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

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

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

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

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

The satellite image of Canada is a composite of several individual satellite images form the Advanced Very High Resolution Radiometre (AVHRR) sensor on board various NOAA Satellites. The colours reflect differences in the density of vegetation cover: bright green for dense vegetation in humid southern regions; yellow for semi-arid and for mountainous regions; brown for the north where vegetation cover is very sparse; and white for snow and ice. An inset map shows a satellite image mosaic of North America with 35 land cover classes, based on data from the SPOT satellite VGT (vegetation) sensor.

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.

QuickBird high resolution optical products are available as part of the Vantor 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 Vantor 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.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

Context The Bhuvan Satellite Dataset is a valuable resource for land cover analysis and segmentation tasks. It includes a collection of satellite images and corresponding segmentation masks. The segmentation masks provide a pixel-level classification for five distinct land cover classes: vegetation, urban areas, forest, water bodies, and roads.

Content The dataset consists of satellite 2D images of Varanasi, a city located in the northern part of India, in the state of Uttar Pradesh, with coordinates ranging from 25.3° to 25.5° N latitude and 83° to 83.2° E longitude. It comprises a collection of high-resolution images capturing the Earth's surface. These images were obtained from the Indian Remote Sensing Satellite (IRS) and were processed and made available through the Bhuvan Geo Platform, which is managed by the Indian Space Research Organization (ISRO).

The dataset includes various files that offer valuable insights into the land cover classification and segmentation tasks. Here are the different data files available:

1. pixel_based_mask: This file contains pixel-level segmentation masks that classify each pixel in the satellite images into one of the land cover classes, such as vegetation, urban areas, forest, water bodies, and roads.
2. test_mask and train_mask: These files contain the manually generated segmentation masks specifically for creating the pixel-based mask.
3. test_image and train_image: These files contain the corresponding satellite images that are used for testing and training the land cover classification models. These high-resolution 2D images provide visual information about the Earth's surface in Varanasi.
4. class_dict_seg.csv: This file serves as a reference for the class labels used in the segmentation masks. It provides a mapping between the class names and their corresponding numeric labels in RGB format.

Researchers and professionals can leverage this dataset to conduct in-depth analysis and segmentation tasks related to land cover classification. The dataset's rich content enables the exploration of urban development, vegetation patterns, forest cover, water resources, and road networks within the Varanasi region.

Acknowledgements We would like to express our gratitude to Bhuvan - India Geo Platform of ISRO for providing the satellite images, which serve as a valuable resource for land cover analysis. We appreciate their efforts in collecting and curating satellite images, enabling researchers and professionals to explore and advance their work in remote sensing and geospatial analysis.

Inspiration Artificial Intelligence, Computer Vision, Image Processing, Deep Learning, Machine Learning, Satellite Image, Remote Sensing

The size of the Nordics Satellite Imagery Services Market market was valued at USD 0.22 Million in 2023 and is projected to reach USD 0.54 Million by 2032, with an expected CAGR of 13.62% during the forecast period. 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.

The SPOT 6 and 7 ESA archive is a dataset of SPOT 6 and SPOT 7 products that ESA collected over the years. The dataset regularly grows as ESA collects new SPOT 6 and 7 products. SPOT 6 and 7 Primary and Ortho products can be available in the following modes: Panchromatic image at 1.5m resolution Pansharpened colour image at 1.5m resolution Multispectral image in 4 spectral bands at 6m resolution Bundle (1.5m panchromatic image + 6m 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.

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-infra...

This dataset collection contains A0 maps of the Keppel Island region based on satellite imagery and fine-scale habitat mapping of the islands and marine environment. This collection provides the source satellite imagery used to produce these maps and the habitat mapping data.

The imagery used to produce these maps was developed by blending high-resolution imagery (1 m) from ArcGIS Online with a clear-sky composite derived from Sentinel 2 imagery (10 m). The Sentinel 2 imagery was used to achieve full coverage of the entire region, while the high-resolution was used to provide detail around island areas.

The blended imagery is a derivative product of the Sentinel 2 imagery and ArcGIS Online imagery, using Photoshop to to manually blend the best portions of each imagery into the final product. The imagery is provided for the sole purpose of reproducing the A0 maps.

Methods:

The high resolution satellite composite composite was developed by manual masking and blending of a Sentinel 2 composite image and high resolution imagery from ArcGIS Online World Imagery (2019).

The Sentinel 2 composite was produced by statistically combining the clearest 10 images from 2016 - 2019. These images were manually chosen based on their very low cloud cover, lack of sun glint and clear water conditions. These images were then combined together to remove clouds and reduce the noise in the image.

The processing of the images was performed using a script in Google Earth Engine. The script combines the manually chosen imagery to estimate the clearest imagery. The dates of the images were chosen using the EOBrowser (https://www.sentinel-hub.com/explore/eobrowser) to preview all the Sentinel 2 imagery from 2015-2019. The images that were mostly free of clouds, with little or no sun glint, were recorded. Each of these dates was then viewed in Google Earth Engine with high contrast settings to identify images that had high water surface noise due to algal blooms, waves, or re-suspension. These were excluded from the list. All the images were then combined by applying a histogram analysis of each pixel, with the final image using the 40th percentile of the time series of the brightness of each pixel. This approach helps exclude effects from clouds.

The contrast of the image was stretched to highlight the marine features, whilst retaining detail in the land features. This was done by choosing a black point for each channel that would provide a dark setting for deep clear water. Gamma correction was then used to lighten up the dark water features, whilst not ove- exposing the brighter shallow areas.

Both the high resolution satellite imagery and Sentinel 2 imagery was combined at 1 m pixel resolution. The resolution of the Sentinel 2 tiles was up sampled to match the resolution of the high-resolution imagery. These two sets of imagery were then layered in Photoshop. The brightness of the high-resolution satellite imagery was then adjusting to match the Sentinel 2 imagery. A mask was then used to retain and blend the imagery that showed the best detail of each area. The blended tiles were then merged with the overall area imagery by performing a GDAL merge, resulting in an upscaling of the Sentinel 2 imagery to 1 m resolution.


Habitat Mapping:

A 5 m resolution habitat mapping was developed based on the satellite imagery, aerial imagery available, and monitoring site information. This habitat mapping was developed to help with monitoring site selection and for the mapping workshop with the Woppaburra TOs on North Keppel Island in Dec 2019.

The habitat maps should be considered as draft as they don't consider all available in water observations. They are primarily based on aerial and satellite images.

The habitat mapping includes: Asphalt, Buildings, Mangrove, Cabbage-tree palm, Sheoak, Other vegetation, Grass, Salt Flat, Rock, Beach Rock, Gravel, Coral, Sparse coral, Unknown not rock (macroalgae on rubble), Marine feature (rock).

This assumed layers allowed the digitisation of these features to be sped up, so for example, if there was coral growing over a marine feature then the boundary of the marine feature would need to be digitised, then the coral feature, but not the boundary between the marine feature and the coral. We knew that the coral was going to cut out from the marine feature because the coral is on top of the marine feature, saving us time in digitising this boundary. Digitisation was performed on an iPad using Procreate software and an Apple pencil to draw the features as layers in a drawing. Due to memory limitations of the iPad the region was digitised using 6000x6000 pixel tiles. The raster images were converted back to polygons and the tiles merged together.

A python script was then used to clip the layer sandwich so that there is no overlap between feature types.

Habitat Validation:

Only limited validation was performed on the habitat map. To assist in the development of the habitat mapping, nearly every YouTube video available, at the time of development (2019), on the Keppel Islands was reviewed and, where possible, georeferenced to provide a better understanding of the local habitats at the scale of the mapping, prior to the mapping being conducted. Several validation points were observed during the workshop. The map should be considered as largely unvalidated.

data/coastline/Keppels_AIMS_Coastline_2017.shp:
The coastline dataset was produced by starting with the Queensland coastline dataset by DNRME (Downloaded from http://qldspatial.information.qld.gov.au/catalogue/custom/detail.page?fid={369DF13C-1BF3-45EA-9B2B-0FA785397B34} on 31 Aug 2019). This was then edited to work at a scale of 1:5000, using the aerial imagery from Queensland Globe as a reference and a high-tide satellite image from 22 Feb 2015 from Google Earth Pro. The perimeter of each island was redrawn. This line feature was then converted to a polygon using the "Lines to Polygon" QGIS tool. The Keppel island features were then saved to a shapefile by exporting with a limited extent.

data/labels/Keppel-Is-Map-Labels.shp:
This contains 70 named places in the Keppel island region. These names were sourced from literature and existing maps. Unfortunately, no provenance of the names was recorded. These names are not official. This includes the following attributes:
- Name: Name of the location. Examples Bald, Bluff
- NameSuffix: End of the name which is often a description of the feature type: Examples: Rock, Point
- TradName: Traditional name of the location
- Scale: Map scale where the label should be displayed.

data/lat/Keppel-Is-Sentinel2-2016-19_B4-LAT_Poly3m_V3.shp:
This corresponds to a rough estimate of the LAT contours around the Keppel Islands. LAT was estimated from tidal differences in Sentinel-2 imagery and light penetration in the red channel. Note this is not very calibrated and should be used as a rough guide. Only one rough in-situ validation was performed at low tide on Ko-no-mie at the edge of the reef near the education centre. This indicated that the LAT estimate was within a depth error range of about +-0.5 m.

data/habitat/Keppels_AIMS_Habitat-mapping_2019.shp:
This shapefile contains the mapped land and marine habitats. The classification type is recorded in the Type attribute.

Format:

GeoTiff (Internal JPEG format - 538 MB)
PDF (A0 regional maps - ~30MB each)
Shapefile (Habitat map, Coastline, Labels, LAT estimate)

Data Location:

This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\Keppels_AIMS_Regional-maps

About Dataset

This dataset is designed for binary classification tasks on geospatial imagery, specifically to distinguish between land areas with trees and those without. The images were captured by the Sentinel-2 satellite.

The dataset structure is straightforward: - Each image has a resolution of 64×64 pixels with encoded in JPG format. - Images are organized into two folders: "Trees" and "NoTrees", corresponding to the two classes. - Each folder contains 5,200 images, totaling 10,400 images across the dataset.

Note: The dataset does not include predefined training, validation, or test splits. Users should partition the data as needed for their specific machine learning, deep learning workflows.

And you can also cite the source of this data EUROSAT: Helber, P., Bischke, B., Dengel, A., & Borth, D. (2019). Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7), 2217-2226.

Since its launch in September 1999, GeoEye's IKONOS satellite has provided a reliable stream of image data since January 2000, which has become the standard for commercial high-resolution satellite data products. With an altitude of 681 km and a revisit time of approximately 3 days, IKONOS produces one-meter panchromatic and four-meter multispectral imagery that can be combined to accommodate a wide range of high-resolution imagery applications.

The PlanetScope Level 1B Basic Scene and Level 3B Ortho Scene full archive products are available as part of Planet imagery offer. The Unrectified Asset: PlanetScope Basic Analytic Radiance (TOAR) product is a Scaled Top of Atmosphere Radiance (at sensor) and sensor corrected product, without correction for any geometric distortions inherent in the imaging processes and is not mapped to a cartographic projection. The imagery data is accompanied by Rational Polynomial Coefficients (RPCs) to enable orthorectification by the user. This kind of product is designed for users with advanced image processing and geometric correction capabilities. Basic Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Rational Polynomial Coefficients (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, Rededge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Accuracy <10 m RMSE The Rectified assets: The PlanetScope Ortho Scene product is radiometrically-, sensor- and geometrically- corrected and is projected to a UTM/WGS84 cartographic map projection. The geometric correction uses fine Digital Elevation Models (DEMs) with a post spacing of between 30 and 90 metres. Ortho Scene Product Components and Format Product Components Image File (GeoTIFF format) Metadata File (XML format) Thumbnail File (GeoTIFF format) Unusable Data Mask UDM File (GeoTIFF format) Usable Data Mask UDM2 File (GeoTIFF format) Bands 3-band natural colour (red, green, blue) or 4-band multispectral image (blue, green, red, near-infrared) or 8-band (coastal-blue, blue, green I, green, yellow, red, RedEdge, near-infrared) Ground Sampling Distance Approximate, satellite altitude dependent Dove-C: 3.0 m-4.1 m Dove-R: 3.0 m-4.1 m SuperDove: 3.7 m-4.2 m Projection UTM WGS84 Accuracy <10 m RMSE PlanetScope Ortho Scene product is available in the following: PlanetScope Visual Ortho Scene product is orthorectified and colour-corrected (using a colour curve) 3-band RGB Imagery. This correction attempts to optimise colours as seen by the human eye providing images as they would look if viewed from the perspective of the satellite. PlanetScope Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and corrected for surface reflection. This data is optimal for value-added image processing such as land cover classifications. PlanetScope Analytic Ortho Scene Surface Reflectance product is orthorectified, 4-band BGRN or 8-band Coastal Blue, Blue, Green I, Green, Yellow, Red, RedEdge, NIR Imagery with geometric, radiometric and calibrated to top of atmosphere radiance. As per ESA policy, very high-resolution imagery of conflict areas cannot be provided.

The WorldView-1 Level 1B Panchromatic Imagery collection contains satellite imagery acquired from Maxar Technologies (formerly known as DigitalGlobe) by the Commercial Smallsat Data Acquisition (CSDA) Program. Panchromatic imagery is collected by the DigitalGlobe WorldView-1 satellite using the WorldView-60 camera across the global land surface from September 2007 to the present. Data have a spatial resolution of 0.5 meters at nadir and a temporal resolution of approximately 1.7 days. The data are provided in National Imagery Transmission Format (NITF) and GeoTIFF formats. This level 1B data is sensor corrected and is an un-projected (raw) product. The data potentially serve a wide variety of applications that require high resolution imagery. Data access is restricted based on a National Geospatial-Intelligence Agency (NGA) license, and investigators must be approved by the CSDA Program.

The pan-European High Resolution (HR) Image Mosaic 2018 provides cloud-free high resolution true colour imagery for EEA39 countries. The mosaic has been produced using Sentinel-2 data in 10 meter resolution, at a Sentinel 2 tile level, and consists of 1079 Sentinel-2 tiles. The imagery for each state is acquired within a predefined window corresponding to the vegetation season in 2018.

The true colour (RGB) composite consists of a three band stack and includes the following bands: Band 4 – Red (0.665 μm) Band 3 – Green (0.560 μm) Band 2 – Blue (0.490 μm)

The mosaic primarily is used as input data in the production of various Copernicus Land Monitoring Service (CLMS) datasets and services, such as land cover maps and high resolution layers on land cover characteristic and can be also useful for CLMS users for visualizations and classifications on land.

can be also used to automatically create mosaics over the area of interest.

WorldView-3 high resolution optical products are available as part of the Vantor 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 Vantor 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.

The ESA SPOT 1-5 collection is a dataset of SPOT 1 to 5 Panchromatic and Multispectral products that ESA collected over the years. The HRV(IR) sensor onboard SPOT 1-4 provides data at 10 m spatial resolution Panchromatic mode (-1 band) and 20 m (Multispectral mode -3 or 4 bands). The HRG sensor on board of SPOT-5 provides spatial resolution of the imagery to < 3 m in the panchromatic band and to 10 m in the multispectral mode (3 bands). The SWIR band imagery remains at 20 m. The dataset mainly focuses on European and African sites but some American, Asian and Greenland areas are also covered. Spatial coverage: Check the spatial coverage of the collection on a map available on the Third Party Missions Dissemination Service. The SPOT Collection

CNR MED Sea Surface Temperature provides daily gap-free maps (L4) at 0.01 deg. x 0.01 deg. horizontal resolution over the Black Sea. The data are obtained from infra-red measurements collected by satellite radiometers and statistical interpolation. It is the CMEMS sea surface temperature nominal operational product for the Black sea.

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