575 $ in total (for further details and information of the costs of satellite images. On top of that, the acquisition process for such images involves various steps, from the initial request to the actual delivery of the images, numerous protocols and processes need to be followed.

After conducting further research, the best possible solution was to use Google Earth as the primary source of data. While Google Earth is not allowed to be used for commercial or promotional purposes, this project is for research purposes only for Naturalis on their research of wild bees, hence the regulation does not apply in this case.

World Imagery provides one meter or better satellite and aerial imagery for most of the world’s landmass and lower resolution satellite imagery worldwide. The map is currently comprised of the following sources: Worldwide 15-m resolution TerraColor imagery at small and medium map scales.Maxar imagery basemap products around the world: Vivid Premium at 15-cm HD resolution for select metropolitan areas, Vivid Advanced 30-cm HD for more than 1,000 metropolitan areas, and Vivid Standard from 1.2-m to 0.6-cm resolution for the most of the world, with 30-cm HD across the United States and parts of Western Europe. More information on the Maxar products is included below. High-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 30-cm to 3-cm resolution. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. Maxar Basemap ProductsVivid PremiumProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product provides 15-cm HD resolution imagery.Vivid AdvancedProvides committed image currency in a high-resolution, high-quality image layer over defined metropolitan and high-interest areas across the globe. The product includes a mix of native 30-cm and 30-cm HD resolution imagery.Vivid StandardProvides a visually consistent and continuous image layer over large areas through advanced image mosaicking techniques, including tonal balancing and seamline blending across thousands of image strips. Available from 1.2-m down to 30-cm HD. More on Maxar HD. Imagery UpdatesYou can use the Updates Mode in the World Imagery Wayback app to learn more about recent and pending updates. Accessing this information requires a user login with an ArcGIS organizational account. CitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

'USGS and Non USGS Agencies Aerial Photo Reference Mosaics inventory contains indexes to aerial photographs. The inventory contains imagery from various government agencies that are now archived at the USGS Earth Resources Observation and Science (EROS) Center. The film types, scales, and acquisition schedules differed according to project requirements. Low-, middle-, and high-altitude photographs were collected. '

This dataset was developed as part of a challenge to segment building footprints from aerial imagery. The goal of the challenge was to accelerate the development of more accurate, relevant, and usable open-source AI models to support mapping for disaster risk management in African cities [Read more about the challenge]. The data consists of drone imagery from 10 different cities and regions across Africa

The global digital mapping aerial photography market is experiencing robust growth, driven by increasing demand for precise geospatial data across diverse sectors. The market, valued at approximately $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033. This expansion is fueled by several key factors, including the proliferation of high-resolution sensors and drones, advancements in image processing and analysis techniques, and the rising adoption of cloud-based solutions for data storage and processing. Furthermore, the increasing need for accurate mapping in urban planning, infrastructure development, agriculture, and environmental monitoring contributes significantly to market growth. The integration of artificial intelligence (AI) and machine learning (ML) is further accelerating the automation of data processing and analysis, improving efficiency and reducing costs. Major players like Vexcel Imaging, Leica Geosystems, and Teledyne Optech are driving innovation through the development of advanced sensor technologies and software solutions. However, the market also faces certain challenges. High initial investment costs associated with specialized equipment and software can be a barrier to entry for smaller players. Data security and privacy concerns, along with the need for skilled professionals to operate and analyze data, also pose limitations. Nevertheless, the ongoing technological advancements and the increasing demand for precise geospatial data are expected to outweigh these challenges, ensuring continued market expansion in the coming years. The segmentation of the market by type of sensor (e.g., LiDAR, RGB), application (e.g., agriculture, urban planning), and region will further contribute to defining the market landscape and potential growth opportunities. This detailed understanding of market dynamics empowers stakeholders to make informed business decisions and capitalize on emerging trends.

This data set includes aerial photography of Barrow, Alaska, which has been geocorrected to a 2002 QuickBird satellite image or Interferometric Synthetic Aperture Radar (IFSAR) imagery. Photography included in the set is from these specific dates, from 1948 to 1997: 4 August 1948, 29 July 1949, 12-14 August 1955, 12-24 August 1962, 14 July 1964, 15 July 1979, 31 August 1984, and 16 July 1997.

Data are in GeoTIFF and ESRI Shapefile formats with FGDC compliant metadata. Data on DVD are available for ordering. Note: The data for 14 July 1964 span both DVDs. Send an email to NSIDC User Services at nsidc@nsidc.org to order the data.

Context and Aim

Deep learning in Earth Observation requires large image archives with highly reliable labels for model training and testing. However, a preferable quality standard for forest applications in Europe has not yet been determined. The TreeSatAI consortium investigated numerous sources for annotated datasets as an alternative to manually labeled training datasets.

We found the federal forest inventory of Lower Saxony, Germany represents an unseen treasure of annotated samples for training data generation. The respective 20-cm Color-infrared (CIR) imagery, which is used for forestry management through visual interpretation, constitutes an excellent baseline for deep learning tasks such as image segmentation and classification.

Description

The data archive is highly suitable for benchmarking as it represents the real-world data situation of many German forest management services. One the one hand, it has a high number of samples which are supported by the high-resolution aerial imagery. On the other hand, this data archive presents challenges, including class label imbalances between the different forest stand types.

The TreeSatAI Benchmark Archive contains:

• 50,381 image triplets (aerial, Sentinel-1, Sentinel-2)

• synchronized time steps and locations

• all original spectral bands/polarizations from the sensors

• 20 species classes (single labels)

• 12 age classes (single labels)

• 15 genus classes (multi labels)

• 60 m and 200 m patches

• fixed split for train (90%) and test (10%) data

• additional single labels such as English species name, genus, forest stand type, foliage type, land cover

The geoTIFF and GeoJSON files are readable in any GIS software, such as QGIS. For further information, we refer to the PDF document in the archive and publications in the reference section.

Version history

v1.0.0 - First release

Citation

Ahlswede et al. (in prep.)

GitHub

Full code examples and pre-trained models from the dataset article (Ahlswede et al. 2022) using the TreeSatAI Benchmark Archive are published on the GitHub repositories of the Remote Sensing Image Analysis (RSiM) Group (https://git.tu-berlin.de/rsim/treesat_benchmark). Code examples for the sampling strategy can be made available by Christian Schulz via email request.

Folder structure

We refer to the proposed folder structure in the PDF file.

• Folder “aerial” contains the aerial imagery patches derived from summertime orthophotos of the years 2011 to 2020. Patches are available in 60 x 60 m (304 x 304 pixels). Band order is near-infrared, red, green, and blue. Spatial resolution is 20 cm.

• Folder “s1” contains the Sentinel-1 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is VV, VH, and VV/VH ratio. Spatial resolution is 10 m.

• Folder “s2” contains the Sentinel-2 imagery patches derived from summertime mosaics of the years 2015 to 2020. Patches are available in 60 x 60 m (6 x 6 pixels) and 200 x 200 m (20 x 20 pixels). Band order is B02, B03, B04, B08, B05, B06, B07, B8A, B11, B12, B01, and B09. Spatial resolution is 10 m.

• The folder “labels” contains a JSON string which was used for multi-labeling of the training patches. Code example of an image sample with respective proportions of 94% for Abies and 6% for Larix is: "Abies_alba_3_834_WEFL_NLF.tif": [["Abies", 0.93771], ["Larix", 0.06229]]

• The two files “test_filesnames.lst” and “train_filenames.lst” define the filenames used for train (90%) and test (10%) split. We refer to this fixed split for better reproducibility and comparability.

• The folder “geojson” contains geoJSON files with all the samples chosen for the derivation of training patch generation (point, 60 m bounding box, 200 m bounding box).

CAUTION: As we could not upload the aerial patches as a single zip file on Zenodo, you need to download the 20 single species files (aerial_60m_…zip) separately. Then, unzip them into a folder named “aerial” with a subfolder named “60m”. This structure is recommended for better reproducibility and comparability to the experimental results of Ahlswede et al. (2022),

Join the archive

Model training, benchmarking, algorithm development… many applications are possible! Feel free to add samples from other regions in Europe or even worldwide. Additional remote sensing data from Lidar, UAVs or aerial imagery from different time steps are very welcome. This helps the research community in development of better deep learning and machine learning models for forest applications. You might have questions or want to share code/results/publications using that archive? Feel free to contact the authors.

Project description

This work was part of the project TreeSatAI (Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees at Infrastructures, Nature Conservation Sites and Forests). Its overall aim is the development of AI methods for the monitoring of forests and woody features on a local, regional and global scale. Based on freely available geodata from different sources (e.g., remote sensing, administration maps, and social media), prototypes will be developed for the deep learning-based extraction and classification of tree- and tree stand features. These prototypes deal with real cases from the monitoring of managed forests, nature conservation and infrastructures. The development of the resulting services by three enterprises (liveEO, Vision Impulse and LUP Potsdam) will be supported by three research institutes (German Research Center for Artificial Intelligence, TU Remote Sensing Image Analysis Group, TUB Geoinformation in Environmental Planning Lab).

Publications

Ahlswede et al. (2022, in prep.): TreeSatAI Dataset Publication

Ahlswede S., Nimisha, T.M., and Demir, B. (2022, in revision): Embedded Self-Enhancement Maps for Weakly Supervised Tree Species Mapping in Remote Sensing Images. IEEE Trans Geosci Remote Sens

Schulz et al. (2022, in prep.): Phenoprofiling

Conference contributions

S. Ahlswede, N. T. Madam, C. Schulz, B. Kleinschmit and B. Demіr, "Weakly Supervised Semantic Segmentation of Remote Sensing Images for Tree Species Classification Based on Explanation Methods", IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, “Exploring the temporal fingerprints of mid-European forest types from Sentinel-1 RVI and Sentinel-2 NDVI time series”, IEEE International Geoscience and Remote Sensing Symposium, Kuala Lumpur, Malaysia, 2022.

C. Schulz, M. Förster, S. Vulova and B. Kleinschmit, “The temporal fingerprints of common European forest types from SAR and optical remote sensing data”, AGU Fall Meeting, New Orleans, USA, 2021.

B. Kleinschmit, M. Förster, C. Schulz, F. Arias, B. Demir, S. Ahlswede, A. K. Aksoy, T. Ha Minh, J. Hees, C. Gava, P. Helber, B. Bischke, P. Habelitz, A. Frick, R. Klinke, S. Gey, D. Seidel, S. Przywarra, R. Zondag and B. Odermatt, “Artificial Intelligence with Satellite data and Multi-Source Geodata for Monitoring of Trees and Forests”, Living Planet Symposium, Bonn, Germany, 2022.

C. Schulz, M. Förster, S. Vulova, T. Gränzig and B. Kleinschmit, (2022, submitted): “Exploring the temporal fingerprints of sixteen mid-European forest types from Sentinel-1 and Sentinel-2 time series”, ForestSAT, Berlin, Germany, 2022.

The computer vision in geospatial imagery market is experiencing robust growth, driven by the increasing availability of high-resolution satellite and aerial imagery, coupled with advancements in artificial intelligence and machine learning algorithms. This convergence allows for automated analysis of vast geospatial datasets, unlocking valuable insights across diverse sectors. The market's expansion is fueled by the rising need for precise and timely information in applications like precision agriculture, urban planning, environmental monitoring, and infrastructure management. Energy sector applications, including pipeline inspection and renewable energy resource assessment, are also significant contributors to market growth. The adoption of smart camera-based systems is gaining traction, offering advantages in portability and real-time processing compared to traditional PC-based solutions. However, challenges remain, including the high cost of specialized hardware and software, the need for skilled professionals to interpret the complex outputs, and data privacy concerns related to the use of imagery data. The market is segmented by application (energy, environmental monitoring, and others) and by type (PC-based and smart camera-based systems), with North America currently holding a significant market share due to early adoption and technological advancements. Future growth will be significantly influenced by technological innovation, government regulations promoting data sharing and accessibility, and the increasing demand for data-driven decision-making in various industries. Despite challenges, the market is poised for continued expansion over the forecast period (2025-2033). The increasing affordability of computer vision technologies, coupled with the ongoing development of more user-friendly software solutions, will likely contribute to broader adoption across various sectors. The integration of cloud-based platforms is also expected to facilitate data processing and analysis, lowering barriers to entry for smaller businesses. Geographic expansion, particularly in developing economies with burgeoning infrastructure projects and agricultural needs, will be another key driver of market growth. Competition among established technology companies and emerging players will continue to intensify, leading to innovation in both hardware and software solutions. A focus on developing robust and reliable algorithms capable of handling complex and noisy data will be paramount for future market success.

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15-meter TerraColor imagery at small and mid-scales (~1:591M down to ~1:288k) for the world. The map features Maxar imagery at 0.3-meter resolution for select metropolitan areas around the world, 0.5-meter resolution across the United States and parts of Western Europe, and 0.6-meter resolution imagery across the rest of the world. In addition to commercial sources, the World Imagery map features high-resolution aerial photography contributed by the GIS User Community. This imagery ranges from 0.3-meter to 0.03-meter resolution, down to ~1:280 in select communities. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program.Updates and CoverageYou can use the World Imagery Updates app to learn more about recent updates and map coverage.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid (WGS84) web map.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.Precise Tile RegistrationThe World Imagery map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

This six inch pixel resolution color aerial photography was flown between July 1, 2008 and August 31, 2008. The files are provided in JPEG2000, an open format supported by most GIS and CAD software packages. Its intended usage for viewing is 1" = 100. The photography has been orthorectified to meet National Map Accuracy Standards for its capture scale. The images are georeferenced to the Illinois State Plane, Eastern Zone, using the NAD83 NSRS2007 horizontal datum. The data set is tiled for dissemination into many separate tiles, each of which is 2500 feet on a side.

This is a layer of aerial photos acquired from the Modoc National Forest with photo dates throughout the summer of 1994, scanned and orthorectified by the Geographical Information Center, California State Univ., Chico, with later processing by the California Department of Fish and Wildlife.It documents the location of aspen stands. It also provides the visual background for the display of polygons and the visual comparison of similar and more recent data sets. This is one of five data sets for a study investigating change in the size of aspen stands since 1946. Other available data sets are a 1946 aerial photo mosiac, interpreted polygons of individual aspen stands from 1946 and 1994, and a sample of aspen stand characteristics in 98 stands from 2002. Geographic extent of the imagery: The study area is the Warner Mountains south of State Highway 299.

Aerial Imaging Market Outlook

The global aerial imaging market size was valued at approximately USD 2.5 billion in 2023 and is poised to reach around USD 6.8 billion by 2032, growing at a robust CAGR of 11.5% during the forecast period. The growth factors driving this market include technological advancements in imaging systems, increased utilization of aerial imaging in various applications such as urban planning and disaster management, and a surge in demand from end-user segments like agriculture and military. Furthermore, the integration of AI and machine learning in aerial imaging for enhanced data analytics and predictive maintenance is also contributing to the market's expansion.

One of the key growth drivers for the aerial imaging market is the rapid advancement in drone technology. Drones have revolutionized aerial imaging by making it more affordable, accessible, and versatile. They can be deployed in various environments that are otherwise challenging for traditional aerial platforms like manned aircraft. The development of high-resolution cameras and sensors that can be mounted on drones has significantly improved the quality of imagery, further boosting their adoption across multiple sectors, from agriculture to real estate. The ability of drones to capture detailed images and videos at a fraction of the cost of conventional methods has made them a preferred choice for many applications.

Another significant factor contributing to the market's growth is the increasing use of aerial imaging for disaster management. In recent years, the frequency and intensity of natural disasters have risen, necessitating advanced tools for effective disaster assessment and response. Aerial imaging provides critical data that helps in real-time monitoring and assessment of disaster-stricken areas, aiding in efficient resource allocation and rescue operations. The ability to capture high-resolution images from different angles provides a comprehensive view of the affected areas, enabling authorities to formulate better strategies for disaster management and mitigation.

The integration of aerial imaging with geographic information systems (GIS) is also propelling market growth. GIS allows for the visualization, analysis, and interpretation of spatial data, which is essential for applications such as urban planning, environmental monitoring, and infrastructure development. Aerial images, when combined with GIS, provide an in-depth understanding of geographic patterns and relationships, facilitating better decision-making processes. This integration is particularly beneficial for urban planners and environmentalists who rely on accurate and detailed spatial data for their projects.

The evolution of aerial imaging technology has been significantly influenced by the development of the Aerial Mapping Camera. This advanced camera system is designed to capture high-resolution images from the sky, providing detailed and accurate data for various applications. The Aerial Mapping Camera is particularly beneficial in geospatial mapping, where precision and clarity are paramount. Its ability to capture images from multiple angles and altitudes enhances the quality of the data collected, making it a valuable tool for urban planners, environmentalists, and researchers. As the demand for detailed aerial imagery continues to grow, the Aerial Mapping Camera plays a crucial role in meeting these needs by delivering superior image quality and reliability.

Regionally, North America holds the largest share of the aerial imaging market, driven by technological advancements, a well-established infrastructure, and high adoption rates across various sectors. The presence of major market players and supportive government initiatives further bolster the market in this region. Europe follows closely, with significant contributions from countries like Germany, France, and the UK, where aerial imaging is extensively used in agriculture, urban planning, and environmental monitoring. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, attributed to rapid urbanization, increased government spending on infrastructure projects, and the growing adoption of advanced technologies in countries like China, India, and Japan.

Platform Analysis

The aerial imaging market can be segmented by platform

The imagery posted on this site is of the Florida coast after Hurricane Wilma made landfall. The regions photographed range from Key West to Sixmile Bend, Florida. The aerial photograph missions were conducted by the NOAA Remote Sensing Division the day after Wilma made landfall, October 25 and concluded October 27. The images were acquired from an altitude of 7,500 feet, using an Emerge/Applanix Digital Sensor System (DSS). Over 1000 aerial images were obtained during this time period, with most available to view online and download.