Declassified satellite images provide an important worldwide record of land-surface change. With the success of the first release of classified satellite photography in 1995, images from U.S. military intelligence satellites KH-7 and KH-9 were declassified in accordance with Executive Order 12951 in 2002. The data were originally used for cartographic information and reconnaissance for U.S. intelligence agencies. Since the images could be of historical value for global change research and were no longer critical to national security, the collection was made available to the public.

Keyhole (KH) satellite systems KH-7 and KH-9 acquired photographs of the Earth’s surface with a telescopic camera system and transported the exposed film through the use of 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 KH-7 surveillance system was a high resolution imaging system that was operational from July 1963 to June 1967. Approximately 18,000 black-and-white images and 230 color images are available from the 38 missions flown during this program. Key features for this program were larger area of coverage and improved ground resolution. The cameras acquired imagery in continuous lengthwise sweeps of the terrain. KH-7 images are 9 inches wide, vary in length from 4 inches to 500 feet long, and have a resolution of 2 to 4 feet.

The KH-9 mapping program was operational from March 1973 to October 1980 and was designed to support mapping requirements and exact positioning of geographical points for the military. This was accomplished by using image overlap for stereo coverage and by using a camera system with a reseau grid to correct image distortion. The KH-9 framing cameras produced 9 x 18 inch imagery at a resolution of 20-30 feet. Approximately 29,000 mapping images were acquired from 12 missions.

The original film sources 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.

Cloud-free Landsat satellite imagery mosaics of the islands of the main 8 Hawaiian Islands (Hawaii, Maui, Kahoolawe, Lanai, Molokai, Oahu, Kauai and Niihau). Landsat 7 ETM (enhanced thematic mapper) is a polar orbiting 8 band multispectral satellite-borne sensor. The ETM+ instrument provides image data from eight spectral bands. The spatial resolution is 30 meters for the visible and near-infrared (bands 1-5 and 7). Resolution for the panchromatic (band 8) is 15 meters, and the thermal infrared (band 6) is 60 meters. The approximate scene size is 170 x 183 kilometers (106 x 115 miles). A Nadir-looking system, the sensor has provided continuous coverage since July 1999, with a 16-day repeat cycle. The Level 1G product is radiometrically and geometrically corrected (systematic) to the user-specified parameters including output map projection, image orientation, pixel grid-cell size, and resampling kernel. The correctional gorithms model the spacecraft and sensor using data generated by onboard computers during imaging. Sensor, focal plane, and detector alignment information provided by the Image Assessment System (IAS) in the Calibration Parameter File (CPF) is also used to improve the overall geometric fidelity. The resulting product is free from distortions related to the sensor (e.g., jitter, view angle effect), satellite (e.g., attitude deviations from nominal), and Earth (e.g., rotation, curvature). Residual error in the systematic L1G product is less than 250 meters (1 sigma) inflat areas at sea level. The systematic L1G correction process does not employ ground control or relief models to attain absolute geodetic accuracy.

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.

QuickBird high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.

In particular, QuickBird offers archive panchromatic products up to 0.60 m GSD resolution and 4-Bands Multispectral products up to 2.4 m GSD resolution.

Band Combination Data Processing Level Resolution Panchromatic and 4-bands Standard(2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map-Ready (Ortho) 1:12,000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm

4-Bands being an option from:

4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) Natural Colour / Coloured Infrared (3-Band pan-sharpened) Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique intelligently increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details.

Snapshot img

Satellite-Based Earth Observation Market Size 2025-2029

The satellite-based earth observation market size is forecast to increase by USD 9.66 billion, at a CAGR of 12% between 2024 and 2029. The market is experiencing significant growth, driven primarily by the increasing demand for advanced environment monitoring.

Major Market Trends & Insights

North America dominated the market and accounted for a 43% share in 2023.
The market is expected to grow significantly in APAC region as well over the forecast period.
Based on Application, the defense segment led the market and was valued at USD 4.28 billion of the global revenue in 2023.
Based on Type, the value-added Services (VAS) segment accounted for the largest market revenue share in 2023.

Market Size & Forecast

2024 Market Size: USD 12.64 Billion
Future Opportunities: USD 9.65 Billion
CAGR (2024-2029): 12%
North America: Largest market in 2023

The market continues to evolve, driven by advancements in remote sensing technology and the increasing demand for accurate and timely geospatial data across various sectors. From infrastructure development to environmental monitoring, earth observation data plays a crucial role in sectors such as urban planning, resource management, and disaster management. Satellite imagery, with its high temporal and spectral resolution, enables effective monitoring of land cover changes, weather patterns, and natural disasters. Geospatial software and remote sensing tools facilitate data fusion, allowing for more comprehensive analysis and data integration. Government agencies, military applications, commercial businesses, and research institutions all rely on earth observation data for intelligence gathering, border surveillance, and precision agriculture.

Big data and data services have emerged as key players in the market, offering data subscriptions, data analytics platforms, and data visualization tools. Machine learning and artificial intelligence are transforming the industry, enabling advanced image interpretation and data processing capabilities. Hyperspectral imagery and data licensing are also gaining traction, providing more detailed information on land use and carbon monitoring. Emergency response and climate change are among the latest applications of earth observation data, highlighting the market's continuous dynamism. The future of the market lies in cloud computing, spatial analysis, and data integration, as the industry continues to unfold and evolve.

What will be the Size of the Satellite-Based Earth Observation Market during the forecast period?

Request Free Sample

This need is fueled by various factors, including climate change mitigation, natural disaster management, and agricultural productivity enhancement. Another key trend in the market is the rising preference for small satellites due to their cost-effectiveness and ease of deployment. However, the market faces challenges as well. Competition from alternate technologies, such as drones and airborne sensors, poses a significant threat to the market's growth. The weather segment of application is the second largest segment and was valued at USD 2.73 billion in 2023.

Additionally, the high cost of satellite manufacturing and launching continues to be a significant barrier to entry for new players. To capitalize on the market opportunities and navigate these challenges effectively, companies must focus on innovation, cost reduction, and strategic partnerships. By investing in advanced technologies and collaborating with industry leaders, they can differentiate themselves and stay competitive in the evolving market landscape.

How is this Satellite-Based Earth Observation Industry segmented?

The satellite-based earth observation industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

Application

  Defense
  Weather
  Location-Based Services (LBS)
  Energy
  Others


Type

  Value-Added Services (VAS)
  Data


Technology

  Synthetic aperture radar (SAR)
  Optical


End-User

  Government
  Commercial
  Academic/Research


Geography

  North America

    US
    Canada


  Europe

    Germany
    Russia
    UK


  Middle East and Africa

    UAE


  APAC

    China
    Japan
    South Korea


  South America

    Brazil


  Rest of World (ROW)

By Application Insights

The defense segment is estimated to witness significant growth during the forecast period. The segment was valued at USD 4.28 billion in 2023. It continued to the largest segment at a CAGR of 9.06%.

The market encompasses various applications, including crop monitoring, weather forecasting, disaster management, resource management, and defense and security. Crop monitoring leverages multispect

High resolution orthorectified images combine the image characteristics of an aerial photograph with the geometric qualities of a map. An orthoimage is a uniform-scale image where corrections have been made for feature displacement such as building tilt and for scale variations caused by terrain relief, sensor geometry, and camera tilt. A mathematical equation based on ground control points, sensor calibration information, and a digital elevation model is applied to each pixel to rectify the image to obtain the geometric qualities of a map.

A digital orthoimage may be created from several photographs mosaicked to form the final image. The source imagery may be black-and-white, natural color, or color infrared with a pixel resolution of 1-meter or finer. With orthoimagery, the resolution refers to the distance on the ground represented by each pixel.

WorldView-1 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-1 offers archive and tasking panchromatic products up to 0.50 m GSD resolution. Band Combination Data Processing Level Resolution Panchromatic Standard(2A)/View Ready STANDARD (OR2A) 50 cm, 30 cm HD View Ready Stereo 50 cm Map-Ready (Ortho) 1:12.000 Orthorectified 50 cm, 30 cm HD Native 50 cm resolution products are processed with MAXAR HD Technology to generate the 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

On February 24, 1995, President Clinton signed an Executive Order,
directing the declassification of intelligence imagery acquired by the
first generation of United States photo-reconnaissance satellites, including
the systems code-named CORONA, ARGON, and LANYARD. More than 860,000 images of the Earth's surface, collected between 1960 and 1972, were declassified with the issuance of this Executive Order.

Image collection was driven, in part, by the need to confirm purported developments in then-Soviet strategic missile capabilities. The images also were used to produce maps and charts for the Department of Defense and for other Federal Government mapping programs. In addition to the images, documents and reports (collateral information) are available, pertaining to frame ephemeris data, orbital ephemeris data, and mission performance. Document availability varies by mission; documentation was not produced for unsuccessful missions.

Since 1972, the joint NASA/ U.S. Geological Survey Landsat series of Earth Observation satellites have continuously acquired images of the Earth’s land surface, providing uninterrupted data to help land managers and policymakers make informed decisions about natural resources and the environment.

Landsat is a part of the USGS National Land Imaging (NLI) Program. To support analysis of the Landsat long-term data record that began in 1972, the USGS. Landsat data archive was reorganized into a formal tiered data collection structure. This structure ensures all Landsat Level 1 products provide a consistent archive of known data quality to support time-series analysis and data “stacking”, while controlling continuous improvement of the archive, and access to all data as they are acquired. Collection 1 Level 1 processing began in August 2016 and continued until all archived data was processed, completing May 2018. Newly-acquired Landsat 8 and Landsat 7 data continue to be processed into Collection 1 shortly after data is downlinked to USGS EROS.

Acknowledgement or credit of the USGS as data source should be provided by including a line of text citation such as the example shown below. (Product, Image, Photograph, or Dataset Name) courtesy of the U.S. Geological Survey Example: Landsat-8 image courtesy of the U.S. Geological Survey

Snapshot img

Commercial Satellite Imaging Market Size 2024-2028

The commercial satellite imaging market size is forecast to increase by USD 2.33 billion at a CAGR of 7.66% between 2023 and 2028.

The market is experiencing significant growth due to advancements in satellite technology and the increasing demand for high-resolution imagery. Additionally, the cost of launching satellites is decreasing, making it more accessible to businesses. However, challenges remain, including regulatory issues and data security and privacy concerns. The key players address these challenges through advanced image-processing techniques, AI-powered analytics, and partnerships with governments and private organizations. Artificial intelligence plays a pivotal role in enhancing image clarity, improving data interpretation, and automating the analysis process. This market analysis report delves into these trends and challenges, providing insights into the future growth prospects of the commercial satellite imaging industry.

What will be the Size of the Commercial Satellite Imaging Market During the Forecast Period?

Request Free Sample

The market encompasses the use of imagery obtained from optically equipped satellites for various applications, including meteorology, oceanography, fisheries, agriculture, biodiversity protection, forestry, geology, cartography, regional planning, intelligence, warfare, aeronautical imaging, terrestrial imaging, and smart cities. These images play a crucial role in providing valuable insights and data for numerous industries and sectors. Meteorology and oceanography applications utilize satellite imagery to monitor weather patterns, ocean currents, and climate trends. This data is essential for forecasting severe weather events, predicting storms, and understanding climate change. In the field of fisheries, satellite imagery is used to monitor fish populations, track migration patterns, and ensure sustainable fishing practices.
Agriculture is another significant sector that benefits from satellite imagery. Farmers and agricultural organizations use this data to optimize crop yields, monitor crop health, and manage irrigation systems. Biodiversity protection and forestry applications rely on satellite imagery for monitoring deforestation, identifying endangered species, and managing forest resources. Geology and cartography applications use satellite imagery for mapping and analyzing geological features, while regional planning and intelligence applications utilize this data for infrastructure development, urban planning, and security purposes. In the field of warfare, satellite imagery is used for reconnaissance, target identification, and battlefield analysis. Aeronautical and terrestrial imaging applications use satellite imagery for mapping and surveying terrain, monitoring infrastructure, and ensuring safety in aviation and transportation.

How is this Commercial Satellite Imaging Industry segmented and which is the largest segment?

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

End-user

  Government
  Military and defense
  Transportation
  Agriculture
  Others


Technology

  Optical
  Radar


Geography

  North America

    US


  APAC

    China
    Japan


  Europe

    France


  South America



  Middle East and Africa

By End-user Insights

The government segment is estimated to witness significant growth during the forecast period.

Satellite imaging, specifically through platforms such as Google Earth, has become a crucial tool for various sectors, particularly the government. This technology aids in civil protection and humanitarian efforts by enabling the analysis and management of disaster causation factors. By assessing risks and planning prevention measures, satellite imagery facilitates more effective disaster response and relief efforts. Furthermore, high-resolution satellite imagery contributes to the restoration and enhancement of facilities, livelihoods, and living conditions in affected communities. In addition, it plays a vital role in protecting natural resources and the environment, including wildlife habitats. High-resolution satellite imagery is also indispensable for engineering and urban planning projects. Location-Based Services (LBS) integrated with satellite imagery can further enhance the utility of this technology in various sectors, including defense and energy.

Get a glance at the Commercial Satellite Imaging Industry report of share of various segments Request Free Sample

The government segment was valued at USD 1.37 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

APAC is estimated to contr

The Pléiades ESA archive is a dataset of Pléiades-1A and 1B products that ESA collected over the years. The dataset regularly grows as ESA collects new Pléiades products. Pléiades Primary and Ortho products can be available in the following modes: Panchromatic image at 0.5 m resolution Pansharpened colour image at 0.5 m resolution Multispectral image in 4 spectral bands at 2 m resolution Bundle (0.5 m panchromatic image + 2 m multispectral image) Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

WorldView-3 high resolution optical products are available as part of the Maxar Standard Satellite Imagery products from the QuickBird, WorldView-1/-2/-3/-4, and GeoEye-1 satellites. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section. In particular, WorldView-3 offers archive and tasking panchromatic products up to 0.31m GSD resolution, 4-Bands/8-Bands products up to 1.24 m GSD resolution, and SWIR products up to 3.70 m GSD resolution. Band Combination Data Processing Level Resolution High Res Optical: Panchromatic and 4-bands Standard(2A)/View Ready Standard (OR2A) 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 15 cm HD, 30 cm HD, 30 cm, 40 cm, 50/60 cm High Res Optical: 8-bands Standard(2A)/View Ready Standard (OR2A) 30 cm, 40 cm, 50/60 cm View Ready Stereo 30 cm, 40 cm, 50/60 cm Map Ready (Ortho) 1:12.000 Orthorectified 30 cm, 40 cm, 50/60 cm High Res Optical: SWIR Standard(2A)/View Ready Standard (OR2A) 3.7 m or 7.5 m (depending on the collection date) Map Ready (Ortho) 1:12.000 Orthorectified 4-Bands being an optional from: 4-Band Multispectral (BLUE, GREEN, RED, NIR1) 4-Band Pan-sharpened (BLUE, GREEN, RED, NIR1) 4-Band Bundle (PAN, BLUE, GREEN, RED, NIR1) 3-Bands Natural Colour (pan-sharpened BLUE, GREEN, RED) 3-Band Colored Infrared (pan-sharpened GREEN, RED, NIR1) 8-Bands being an optional from: 8-Band Multispectral (COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) 8-Band Bundle (PAN, COASTAL, BLUE, GREEN, YELLOW, RED, RED EDGE, NIR1, NIR2) Native 30 cm and 50/60 cm resolution products are processed with MAXAR HD Technology to generate respectively the 15 cm HD and 30 cm HD products: the initial special resolution (GSD) is unchanged but the HD technique increases the number of pixels and improves the visual clarity achieving aesthetically refined imagery with precise edges and well reconstructed details. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

This dataset contains Sentinel 2 and Landsat 8 cloud free composite satellite images of the Coral Sea reef areas and some parts of the Great Barrier Reef. It also contains raw depth contours derived from the satellite imagery. This dataset was developed as the base information for mapping the boundaries of reefs and coral cays in the Coral Sea. It is likely that the satellite imagery is useful for numerous other applications. The full source code is available and can be used to apply these techniques to other locations.

This dataset contains two sets of raw satellite derived bathymetry polygons for 5 m, 10 m and 20 m depths based on both the Landsat 8 and Sentinel 2 imagery. These are intended to be post-processed using clipping and manual clean up to provide an estimate of the top structure of reefs. This dataset also contains select scenes on the Great Barrier Reef and Shark bay in Western Australia that were used to calibrate the depth contours. Areas in the GBR were compared with the GA GBR30 2020 (Beaman, 2017) bathymetry dataset and the imagery in Shark bay was used to tune and verify the Satellite Derived Bathymetry algorithm in the handling of dark substrates such as by seagrass meadows. This dataset also contains a couple of small Sentinel 3 images that were used to check the presence of reefs in the Coral Sea outside the bounds of the Sentinel 2 and Landsat 8 imagery.

The Sentinel 2 and Landsat 8 imagery was prepared using the Google Earth Engine, followed by post processing in Python and GDAL. The processing code is available on GitHub (https://github.com/eatlas/CS_AIMS_Coral-Sea-Features_Img).

This collection contains composite imagery for Sentinel 2 tiles (59 in Coral Sea, 8 in GBR) and Landsat 8 tiles (12 in Coral Sea, 4 in GBR and 1 in WA). For each Sentinel tile there are 3 different colour and contrast enhancement styles intended to highlight different features. These include: - TrueColour - Bands: B2 (blue), B3 (green), B4 (red): True colour imagery. This is useful to identifying shallow features are and in mapping the vegetation on cays. - DeepFalse - Bands: B1 (ultraviolet), B2 (blue), B3 (green): False colour image that shows deep marine features to 50 - 60 m depth. This imagery exploits the clear waters of the Coral Sea to allow the ultraviolet band to provide a much deeper view of coral reefs than is typically achievable with true colour imagery. This imagery has a high level of contrast enhancement applied to the imagery and so it appears more noisy (in particular showing artefact from clouds) than the TrueColour styling. - Shallow - Bands: B5 (red edge), B8 (Near Infrared) , B11 (Short Wave infrared): This false colour imagery focuses on identifying very shallow and dry regions in the imagery. It exploits the property that the longer wavelength bands progressively penetrate the water less. B5 penetrates the water approximately 3 - 5 m, B8 approximately 0.5 m and B11 < 0.1 m. Features less than a couple of metres appear dark blue, dry areas are white. This imagery is intended to help identify coral cay boundaries.

For Landsat 8 imagery only the TrueColour and DeepFalse stylings were rendered.

All Sentinel 2 and Landsat 8 imagery has Satellite Derived Bathymetry (SDB) depth contours. - Depth5m - This corresponds to an estimate of the area above 5 m depth (Mean Sea Level). - Depth10m - This corresponds to an estimate of the area above 10 m depth (Mean Sea Level). - Depth20m - This corresponds to an estimate of the area above 20 m depth (Mean Sea Level).

For most Sentinel and some Landsat tiles there are two versions of the DeepFalse imagery based on different collections (dates). The R1 imagery are composites made up from the best available imagery while the R2 imagery uses the next best set of imagery. This splitting of the imagery is to allow two composites to be created from the pool of available imagery. This allows any mapped features to be checked against two images. Typically the R2 imagery will have more artefacts from clouds. In one Sentinel 2 tile a third image was created to help with mapping the reef platform boundary.

The satellite imagery was processed in tiles (approximately 100 x 100 km for Sentinel 2 and 200 x 200 km for Landsat 8) to keep each final image small enough to manage. These tiles were not merged into a single mosaic as it allowed better individual image contrast enhancement when mapping deep features. The dataset only covers the portion of the Coral Sea where there are shallow coral reefs and where their might have been potential new reef platforms indicated by existing bathymetry datasets and the AHO Marine Charts. The extent of the imagery was limited by those available through the Google Earth Engine.

Methods:

The Sentinel 2 imagery was created using the Google Earth Engine. The core algorithm was: 1. For each Sentinel 2 tile, images from 2015 – 2021 were reviewed manually after first filtering to remove cloudy scenes. The allowable cloud cover was adjusted so that at least the 50 least cloud free images were reviewed. The typical cloud cover threshold was 1%. Where very few images were available the cloud cover filter threshold was raised to 100% and all images were reviewed. The Google Earth Engine image IDs of the best images were recorded, along with notes to help sort the images based on those with the clearest water, lowest waves, lowest cloud, and lowest sun glint. Images where there were no or few clouds over the known coral reefs were preferred. No consideration of tides was used in the image selection process. The collection of usable images were grouped into two sets that would be combined together into composite images. The best were added to the R1 composite, and the next best images into the R2 composite. Consideration was made as to whether each image would improve the resultant composite or make it worse. Adding clear images to the collection reduces the visual noise in the image allowing deeper features to be observed. Adding images with clouds introduces small artefacts to the images, which are magnified due to the high contrast stretching applied to the imagery. Where there were few images all available imagery was typically used. 2. Sunglint was removed from the imagery using estimates of the sunglint using two of the infrared bands (described in detail in the section on Sun glint removal and atmospheric correction). 3. A composite image was created from the best images by taking the statistical median of the stack of images selected in the previous stage, after masking out clouds and their shadows (described in detail later). 4. The brightness of the composite image was normalised so that all tiles would have a similar average brightness for deep water areas. This correction was applied to allow more consistent contrast enhancement. Note: this brightness adjustment was applied as a single offset across all pixels in the tile and so this does not correct for finer spatial brightness variations. 5. The contrast of the images was enhanced to create a series of products for different uses. The TrueColour colour image retained the full range of tones visible, so that bright sand cays still retain detail. The DeepFalse style was optimised to see features at depth and the Shallow style provides access to far red and infrared bands for assessing shallow features, such as cays and island. 6. The various contrast enhanced composite images were exported from Google Earth Engine and optimised using Python and GDAL. This optimisation added internal tiling and overviews to the imagery. The depth polygons from each tile were merged into shapefiles covering the whole for each depth.

Cloud Masking

Prior to combining the best images each image was processed to mask out clouds and their shadows.

The cloud masking uses the COPERNICUS/S2_CLOUD_PROBABILITY dataset developed by SentinelHub (Google, n.d.; Zupanc, 2017). The mask includes the cloud areas, plus a mask to remove cloud shadows. The cloud shadows were estimated by projecting the cloud mask in the direction opposite the angle to the sun. The shadow distance was estimated in two parts.

A low cloud mask was created based on the assumption that small clouds have a small shadow distance. These were detected using a 40% cloud probability threshold. These were projected over 400 m, followed by a 150 m buffer to expand the final mask.

A high cloud mask was created to cover longer shadows created by taller, larger clouds. These clouds were detected based on an 80% cloud probability threshold, followed by an erosion and dilation of 300 m to remove small clouds. These were then projected over a 1.5 km distance followed by a 300 m buffer.

The buffering was applied as the cloud masking would often miss significant portions of the edges of clouds and their shadows. The buffering allowed a higher percentage of the cloud to be excluded, whilst retaining as much of the original imagery as possible.

The parameters for the cloud masking (probability threshold, projection distance and buffer radius) were determined through trial and error on a small number of scenes. The algorithm used is significantly better than the default Sentinel 2 cloud masking and slightly better than the COPERNICUS/S2_CLOUD_PROBABILITY cloud mask because it masks out shadows, however there is potentially significant improvements that could be made to the method in the future.

Erosion, dilation and buffer operations were performed at a lower image resolution than the native satellite image resolution to improve the computational speed. The resolution of these operations were adjusted so that they were performed with approximately a 4 pixel resolution during these operations. This made the cloud mask significantly more spatially coarse than the 10 m Sentinel imagery. This resolution was chosen as a trade-off between the coarseness of the mask verse the processing time for these operations.

The Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) are onboard the Landsat 8 satellite, have acquired images of the Earth since February 2013. The sensors collect images of the Earth with a 16-day repeat cycle, referenced to the Worldwide Reference System-2. The approximate scene size is 170 km north-south by 183 km east-west (106 mi by 114 mi). Landsat 8 image data files consist of 11 spectral bands with a spatial resolution of 30 meters for bands 1-7 and bands 9-11; 15-meters for the panchromatic band 8. Delivered Landsat 8 Level-1 data typically include both OLI and TIRS data files; however, there may be OLI-only and/or TIRS-only scenes in the USGS archive. A Quality Assurance (QA.tif) band is also included. This file provides bit information regarding conditions that may affect the accuracy and usability of a given pixel – clouds, water or snow, for example.

Snapshot img

Satellite Market Size 2024-2028

The satellite market size is forecast to increase by USD 14.53 billion at a CAGR of 3.31% between 2023 and 2028. The market is experiencing significant growth, driven by the increasing demand for DTH services and satellite-based telemetry applications. The number of DTH subscribers continues to rise, fueled by the availability of affordable satellite receivers. High-throughput satellites (HTS) are gaining popularity due to their ability to provide faster data transmission rates, making them ideal for IoT devices and other bandwidth-intensive applications. The high cost of satellite hardware and components remains a challenge, but innovations such as 3D-printed satellite parts are helping to reduce costs. OneWeb, among others, is leading the charge in this area, with plans to launch a constellation of low Earth orbit satellites to provide global connectivity. Overall, the market is poised for continued growth, driven by advancements in technology and increasing demand for reliable, high-speed connectivity.

Request Free Sample

The market is witnessing significant growth due to the increasing demand for artificial satellites in various applications such as communication, earth observation, navigation, scientific research, and space exploration. These satellites are launched into different orbits, including Low Earth Orbit (LEO), Medium Earth Orbit (MEO), and Geostationary Earth Orbit (GEO), depending on their function. Communication is a major application segment in the market, driving the demand for satellite services, including satellite-based internet services and voice communications. Earth observation is another significant application area, with satellite imaging playing a crucial role in areas like agriculture, forestry, and disaster management. High-throughput satellites and mega constellations, such as Starlink satellites, are emerging trends in the market, aiming to bridge the digital divide by providing internet access to remote and underserved areas.

Furthermore, space exploration missions and satellite-based warfare are other key applications driving the growth of the market. Small satellites are gaining popularity due to their cost-effectiveness and ease of deployment. Satellite data transmission is another crucial aspect of the market, with space data being used for various applications, including television and video distribution, digital television, and internet access. The market is witnessing significant investments from satellite enterprises, communication service providers, and space-focused firms.

Market Segmentation

The market 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.

End-user

  Civil
  Defense


Type

  Large
  medium satellite
  Small satellite


Geography

  North America

    US


  Europe

    France


  APAC

    China
    Japan


  Middle East and Africa



  South America

By End-user Insights

The civil segment is estimated to witness significant growth during the forecast period. Satellites play a crucial role in driving economic growth for various sectors, including commercial enterprises, government agencies, and the telecommunications and space industries. These technological marvels come in different forms, determined by their frequency, orbit, and mission objectives. With the escalating demand for Internet services and the growing number of mobile users, satellites have found extensive applications in civilian domains, such as voice communications, satellite-based internet services, and meteorology. Manufacturers of GPS receivers cater to the needs of civilians, particularly scientists and surveyors, who rely on these devices for precise time and position measurements for research and surveying purposes. Additionally, satellites contribute significantly to civil aviation, ensuring optimal navigational services and flight information region (FIR) coverage.

Get a glance at the market share of various segments Request Free Sample

The civil segment was valued at USD 45.95 billion in 2018 and showed a gradual increase during the forecast period.

Regional Insights

North America is estimated to contribute 38% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

For more insights on the market share of various regions Request Free Sample

Satellite services play a crucial role in various applications, including satellite imaging, internet connectivity, and voice communications. North America is a significant market for these services, with the US, Canada, and Mexico being the primary consumers. The US, in particular, has a

AID is a new large-scale aerial image dataset, by collecting sample images from Google Earth imagery. Note that although the Google Earth images are post-processed using RGB renderings from the original optical aerial images, it has proven that there is no significant difference between the Google Earth images with the real optical aerial images even in the pixel-level land use/cover mapping. Thus, the Google Earth images can also be used as aerial images for evaluating scene classification algorithms.

The new dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes.

The images in AID are actually multi-source, as Google Earth images are from different remote imaging sensors. This brings more challenges for scene classification than the single source images like UC-Merced dataset. Moreover, all the sample images per each class in AID are carefully chosen from different countries and regions around the world, mainly in China, the United States, England, France, Italy, Japan, Germany, etc., and they are extracted at different time and seasons under different imaging conditions, which increases the intra-class diversities of the data.

What is SEPAL?

SEPAL (https://sepal.io/) is a free and open source cloud computing platform for geo-spatial data access and processing. It empowers users to quickly process large amounts of data on their computer or mobile device. Users can create custom analysis ready data using freely available satellite imagery, generate and improve land use maps, analyze time series, run change detection and perform accuracy assessment and area estimation, among many other functionalities in the platform. Data can be created and analyzed for any place on Earth using SEPAL.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/63a3efa0-08ab-4ad6-9d4a-96af7b6a99ec/download/cambodia_mosaic_2020.png" alt="alt text" title="Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia">

Figure 1: Best pixel mosaic of Landsat 8 data for 2020 over Cambodia

SEPAL reaches over 5000 users in 180 countries for the creation of custom data products from freely available satellite data. SEPAL was developed as a part of the Open Foris suite, a set of free and open source software platforms and tools that facilitate flexible and efficient data collection, analysis and reporting. SEPAL combines and integrates modern geospatial data infrastructures and supercomputing power available through Google Earth Engine and Amazon Web Services with powerful open-source data processing software, such as R, ORFEO, GDAL, Python and Jupiter Notebooks. Users can easily access the archive of satellite imagery from NASA, the European Space Agency (ESA) as well as high spatial and temporal resolution data from Planet Labs and turn such images into data that can be used for reporting and better decision making.

National Forest Monitoring Systems in many countries have been strengthened by SEPAL, which provides technical government staff with computing resources and cutting edge technology to accurately map and monitor their forests. The platform was originally developed for monitoring forest carbon stock and stock changes for reducing emissions from deforestation and forest degradation (REDD+). The application of the tools on the platform now reach far beyond forest monitoring by providing different stakeholders access to cloud based image processing tools, remote sensing and machine learning for any application. Presently, users work on SEPAL for various applications related to land monitoring, land cover/use, land productivity, ecological zoning, ecosystem restoration monitoring, forest monitoring, near real time alerts for forest disturbances and fire, flood mapping, mapping impact of disasters, peatland rewetting status, and many others.

The Hand-in-Hand initiative enables countries that generate data through SEPAL to disseminate their data widely through the platform and to combine their data with the numerous other datasets available through Hand-in-Hand.

https://data.apps.fao.org/catalog/dataset/9c4d7c45-7620-44c4-b653-fbe13eb34b65/resource/868e59da-47b9-4736-93a9-f8d83f5731aa/download/probability_classification_over_zambia.png" alt="alt text" title="Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia">

Figure 2: Image classification module for land monitoring and mapping. Probability classification over Zambia

The size of the Geospatial Imagery Analytics Market was valued at USD 12.97 billion in 2023 and is projected to reach USD 91.05 billion by 2032, with an expected CAGR of 32.1% during the forecast period. Geospatial Imagery Analytics increases market sizes when you talk to the industries - people discover more how right would be to use images--ads make the presentation of information about the customers. The fact would be that geospatial sources present geospatial imagery, including data from satellites and airborne vehicles, for advanced analytics, and these in particular extract information from niches using city planning, environmental monitoring and defense, and agriculture and disaster management. This brings about the tracking of changes occurring in landscapes, risk assessments as well as efficient operations. Worth mentioning among the driving forces mentioned would be the increasing geographic data demands articulated by industries, the availability of high-resolution satellite images, and the growth of Artificial Intelligence and machine learning and its relationship to big data resulting in easier processing and analysis of geographic data. Besides, drone and unmanned aerial vehicles (UAVs) are increasingly preferred for realtime-capturing audiovisuality and growth in market size. Recent developments include: In September 2023, SkyFi, a prominent provider of Earth observation data, is pleased to introduce a significant advancement in data accessibility through the simultaneous release of two groundbreaking products: open data and SkyFi Insights. SkyFi establishes itself as the global geospatial center with the introduction of open data and SkyFi Insights. Users will no longer face technological difficulties or jargon-laden hurdles when accessing crucial Earth observation information. SkyFi is a user-friendly web and mobile application that enables users to easily access and utilize geospatial knowledge. SkyFi has incorporated Sentinel 2 data into its platform, enabling the provision of free and accessible geospatial data in the field of satellite imaging. This integration promotes cooperation, creativity, and accessibility. SkyFi is the pioneering company that provides exclusive access to unfiltered satellite data via a mobile application, marking a significant milestone in history. By providing daily updated global imagery, users can effortlessly access a vast amount of valuable and free information, thereby removing the conventional obstacles to entrance. SkyFi plans to incorporate other satellite sources in order to further enhance its open data offerings., In December 2023, the UAE Space Agency initiated the operational phase of the Geo-Spatial Analytics Platform in collaboration with Bayanat. The platform will provide three essential services: enabling access to satellite imagery from international space agencies and top private companies, offering analytics reports generated by Artificial Intelligence (AI)-based algorithms, and hosting a market-place for space applications and AI Space-Based Models. These services will facilitate the attraction of prominent innovators, expedite the development process of space products, and contribute to the expansion of the UAE's economy. The implementation phase of the Geo-Spatial Analytics Platform is a crucial strategic move to enhance the UAE's standing as a prominent participant in the international space sector and promote sustainability. The platform will undoubtedly enhance our comprehension of Earth and climate alterations, as well as foster the advancement of scientific and technological capacities to mitigate these modifications.. Key drivers for this market are: Increasing Use of Location-Based Services, Huge Requirement of Geospatial Analytics for Security and Surveillance Applications; Development in Big Data Technology. Potential restraints include: Stringent Government Rules and Regulations for Using Geospatial Information. Notable trends are: integration with AI and Machine Learning Real-Time Analytics.

The Landsat TM Orthorectified Mosaics data collection is derived from a global set of high-quality, relatively cloud-free orthorectified TM imagery from Landsats 4-5. This dataset was selected and generated through NASA's Commercial Remote Sensing Program, as part of a cooperative effort between NASA and the commercial remote sensing community to provide users with access to quality-screened, high-resolution satellite images with global coverage over the Earth's land masses. The data collection was compiled via NASA contract with Earth Satellite Corporation (Rockville, MD) in association with NASA's Scientific Data Purchase program.

The Landsat Orthorectified TM Mosaics data collection is derived from approximately 7,461 TM (Landsat 4-5) images, which were selected to provide a full set of global coverage (circa 1990). All selected images were either cloud-free or contained minimal cloud cover. In addition, only images with a high quality ranking in regards to the possible presence of errors such as missing scans or saturated bands were selected.

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.