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.

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.

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

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.

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...

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.

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

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 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 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

The NSW SPOT6/7 imagery product is a state-wide satellite imagery product provided by Geoimage Pty Ltd for NSW Government. The images were captured September 2022 through to March 2023. The imagery scenes used to create the NSW mosaic includes Lord Howe Island. This imagery data set has been acquired through GeoImages Pty Ltd and Airbus Defence and Space.

SPOT imagery products offer high resolution over broad areas using the SPOT 6/7 satellites. A SPOT satellite acquisition covers large areas in a single pass at resolutions up to 1.5m. Such precise coverage is ideal for applications at national and regional scales from 1:250,000 to 1:15,000. SPOT 6/7 also includes the benefits of the near-infrared (NIR) which enables applications for detection of features not visible to the human eye, such as detecting and monitoring vegetation health.

Data products supplied for all of NSW are:

1. State-wide mosaic

2. 100k Mapsheet tiles (GDA94 and GDA2020)

3. Multi spectral scenes (GDA94 and GDA2020)

4. Pan sharpened scenes (GDA94 and GDA2020)

5. Panchromatic scenes (GDA94 and GDA2020)

6. Shapefile cutlines of statewide mosaic

The statewide mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG2000 file (456gb in size). Individual 100k mapsheet mosaics contain BGR+NIR band combination; unenhanced 16-bit GeoTIFF format tile.

The NSW mosaic is available from internal DPE APOLLO Image Webserver for DCCEEW employees.

The 4band 100k mapsheet tiles are available for download from JDAP(pending). The rectified multispectral, pan sharpened and panchromatic scenes are available for download from JDAP (pending)

Acknowledgement when referencing: includes material © CNES_ (year of production), Distribution Airbus Services/SPOT Image, S.A, France, all rights reserved

Contact spatial.imagery@environment.nsw.gov.au for further information or to request access to JDAP

These image products are only available to other NSW Government agencies upon request.

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.

"Gaofen-1 satellite" is the first earth observation system with high resolution, launched in April 2013. Its data is mainly used now in the fields of land, agriculture and environment. "Gaofeng-1" has three different resolutions such as 2 meters of panchromatic data, 8 meters and 16 meters of spectral data, combining. The Institute of agricultural resources and regionalization, Chinese academy of agricultural sciences, as the main application unit of the agricultural sector, can obtain relevant data for free and in real time. After the atmospheric and radiation correction, geometric correctionand projection transformation, and on the basis of "status of land use classification standard (GB - T21010-2015)" issued by the ministry of land and resources, for all types of land classification and summary statistics can be obtained through image analysis. At present, the land-use classification and extraction of 2016-2017 of 16 provinces have been preliminarily completed, which has formed a land-use map and statistics based on the administrative region. Comparing with the land-use data obtained by the Institute of Geographic Science and Resources, Chinese academy of sciences based on MODIS data, this data has a higher resolution and a characteristic of more up-to-date, and it can provide better service for all kinds of management and research departments.

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

We introduce a cost-effective public health analysis solution for low- and middle-income countries—the Multi-Modal Satellite Imagery Dataset in Colombia. By leveraging high-quality, spatiotemporally aligned satellite images and corresponding metadata, the dataset integrates economic, demographic, meteorological, and epidemiological data. Employing a single forwards and a forward-backward technique ensures clear satellite images with minimal cloud cover for every epi-week, significantly enhancing overall data quality. The extraction process utilizes the satellite extractor package powered by the SentinelHub API, resulting in a comprehensive dataset of 12,636 satellite images from 81 municipalities in Colombia between 2016 and 2018, along with relevant metadata. Beyond expediting public health data analysis across diverse locations and timeframes, this versatile framework consistently captures multimodal features. Its applications extend to various realms in multimodal AI, encompassing deforestation monitoring, forecasting education indices, water quality assessment, tracking extreme climatic events, addressing epidemic illnesses, and optimizing precision agriculture.

The NSW SPOT 5 imagery product is a state-wide satellite imagery product provided by the Remote Sensing and Regulatory Mapping team of NSW Government. Capture dates for imagery products for 2010-2015 are;

• 2015 - September 2014 through to March 2015

• 2014 - October 2013 through to August 2014

• 2013 - January 2012 through to July 2013

• 2012 - January 2011 through to July 2012

• 2011 - November 2010 through to July 2011

• 2010 - October 2009 through to August 2010

The imagery scenes used to create the NSW mosaic includes Lord Howe Island. This imagery data sets for 2010-2012 have been supplied by SPOT imaging and processing done by GeoImage Pty Ltd. Imagery for 2013-2015 has been supplied by Astrium/Airbus and processed by GeoImage Pty Ltd.

SPOT imagery products offer high resolution over broad areas using the SPOT 5 satellites. A SPOT satellite acquisition covers large areas in a single pass at resolutions up to 2.5m. Such precise coverage is ideal for applications at national and regional scales from 1:250,000 to 1:15,000.

Data products supplied for all of NSW are:

1. State-wide mosaic

2. Reflectance scenes

3. Panchromatic scenes

The statewide mosaic is provided as a Red Green Blue (RGB) band combination; contrast enhanced lossless 8-bit JPEG2000 file.

The NSW mosaic is available from internal DPE APOLLO Image Webserver for DCCEEW employees.

The rectified reflectance and panchromatic scenes are available for download from JDAP.

Contact spatial.imagery@environment.nsw.gov.au for further information or to request access to JDAP

“Includes material © CNES 2010, 2011 & 2012, Distribution Astrium Services / Spot Image S.A., France, all rights reserved”

These image products are only available to other NSW Government agencies upon request.

Massachusetts Buildings Dataset (Resized to 512x512) for Segmentation and Detection

Preprocessed Satellite Imagery for Building Detection and Segmentation Tasks

This dataset is a resized and pathified version of the Massachusetts Buildings Dataset, where all images have been standardized to 512x512 pixels. The original dataset includes satellite imagery and corresponding building masks, intended for use in building detection and segmentation tasks.

Resizing the images to 512x512 ensures uniformity across the dataset, facilitating better model training, especially when working with deep learning architectures like ResNet and EfficientNet. These resized images are ideal for use in segmentation and classification tasks, where high-resolution images may be required to identify urban structures and geographic features.

Applications:

• Building Segmentation: Perfect for segmentation tasks using models like UNet, SegNet, and FCNs, allowing pixel-level classification of buildings from satellite images.
• Urban Planning & GIS: Useful in GIS and urban planning projects for mapping building footprints, analyzing urban expansion, and infrastructure management.
• Transfer Learning with CNNs: Pretrained models like ResNet, EfficientNet, and DenseNet can be fine-tuned on this dataset for tasks involving remote sensing and geographic analysis.

Key Features:

• 512x512 Resized Images: High-quality satellite imagery resized for uniform model input.
• Building Masks Included: Pixel-accurate segmentation masks to aid in training deep learning models for building detection.
• Model Compatibility: Optimized for CNN-based architectures like ResNet, EfficientNet, and segmentation models like UNet for better performance on tasks related to building identification and segmentation.
• MIT License: The dataset is licensed under the MIT License, allowing free usage and distribution for research and commercial purposes.

This dataset offers a balanced, high-quality resource for any project involving satellite image analysis, building footprint detection, and urban planning applications, with the flexibility provided by its MIT License.

High-resolution satellite cloud image data *Changes in download URL as of September 15, 2023, please switch by December 31, 2023, the old version link will expire after the deadline. For those who need to download a large amount of data, please apply for membership at the open platform for meteorological data: https://opendata.cwa.gov.tw/index

The Meteosat Second Generation (MSG) satellites, operated by EUMETSAT (The European Organisation for the Exploitation of Meteorological Satellites), provide almost continuous imagery to meteorologists and researchers in Europe and around the world. These include visible, infra-red, water vapour, High Resolution Visible (HRV) images and derived cloud top height, cloud top temperature, fog, snow detection and volcanic ash products. These images are available for a range of geographical areas.

This dataset contains high resolution visible images from MSG satellites over the UK area. Imagery available from March 2005 onwards at a frequency of 15 minutes (some are hourly) and are at least 24 hours old.

The geographic extent for images within this datasets is available via the linked documentation 'MSG satellite imagery product geographic area details'. Each MSG imagery product area can be referenced from the third and fourth character of the image product name giving in the filename. E.g. for EEAO11 the corresponding geographic details can be found under the entry for area code 'AO' (i.e West Africa).