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.

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.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Description

4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

The global market for satellite remote sensing software is experiencing robust growth, driven by increasing demand across various sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are providing higher-resolution imagery and enhanced data analytics capabilities, leading to improved accuracy and efficiency in applications like precision agriculture, urban planning, and environmental monitoring. Secondly, the decreasing cost of satellite data and the rising accessibility of cloud-based processing platforms are democratizing access to this technology for a wider range of users and organizations. Furthermore, the growing need for real-time data and predictive analytics in various industries is significantly boosting the adoption of sophisticated satellite remote sensing software. Competition among established players like GAMMA Remote Sensing AG, ESRI, and Trimble, alongside emerging innovative companies, is fostering a dynamic market landscape with continuous improvements in software functionality and user experience. However, certain restraints are also influencing the market's trajectory. The complexity of some software packages and the requirement for specialized skills to operate them can pose a barrier to entry for some users. Data security and privacy concerns also need to be addressed to ensure the responsible use of sensitive geospatial information. Despite these challenges, the long-term outlook for the satellite remote sensing software market remains positive, with continued growth expected across diverse geographical regions, particularly in North America and Europe where adoption rates are currently higher. Segmentation within the market reflects specialization in particular applications (e.g., agriculture, defense, environmental management) and software types (e.g., image processing, GIS integration). Future growth will be heavily influenced by the ongoing integration of artificial intelligence and machine learning into these software packages, enabling automated analysis and unlocking even greater insights from satellite imagery.

This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats.

Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format).

Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks.

The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data. Contact NSIDC User Services at nsidc@nsidc.org to order the data, and include an NSF OPP award number in the email.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)

Description

3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571
* Labels have been reclassified from 4 classes to 2 classes.
* Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
* These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images
• swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels
• resized_nir.zip, NIR images resized to 512x512x1 pixels
• resized_swir.zip, SWIR images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

The remote sensing software market is experiencing robust growth, driven by increasing demand for geospatial data across various sectors. The market's expansion is fueled by advancements in sensor technology, satellite imagery availability, and the rising adoption of cloud-based solutions for data processing and analysis. Factors like the need for precise land management, environmental monitoring, urban planning, and defense applications are significant contributors to this growth. While precise figures for market size and CAGR are unavailable in the provided information, based on industry reports and trends, a reasonable estimation would place the 2025 market size at approximately $5 billion, experiencing a compound annual growth rate (CAGR) of around 8% during the forecast period (2025-2033). This growth trajectory is expected to continue, driven by the increasing integration of AI and machine learning algorithms within remote sensing software for improved data analysis and automation. The competitive landscape is marked by a mix of established players like PCI Geomatics, Hexagon, and Esri, and emerging technology providers. These companies are constantly innovating to offer advanced functionalities such as 3D modeling, image processing, and data visualization capabilities. However, high initial investment costs for software licenses and specialized hardware can present a barrier to entry for some organizations. Further, data security concerns and the need for specialized expertise in data interpretation can pose some challenges to market growth. Despite these constraints, the long-term prospects of the remote sensing software market remain highly positive, fueled by government initiatives promoting geospatial data accessibility and the ongoing development of more sophisticated and user-friendly software solutions. The increasing availability of affordable high-resolution imagery and the integration of remote sensing data with other data sources promise to further boost market expansion in the coming years.

Map InformationThis nowCOAST updating map service provides maps depicting visible, infrared, and water vapor imagery composited from NOAA/NESDIS GOES-EAST and GOES-WEST. The horizontal resolutions of the IR, visible, and water vapor composite images are approximately 1km, 4km, and 4km, respectively. The visible and IR imagery depict the location of clouds. The water vapor imagery indicates the amount of water vapor contained in the mid to upper levels of the troposphere. The darker grays indicate drier air while the brighter grays/whites indicates more saturated air. The GOES composite imagers are updated in the nowCOAST map service every 30 minutes. For more detailed information about the update schedule, see: http://new.nowcoast.noaa.gov/help/#section=updatescheduleBackground InformationThe GOES map layer displays visible (VIS) and infrared (IR4) cloud, and water vapor (WV) imagery from the NOAA/ National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Satellites (GOES-East and GOES-West). These satellites circle the Earth in a geosynchronous orbit (i.e. orbit the equatorial plane of the Earth at a speed matching the rotation of the Earth). This allows the satellites to hover continuously over one position on the surface. The geosynchronous plane is about 35,800 km (22,300 miles) above the Earth which is high enough to allow the satellites a full-disc view of the Earth. GOES-East is positioned at 75 deg W longitude and the equator. GOES-West is located at 135 deg W and the equator. The two satellites cover an area from 20 deg W to 165 deg E. The images are derived from data from GOES' Imagers. An imager is a multichannel instrument that senses radiant energy and reflected solar energy from the Earth's surface and atmosphere. The VIS, IR4, and WV images are obtained from GOES Imager Channels 1, 4, and 3, respectively. The GOES raster images are obtained from NESDIS servers in geo-referenced Tagged-Image File Format (geoTIFF).Time InformationThis map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfoReferencesNOAA, 2013: Geostationary Operational Environmental Satellites (GOES). (Available at http://www.ospo.noaa.gov/Operations/GOES/index.html)A Basic Introduction to Water Vapor Imagery. (Available at http://cimss.ssec.wisc.edu/goes/misc/wv/wv_intro.html)CIMSS, 1996: Water Vapor Imagery Tutorial (Available at http://cimss.ssec.wisc.edu/goes/misc/wv/)

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

The Nordics satellite imagery services market is projected to grow from $0.22 million in 2025 to $0.96 million by 2033, exhibiting a CAGR of 13.62% during the forecast period. The increasing adoption of satellite imagery for various applications, such as geospatial data acquisition and mapping, natural resource management, and surveillance and security, is driving the market growth. Moreover, the expanding construction and transportation & logistics sectors in the region are further boosting the demand for satellite imagery services. Key trends shaping the Nordics satellite imagery services market include:

Growing adoption of cloud-based platforms and services for satellite imagery processing and analysis: This trend is enabling end-users to access satellite imagery data and services without the need for significant upfront investments in infrastructure. Increasing availability of high-resolution satellite imagery: The launch of new satellites and the development of new image processing technologies are making it possible to obtain high-resolution satellite imagery, which is essential for a variety of applications, such as mapping and land use planning. Emergence of new applications for satellite imagery: Satellite imagery is increasingly being used for a variety of new applications, such as environmental monitoring, disaster management, and precision agriculture. These new applications are creating new opportunities for growth in the Nordics satellite imagery services market. Recent developments include: May 2023 - Business Finland granted EUR 30 million (USD 32.75 million) loan funding for ICEYE's product development project based on innovative new sensor and space technology that will provide real-time and reliable information to support decision-making worldwide. The project aims to create a unique information and software platform, design and develop technology for next-generation satellites, and apply the high-accuracy information from satellites globally for natural catastrophe analysis, modeling, and decision-making., March 2023 - Norway's International Climate and Forest Initiative (NICFI) announced that NICFI's satellite data program is extended until September 2023. Norway's International Climate and Forest Initiative (NICFI) grant free access to high-resolution satellite imagery of the tropics to anyone, anywhere, to monitor tropical deforestation. Through Norway's International Climate & Forests Initiative, users can access the planet's high-resolution, analysis-ready satellite images of the world's tropics to help reduce and combat climate change and reverse the loss of tropical forests.. Key drivers for this market are: Increasing Demand among Various End-user Industries, notablly in Forestry Sector, Adoption of Big Data and Imagery Analytics. Potential restraints include: High Cost of Satellite Imaging Data Acquisition and Processing. Notable trends are: Forestry and Agriculture is Analyzed to Hold Significant Market Share.

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

The Defense Meteorological Satellite Program (DMSP) satellites collect visible and infrared cloud imagery as well as monitoring the atmospheric, oceanographic, hydrologic, cryospheric and near-Earth space environments. The DMSP program maintains a constellation of sun-synchronous, near-polar orbiting satellites. The orbital period is 101 minutes and inclination is 99 degrees. The atmospheric and oceanographic sensors record radiances at visible, infrared and microwave wavelengths. The solar geophysical sensors measure ionospheric plasma fluxes, densities, temperatures and velocities. DMSP visible and infrared imagery of clouds covers a 3,000 km swath, thus each satellite provides global coverage of both day night time conditions each day. The field view of the microwave imagers and sounders is only 1,500 km thus approximately 3 days data are required for one instrument to provide global coverage at equatorial latitudes. The solar geophysical instruments make in-situ measurements of ionospheric parameters, some of which vary very rapidly. The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) receive the complete DMSP data stream from the Air Force Weather Agency (AFWA), Offutt Air Force Base, Omaha, Nebraska. Data are currently transmitted in near realtime from AFWA directly to the archive via a designated T1 line. Archive processing prepares orbital data sets of calibrated, quality assessed data organized as a time-series, restores data lost during transmission,and accurately computes satellite positions. NCEI maintains an archive of all data recorded on DMSP satellites as relayed to The NOAA National Centers for Environmental Information (formerly National Geophysical Data Center) by the Air Force Weather Agency. Data from March 1992 to March 1994, are considered to be experimental. After March 1994, the system was fully operational. NCEI archives contain data that are post process reconstructed, positioned and geolocated using the same software.

The size of the India Satellite Imagery Services market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 17.43% during the forecast period.The satellite image services primarily include acquisition, processing, analysis, and interpretation to extract useful information. This high-resolution information obtained and captured from Earth-orbiting satellites indicates aspects pertaining to land use and development in urban areas, agriculture, natural resources, and climate change.Indian satellite image services are achieving an exponential growth rate as they meet the increasing demand of various sectors. These sat data are increasingly being used by governments for urban planning, disaster management, and border surveillance. Agriculture uses satellite data to monitor crop growth, estimate yields, and carry out precision farming, while resource exploration and environmental impact assessments are common applications of satellite imagery in the mining and energy sectors. Telecommunications and the GIS industries depend on satellite imagery to plan networks and map areas.The growth of the Indian market is due to the focus of the Indian government on space technology and its initiatives to encourage the use of satellite data. There is vast potential and promising applications of satellite imagery services in the country of India, as there has been a rising advancement in technology along with sophistication of techniques in data analysis. Recent developments include: January 2023: The Indian Space Research Organization's National Remote Sensing Center released satellite images of Joshimath, a town in Uttarakhand that is slowly sinking due to land subsidence, and the images show that a rapid subsidence of 5.4 cm was observed in a span of twelve days between December last week and January first week., June 2022: Pataa Navigations, an India-based software firm, and Indian National Space Promotion and Authorisation Centre (IN-SPACe) signed an MoU to enable access to ISRO's Geospatial Services and APIs for the creation of an addressing system during the opening of the In-Space headquarters. The company would launch an addressing revolution in India by providing access to satellite image-based digital addresses. Through this MoU, the partnership would be for the ISRO portals Bhuvan, VEDAS, and MOSDAC services.. Key drivers for this market are: Government Initiatives to Foster the Growth of Satellite Imagery Services in India, Increasing Importance on Disaster Management and Mitigation Efforts. Potential restraints include: Affordability and Accessibility might restrain the Market Growth, Limited Standardization and Interoperability. Notable trends are: Government Initiatives to Foster the Growth of Satellite Imagery Services in India.

The Intelligent Remote Sensing Interpretation Software market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors. Firstly, advancements in artificial intelligence (AI) and machine learning (ML) are significantly enhancing the accuracy and speed of image analysis, leading to more efficient data processing and interpretation. Secondly, the rising adoption of cloud-based solutions is improving accessibility and scalability, reducing the need for substantial on-premise infrastructure investment. Thirdly, the increasing availability of high-resolution satellite and aerial imagery, coupled with the growing need for precise geospatial data in various applications, is boosting market demand. Specific applications such as precision agriculture, urban planning, and environmental monitoring are witnessing particularly rapid growth, as these sectors leverage the software's capabilities to optimize resource management and improve decision-making. While the high initial investment costs for software and hardware can be a restraint, the long-term benefits in terms of cost savings and improved efficiency are driving adoption. The competitive landscape is characterized by a mix of established technology giants and specialized geospatial companies, indicating a healthy and dynamic market. Based on a projected CAGR (assume 15% for illustrative purposes, adjusting to the provided value if available), and considering the market dynamics, we can expect continued market expansion throughout the forecast period. Further growth will be fueled by the increasing integration of remote sensing data with other sources like IoT sensors and GIS platforms, creating a more holistic view for various applications. Government initiatives promoting digitalization and infrastructure development, especially in emerging economies, will also contribute significantly to market growth. The market segmentation, with its diverse applications and deployment models (cloud-based vs. on-premise), indicates opportunities for specialization and targeted marketing strategies. While North America and Europe currently hold significant market share, the Asia-Pacific region, particularly China and India, are emerging as key growth drivers, spurred by increasing government investment in infrastructure projects and expanding digitalization efforts. The continued innovation in AI, coupled with the decreasing costs of high-resolution imagery and cloud computing, suggests that the Intelligent Remote Sensing Interpretation Software market will continue its upward trajectory in the coming years.

Remote Sensing Software Market Outlook

In 2023, the global remote sensing software market size was valued at approximately USD 3.8 billion and is expected to reach around USD 8.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. The robust growth of this market is driven by the increasing adoption of advanced remote sensing technologies across various industries, such as agriculture, defense, and urban planning, coupled with the rising demand for high-resolution satellite imagery.

One of the primary growth factors for the remote sensing software market is the escalating demand for precision agriculture. As the global population continues to rise, the need for efficient and effective agricultural practices has become paramount. Remote sensing software provides farmers with vital data regarding crop health, soil conditions, and water availability, enabling them to make informed decisions that enhance yield and resource utilization. Moreover, advancements in drone technology and satellite imagery have further augmented the capabilities of remote sensing software in the agricultural sector.

Another significant growth driver is the increasing focus on environmental monitoring and disaster management. Climate change and its associated impacts, such as more frequent and severe natural disasters, necessitate enhanced monitoring and response systems. Remote sensing software offers critical insights into environmental parameters like deforestation rates, water quality, and natural disaster occurrences. Governments and organizations worldwide are investing heavily in these technologies to mitigate the adverse effects of climate change and improve disaster preparedness and response.

The defense and intelligence sector also plays a crucial role in propelling the growth of the remote sensing software market. With the rising geopolitical tensions and the need for national security, defense agencies are increasingly relying on remote sensing technologies for surveillance, reconnaissance, and intelligence gathering. The ability to obtain real-time, high-resolution imagery and data has become indispensable for strategic planning and threat assessment, further bolstering the growth of this market segment.

Remote Sensing Technologies have revolutionized the way industries gather and analyze data from the earth's surface. These technologies encompass a wide range of tools and methods, including aerial and satellite imaging, which provide critical insights into various environmental and industrial parameters. By capturing data from multiple sensors, remote sensing technologies enable the creation of detailed maps and models that are invaluable for applications such as urban planning, agriculture, and disaster management. The integration of these technologies with advanced software solutions enhances their capabilities, allowing for real-time data processing and analysis. As a result, industries can make more informed decisions, optimize resource utilization, and improve operational efficiency.

Looking at the regional outlook, North America is expected to dominate the remote sensing software market during the forecast period, primarily due to the presence of key market players and substantial investments in technological advancements. Additionally, the Asia Pacific region is anticipated to exhibit the highest growth rate, driven by rapid urbanization, increasing defense budgets, and growing awareness about the benefits of remote sensing technologies. Europe is also projected to witness significant growth, fueled by stringent environmental regulations and government initiatives aimed at sustainable development.

Component Analysis

The remote sensing software market can be segmented by component into software and services. The software segment encompasses various types of remote sensing tools, including image processing software, data analysis software, and geographic information system (GIS) software. These tools are essential for interpreting and analyzing the vast amounts of data collected through remote sensing technologies. The increasing demand for high-resolution data and the need for real-time analysis have been key factors driving the growth of the software segment.

Within the software segment, image processing software holds a significant share due to its ability to enhance and interpret satellite and aerial imagery. This software enables the extractio

EOS-WEBSTER has agreed to serve satellite image subsets for the Forest Watch ("http://www.forestwatch.sr.unh.edu") program and other educational programs which make use of satellite imagery. Forest Watch is a New England-wide environmental education activity designed to introduce teachers and students to field, laboratory, and satellite data analysis methods for assessing the state-of-health of local forest stands. One of the activities in Forest Watch involves image processing and data analysis of Landsat Thematic Mapper data (TM/ETM+) for the area around a participant's school. The image processing of local Landsat data allows the students to use their ground truth data from field-based activities to better interpret the satellite data for their own back yard. Schools use a freely available image processing software, MultiSpec ("http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"), to analyze the imagery. Value-added Landsat data, typically in a 512 x 512 pixel subset, are supplied by this collection. The Forest Watch program has supplied the data subsets in this collection based on the schools involved with their activities.

Satellite data subsets may be searched by state or other category, and by spectral type. These images may be previewed through this system, ordered, and downloaded. Some historic Landsat 5 data subsets, which were acquired for this program, are also provided through this system. Landsat 5 subsets are multispectral data with 5 bands of data (TM bands 1-5). Landsat 7 subsets contain all bands of data and each subset has three spectral file types: 1) multispectral (ETM+ bands 1-5 and 7), 2) panchromatic (ETM+ band 8), and 3) Thermal (ETM+ band 6 high and low gain channels). Each spectral type must be ordered separately; this can be accomplished by choosing more than one spectral file type in your search parameters.

These image subsets are served in the ERDAS Imagine (.img) format, which can be opened by newer versions of the MultiSpec program (versions greater than Nov. 1999). The MultiSpec program can be downloaded via the Internet at: "http://dynamo.ecn.purdue.edu/%7Ebiehl/MultiSpec/"

A header file is provided with most Landsat 7 subsets giving the specifics of the image.

Please refer to the references to learn more about Forest Watch, Landsat, and the data this satellite acquires.

In the near future we hope to release a new Satellite Interface, which would allow a user to search for satellite data from a number of platforms based on user-selected search parameters and then sub-set the data and choose an appropriate output format.

If you have any other questions regarding our Forest Watch Satellite data holdings, please contact our User Services Personnel (support@eos-webster.sr.unh.edu).

Available Data Sets:

Many New England subsets are available, based on the location of participating schools in the Forest Watch program. Additional scenes are also included based on historical use within the Forest Watch program. Other scenes may be added in the future. If you don't see a scene of the location you are interested in, and that location is within New England, then please contact User Services (support@eos-webster.sr.unh.edu) to see if we can custom-create a subset for you.

Data Format

The data are currently held in EOS-WEBSTER in ERDAS Imagine (.img) format. This format is used by new versions of the MultiSpec program, and other image processing programs. Most of the subset scenes provided through this system have been projected to a Lambert Projection so that MultiSpec can display Latitude and Longitude values for each image cell (see "http://www.forestwatch.sr.unh.edu/online/" Using Mac MultiSpec to display Lat./Lon. Coordinates).

Data can be ordered by spectral type. For Landsat 7, three spectral types are available: 1) Multispectral (bands 1-5 & 7), 2) Panchromatic (pan), and 3) Thermal (bands 6 a&b) (see Table 2). The multispectral (ms) files contain six bands of data, the panchromatic (pan) files contains one band of data, and the thermal (therm) files contain two bands of data representing a high and low sensor gain.

A header file is provided for most Landsat 7 subsets which have been projected in the Lambert projection. This header file provides the necessary information for importing the data into MultiSpec for Latitude/Longitude display.

Aerial Imagery System Market Outlook

The global aerial imagery system market size was valued at approximately USD 3.7 billion in 2023 and is projected to reach USD 10.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.3% during the forecast period. The rapid adoption of advanced technologies such as high-resolution cameras, drones, and satellite imaging is significantly contributing to the growth of this market. Increasing demand for sophisticated geospatial data in various sectors such as agriculture, defense, urban planning, and environmental monitoring is also fueling market expansion.

Various growth factors are driving the aerial imagery system market. One of the primary factors is the increasing need for high-precision and real-time data in urban planning and smart city projects. Governments and municipalities are heavily investing in aerial imagery systems to monitor infrastructure development, manage urban sprawl, and improve city planning. The integration of these systems with Geographic Information Systems (GIS) and other data analytics platforms allows for more efficient data processing and decision-making. This integration helps in creating more sustainable and efficient urban environments.

Another significant growth factor is the rising application of aerial imagery in agriculture. Farmers and agribusinesses are increasingly utilizing aerial imagery systems to monitor crop health, assess soil conditions, and optimize irrigation systems. The high-resolution images and data collected through these systems help in making informed decisions that can improve crop yields and reduce costs. Precision agriculture is becoming a critical component in modern farming, and aerial imagery systems are at the forefront of this technological advancement. As the global population continues to rise, the demand for efficient agricultural practices will further drive the market.

Moreover, advancements in drone technology and declining costs of unmanned aerial vehicles (UAVs) are making aerial imagery systems more accessible to a broader range of industries. Drones equipped with high-resolution cameras and sensors can capture detailed images and data over large areas quickly and efficiently. This capability is particularly beneficial for applications such as disaster management, where timely and accurate information is crucial. The use of UAVs in emergency response situations to assess damage, plan rescue operations, and monitor recovery efforts is becoming increasingly common, thereby bolstering the market.

In terms of regional outlook, North America currently holds the largest market share due to the presence of numerous technology companies and high adoption rates of advanced imaging systems. The region is expected to maintain its dominance throughout the forecast period. However, the Asia Pacific region is anticipated to experience the highest growth rate, driven by rapid urbanization, infrastructural development, and increasing investments in smart city projects. Countries like China, India, and Japan are leading the charge in adopting aerial imagery technologies, which will significantly contribute to the market's growth in this region.

Component Analysis

The component segmentation of the aerial imagery system market includes hardware, software, and services. The hardware segment encompasses cameras, sensors, drones, and satellite systems, which are fundamental for capturing high-quality images and data. The growing demand for high-resolution and multispectral cameras is a significant driver in this segment. These advanced cameras offer superior image quality and have become essential tools in sectors such as agriculture, environmental monitoring, and defense. Additionally, continuous innovations in sensor technologies are enabling more precise data collection, further propelling the hardware market.

On the software side, the market is witnessing substantial growth due to the increasing need for data processing, analysis, and visualization tools. Software solutions are critical for converting raw imagery data into actionable insights. Geographic Information Systems (GIS), image processing software, and data analytics platforms are some of the key components in this segment. The integration of artificial intelligence and machine learning algorithms into these software solutions is enhancing their capabilities, allowing for more accurate and efficient data interpretation. This, in turn, is d