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.Vantor 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 Vantor 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. Vantor 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 Vantor 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.

This dataset provides a seamless cloud-free 10m resolution satellite imagery layer of the New Zealand mainland and offshore islands.

The imagery was captured by the European Space Agency Sentinel-2 satellites between September 2022 - April 2023.

Data comprises: • 450 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:50000 tile layout. • Satellite sensors: ESA Sentinel-2A and Sentinel-2B • Acquisition dates: September 2022 - April 2023 • Spectral resolution: R, G, B • Spatial resolution: 10 meters • Radiometric resolution: 8-bits (downsampled from 12-bits)

This is a visual product only. The data has been downsampled from 12-bits to 8-bits, and the original values of the images have been modified for visualisation purposes.

Also available on: • Basemaps • NZ Imagery - Registry of Open Data on AWS

The cost of acquiring a satellite data was highest for the images from the GeoEye-1 satellite, at ** U.S. dollars per square kilometer of the image. Most of the satellite data have a minimum order quantities based on the company and the cost depends mostly on the spatial resolution of the satellite image. Most of the satellites are commercially owned and provide users with data as an end product based on the requirement. Processing smaller patches of the raw images obtained from a satellite to an end product are not profitable. Hence, there is a minimum order limit of ** to ** square kilometers based on the requested product.

We have collected a dataset of high-resolution satellite images of airport landscapes, ranging in size from 1500 by 1500 px to 6000 by 6000 px. The dataset includes a total of 4204 runway instances, which were generated through the labeling process. The images were collected from the Federal Aviation Administration (FAA) sources and cover a wide range of geographical locations across the United States. The dataset is diverse in terms of airport sizes, runway shapes, and surroundings, providing a comprehensive sample for training and testing object detection models. We believe this dataset will contribute to advancing the field of airport runway detection using satellite imagery and enable the development of more accurate and efficient models for this important task. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F9357365%2Fcdc16f9e8ebf6775b25f13ea8f8545e5%2FH_0009.png?generation=1680818126663850&alt=media" alt="">

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 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. The map features 0.5m resolution imagery in the continental United States and parts of Western Europe from DigitalGlobe. Additional DigitalGlobe sub-meter imagery is featured in many parts of the world. In the United States, 1 meter or better resolution NAIP imagery is available in some areas. In other parts of the world, imagery at different resolutions has been contributed by the GIS User Community. In select communities, very high resolution imagery (down to 0.03m) is available down to ~1:280 scale. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. View the list of Contributors for the World Imagery Map.CoverageView the links below to learn more about recent updates and map coverage:What's new in World ImageryWorld coverage mapCitationsThis 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. A similar raster web map, Imagery with Labels, is also available.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.

Lebanon has traditionally been a major potato producer with 451,860 tons produced in 2014. Generally, potatoes make up 30% of the total Lebanese agricultural exports where approximately 60% of the potato production is exported to the Arab region, the UK and Brazil. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m including three 60 m bands pixel resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. Most Lebanese farmers inherit their land from their parents over generations, and as a result most still use traditional farming techniques for irrigation, where decisions are based on prior generations’ practices. However, with the changes in climate conditions within the region, these practices are no longer as efficient as they used to be. Normalized Difference Water Indices are calculated from satellite bands in the near-infrared and short-wave infrared to provide a better understanding about the water stress status of crops within the field. Preliminary results demonstrate that Sentinel-2 data can provide detailed and timely data for farmers to effectively manage fields. Despite the fact that most Lebanese farmers rely on traditional farming methods, providing them with crop health information on their mobile phones and allowing them to test its efficiency has the potential to be a catalyst to help them improve their farming practices.

This web map contains the Bing Maps aerial imagery web mapping service, which offers worldwide orthographic aerial and satellite imagery. Coverage varies by region, with the most detailed coverage in the USA and United Kingdom. Coverage in different areas within a country also varies in detail based on the availability of imagery for that region. Bing Maps is continuously adding imagery in new areas and updating coverage in areas of existing coverage. This map does not include bird's eye imagery. Information regarding monthly updates of imagery coverage are available on the Bing Community blog. Post a comment to the Bing Community blog to request imagery vintage information for a specific area.Tip: The Bing Maps Aerial service is one of the basemaps used in the ArcGIS.com map viewer and ArcGIS Explorer Online. Simply click one of those links to launch the interactive application of your choice, and then choose Bing Maps Aerial from the Basemap control to start browsing! You'll also find this service in the Basemap gallery in ArcGIS Explorer Desktop and ArcGIS Desktop 10.If you need information on how to access Bing Maps, information is available in the ArcGIS Online Content Resource Center.See Bing Maps (http://www.bing.com/maps) for more information about the Bing Maps mapping system, terms of use, and a complete list of data suppliers.

Multispectral imagery captured by Sentinel-2 satellites, featuring 13 spectral bands (visible, near-infrared, and short-wave infrared). Available globally since 2018 (Europe since 2017) with 10-60 m spatial resolution and revisit times of 2-3 days at mid-latitudes. Accessible through the EOSDA LandViewer platform for visualization, analysis, and download.

This dataset consists of annotated high-resolution aerial imagery of roof materials in Bonn, Germany, in the Ultralytics YOLO instance segmentation dataset format. Aerial imagery was sourced from OpenAerialMap, specifically from the Maxar Open Data Program. Roof material labels and building outlines were sourced from OpenStreetMap. Images and labels are split into training, validation, and test sets, meant for future machine learning models to be trained upon, for both building segmentation and roof type classification.The dataset is intended for applications such as informing studies on thermal efficiency, roof durability, heritage conservation, or socioeconomic analyses. There are six roof material types: roof tiles, tar paper, metal, concrete, gravel, and glass.Note: The data is in a .zip due to file upload limits. Please find a more detailed dataset description in the README.md

The Ontario Imagery Web Map Service (OIWMS) is an open data service available to everyone free of charge. It provides instant online access to the most recent, highest quality, province wide imagery. GEOspatial Ontario (GEO) makes this data available as an Open Geospatial Consortium (OGC) compliant web map service or as an ArcGIS map service. Imagery was compiled from many different acquisitions which are detailed in the Ontario Imagery Web Map Service Metadata Guide linked below. Instructions on how to use the service can also be found in the Imagery User Guide linked below. Note: This map displays the Ontario Imagery Web Map Service Source, a companion ArcGIS web map service to the Ontario Imagery Web Map Service. It provides an overlay that can be used to identify acquisition relevant information such as sensor source and acquisition date. OIWMS contains several hierarchical layers of imagery, with coarser less detailed imagery that draws at broad scales, such as a province wide zooms, and finer more detailed imagery that draws when zoomed in, such as city-wide zooms. The attributes associated with this data describes at what scales (based on a computer screen) the specific imagery datasets are visible. Available Products Ontario Imagery OGC Web Map Service – public linkOntario Imagery ArcGIS Map Service – public linkOntario Imagery Web Map Service Source – public linkOntario Imagery ArcGIS Map Service – OPS internal linkOntario Imagery Web Map Service Source – OPS internal linkAdditional Documentation Ontario Imagery Web Map Service Metadata Guide (PDF)Ontario Imagery Web Map Service Copyright Document (PDF) Imagery User Guide (Word)StatusCompleted: Production of the data has been completed Maintenance and Update FrequencyAnnually: Data is updated every year ContactOntario Ministry of Natural Resources, Geospatial Ontario, imagery@ontario.ca

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

Wayback is a digital archive, providing users with access to the different versions of World Imagery created over time. Each layer in the archive represents a snapshot of the entire World Imagery map, as it existed on the date it was published. This Wayback layer is the June 26, 2025 version of World Imagery. See World Imagery (Wayback 2025-06-26) Metadata for detailed information about each image source in this layer.World Imagery provides one meter or better satellite and aerial imagery for much of the world, and lower resolution satellite imagery worldwide. As World Imagery is updated with more current imagery, new versions of the map are published. When and where updates occur, the previous imagery is replaced and is no longer visible. For many use cases, the new imagery is more desirable and typically preferred. Other times, however, the previous imagery may support use cases that the new imagery does not. In these cases, a user may need to access a previous version of World Imagery.Wayback currently provides access to all published versions of World Imagery, dating back to February 20, 2014. There is an ArcGIS Online item for every version which can be viewed in the Wayback Imagery group.

This dataset contains remote sensing data for every village in the state of Bihar, India. For most of these villages, the data contains the corresponding electrification rate as reported by the Garv data platform from the Indian government as of July 2017. This dataset contains satellite imagery, political boundaries, lights at night imagery, rainfall measurements, and vegetation indices data for 45,220 villages and the electrification rate data for 32,817 of those villages. This dataset may be of particular interest to those investigating how electricity access maps to infrastructure and agricultural production. This dataset was compiled as part of the Summer 2017 Duke University Data+ team, titled "Electricity Access in Developing Countries from Aerial Imagery."

This imagery service contains natural color orthophotos covering counties in north Florida that had imagery captured from October 2012 till spring 2013. An orthophoto is remotely sensed image data in which displacement of features in the image caused by terrain relief and sensor orientation have been mathematically removed. Orthophotography combines the image characteristics of a photograph with the geometric qualities of a map. Counties covered in this dataset are: Bay, Bradford, Calhoun, Columbia, Dixie, Duval, Escambia, Franklin, Gadsden, Gilchrist, Gulf, Hamilton, Holmes, Jackson, Jefferson, Lafayette, Levy, Madison, Okaloosa, Palm Beach (partial), Santa Rosa, Suwannee, Taylor, Union, Wakulla, Walton, and Washington. Please contact GIS.Librarian@FloridaDEP.gov for more information.

This collection is a legacy product that is no longer supported. It may not meet current government standards. This inventory presents chronologically the satellite images acquired, orthorectified and published over time by Natural Resources Canada. It is composed of imagery from the Landsat7 (1999-2003) and RADARSAT-1 (2001-2002) satellites, as well as the CanImage by-product and the control points used to process the images. Landsat7 Orthorectified Imagery: The orthoimage dataset is a complete set of cloud-free (less than 10%) orthoimages covering the Canadian landmass and created with the most accurate control data available at the time of creation. RADARSAT-1 Orthorectified Imagery: The 5 RADARSAT-1 images (processed and distributed by RADARSAT International (RSI) complete the landsat 7 orthoimagery coverage. They are stored as raster data produced from SAR Standard 7 (S7) beam mode with a pixel size of 15 m. They have been produced in accordance with NAD83 (North American Datum of 1983) using the Universal Transverse Mercator (UTM) projection. RADARSAT-1 orthoimagery were produced with the 1:250 000 Canadian Digital Elevation Data (CDED) and photogrammetric control points generated from the Aerial Survey Data Base (ASDB). CanImage -Landsat7 Orthoimages of Canada,1:50 000: CanImage is a raster image containing information from Landsat7 orthoimages that have been resampled and based on the National Topographic System (NTS) at the 1:50 000 scale in the UTM projection. The product is distributed in datasets in GeoTIFF format. The resolution of this product is 15 metres. Landsat7 Imagery Control Points: the control points were used for the geometric correction of Landsat7 satellite imagery. They can also be used to correct vector data and for simultaneously displaying data from several sources prepared at different scales or resolutions.

This dataset contains satellite imagery of 4,454 power plants within the United States. The imagery is provided at two resolutions: 1m (4-band NAIP iamgery with near-infrared) and 30m (Landsat 8, pansharpened to 15m). The NAIP imagery is available for the U.S. and Landsat 8 is available globally. This dataset may be of value for computer vision work, machine learning, as well as energy and environmental analyses.Additionally, annotations of the specific locations of the spatial extent of the power plants in each image is provided. These annotations were collected via the crowdsourcing platform, Amazon Mechanical Turk, using multiple annotators for each image to ensure quality. Links to the sources of the imagery data, the annotation tool, and the team that created the dataset are included in the "References" section.To read more on these data, please refer to the "Power Plant Satellite Imagery Dataset Overview.pdf" file. To download a sample of the data without downloading the entire dataset, download "sample.zip" which includes two sample powerplants and the NAIP, Landsat 8, and binary annotations for each.Note: the NAIP imagery may appear "washed out" when viewed in standard image viewing software because it includes a near-infrared band in addition to the standard RGB data.

The Spain Satellite Imagery Services market, valued at €160 million in 2025, is projected to experience robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 11.77% from 2025 to 2033. This expansion is driven by several key factors. Firstly, increasing government investments in infrastructure projects, particularly within the transportation and construction sectors, fuel the demand for precise geospatial data. Secondly, the growing adoption of satellite imagery in agriculture and forestry for precision farming and resource management contributes significantly to market growth. Enhanced surveillance and security needs, coupled with the rising importance of disaster management and environmental conservation initiatives, further solidify the market's upward trajectory. The market's segmentation reveals that the government sector holds a substantial share of the market, followed by the construction and transportation sectors. Key players like Airbus SE, Hexagon AB, and Maxar Technologies Inc., alongside several specialized firms, are actively shaping the competitive landscape through technological advancements and strategic partnerships. The market's historical period (2019-2024) likely saw a slower growth rate than the projected forecast period, reflecting an increasing market maturity and wider adoption. The forecast period (2025-2033) anticipates a significant acceleration in market growth, driven by factors including the increasing affordability of satellite imagery, advancements in image processing technologies (allowing for faster and more accurate analysis), and a heightened awareness of the benefits of satellite imagery across diverse sectors. The continued focus on sustainable development and the need for accurate environmental monitoring will further bolster the market's growth. While the market faces certain restraints, such as data security concerns and the need for skilled professionals to interpret the data, the overall market outlook remains optimistic due to the compelling applications and the ongoing technological advancements within the industry. The Spanish market, specifically, is likely to benefit from its strategic geographical location and increasing investments in technological infrastructure. This report provides a detailed analysis of the Spain satellite imagery services market, offering invaluable insights for businesses and investors seeking to navigate this dynamic sector. With a study period spanning 2019-2033, a base year of 2025, and a forecast period from 2025-2033, this report offers a comprehensive overview of market size, trends, and future projections, valued in millions. The report covers key market segments including applications (Geospatial Data Acquisition and Mapping, Natural Resource Management, Surveillance and Security, Conservation and Research, Disaster Management, Intelligence) and end-users (Government, Construction, Transportation and Logistics, Military and Defense, Forestry and Agriculture, Others). Recent developments include: July 2023: Maxar Technologies, a provider of comprehensive space services and secure, precise geospatial intelligence, announced the initial release of its new Maxar Geospatial Platform (MGP), enabling fast and easy access to the world's most advanced Earth intelligence. MGP will simplify geospatial data and analytics discovery, purchasing, and integration.MGP users will have access to Maxar's industry-leading geospatial content, including high-resolution satellite imagery, stunning imagery base maps, 3D models, analysis-ready data, and image-based change detection and analytic outputs., September 2022: Planet added another type of imagery satellite to its product line, the latest development of the firm's data-gathering operations. The company called the new satellites Tanager -- named after a bird family like the current lines of Dove and Pelican satellites it manufactures. But unlike those satellites, which have cameras and detectors capturing images in the same range as the human eye, the Tanager satellites will capture 'hyperspectral' imagery, separating the light spectrum into hundreds of bands of light.. Key drivers for this market are: Increasing Adoption of Location-based Services, Satellite data usage is increasing. Potential restraints include: Strict government regulations, High-resolution Images Offered by Other Imaging Technologies. Notable trends are: Surveillance and Security is Expected to Hold Significant Share.

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.Vantor 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 Vantor 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. Vantor 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 Vantor 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.

The global satellite imagery and image processing services market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are providing higher-resolution imagery with improved accuracy and faster processing times, enabling more detailed analysis for various applications. Secondly, the rising adoption of cloud-based platforms for image processing and analytics is streamlining workflows and reducing costs for users. This is particularly crucial for smaller businesses and organizations that previously lacked access to sophisticated image processing capabilities. Thirdly, the growing need for precise geographical information across diverse sectors, including environmental monitoring, precision agriculture, urban planning, and disaster response, fuels market demand. The defense and security sector remains a significant contributor, with increasing reliance on satellite imagery for intelligence gathering and surveillance. Market segmentation reveals significant opportunities within specific application areas. The environmental sector, utilizing satellite imagery for deforestation monitoring, climate change analysis, and pollution detection, is a rapidly growing segment. Similarly, the energy and power sector leverages satellite imagery for pipeline monitoring, renewable energy resource assessment, and infrastructure management. Within image processing types, the demand for advanced data analytics is soaring, with growing adoption of artificial intelligence and machine learning for automated feature extraction and predictive analysis. While regulatory hurdles and the high initial investment cost of satellite technologies pose some challenges, the overall market outlook remains positive, driven by technological advancements, increasing data accessibility, and rising demand for location-based intelligence. Competition is intensifying amongst established players and new entrants, leading to innovation and affordability in the market.

The first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis.

The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995.

The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into recovery capsules. The capsules or buckets were de-orbited and retrieved by aircraft while the capsules parachuted to earth. The exposed film was developed and the images were analyzed for a range of military applications.

The intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data.

A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft.

The original film and technical mission-related documents are maintained by the National Archives and Records Administration (NARA). Duplicate film sources held in the USGS EROS Center archive are used to produce digital copies of the imagery.

Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.