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.

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.

Global mining footprint mapped from high-resolution satellite imagery.

Satellite images are essentially the eyes in the sky. Some of the recent satellites, such as WorldView-3, provide images with a spatial resolution of *** meters. This satellite with a revisit time of under ** hours can scan a new image of the exact location with every revisit.

Spatial resolution explained Spatial resolution is the size of the physical dimension that can be represented on a pixel of the image. Or in other words, spatial resolution is a measure of the smallest object that the sensor can resolve measured in meters. Generally, spatial resolution can be divided into three categories:

– Low resolution: over 60m/pixel. (useful for regional perspectives such as monitoring larger forest areas)

– Medium resolution: 10‒30m/pixel. (Useful for monitoring crop fields or smaller forest patches)

– High to very high resolution: ****‒5m/pixel. (Useful for monitoring smaller objects like buildings, narrow streets, or vehicles)

Based on the application of the imagery for the final product, a choice can be made on the resolution, as labor intensity from person-hours to computing power required increases with the resolution 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 2020 - April 2021.

Technical specifications:

• 450 x ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:50,000 tile layout
• Satellite sensors: ESA Sentinel-2A and Sentinel-2B
• Acquisition dates: September 2020 - April 2021
• 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.

This dataset combines high-resolution satellite imagery from three distinct sources into a unified resource for advanced semantic segmentation tasks. It features a total of 203 images, meticulously annotated to identify six key classes:

1. Building: #3C1098
2. Land (unpaved area): #8429F6
3. Road: #6EC1E4
4. Vegetation: #FEDD3A
5. Water: #E2A929
6. Unlabeled: #9B9B9B
7. Object

The images span diverse urban and natural environments, providing a rich basis for developing and testing segmentation models. The standardized labels and masks facilitate straightforward application in machine learning projects aimed at urban planning, environmental monitoring, and beyond.

This dataset was created by joining 3 different datasets; 1. Semantic segmentation of aerial imagery 2. Land Cover Classification : Bhuvan Satellite Data 3. Urban Segmentation - ISPRS

I joined the datasets and standardized the masks so they have same color codings. And you can see the data yourself. Dive into the data and discover its potential for your projects!

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.

World Imagery provides one meter or better satellite and aerial imagery in many parts of the world and lower resolution satellite imagery worldwide. The map includes 15m TerraColor imagery at small and mid-scales (~1:591M down to ~1:72k) and 2.5m SPOT Imagery (~1:288k to ~1:72k) for the world. The map features 0.5m resolution imagery in the continental United States and parts of Western Europe from DigitalGlobe. Additional DigitalGlobe sub-meter imagery is featured in many parts of the world. In the United States, 1 meter or better resolution NAIP imagery is available in some areas. In other parts of the world, imagery at different resolutions has been contributed by the GIS User Community. In select communities, very high resolution imagery (down to 0.03m) is available down to ~1:280 scale. You can contribute your imagery to this map and have it served by Esri via the Community Maps Program. View the list of Contributors for the World Imagery Map.CoverageView the links below to learn more about recent updates and map coverage:What's new in World ImageryWorld coverage mapCitationsThis layer includes imagery provider, collection date, resolution, accuracy, and source of the imagery. With the Identify tool in ArcGIS Desktop or the ArcGIS Online Map Viewer you can see imagery citations. Citations returned apply only to the available imagery at that location and scale. You may need to zoom in to view the best available imagery. Citations can also be accessed in the World Imagery with Metadata web map.UseYou can add this layer to the ArcGIS Online Map Viewer, ArcGIS Desktop, or ArcGIS Pro. To view this layer with a useful reference overlay, open the Imagery Hybrid web map. A similar raster web map, Imagery with Labels, is also available.FeedbackHave you ever seen a problem in the Esri World Imagery Map that you wanted to report? You can use the Imagery Map Feedback web map to provide comments on issues. The feedback will be reviewed by the ArcGIS Online team and considered for one of our updates.

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

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.

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

The 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).

Footprints for all imagery in the Government of Yukon High Resolution Satellite Imagery Service.Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon's digital map data collection.For more information: geomatics.help@yukon.ca

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

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

aligning with the typical resolution of panchromatic (PAN) bands in modern VHR satellites.

The folders in labels.zip contain labels for solar panel objects as part of the Solar Panels in Satellite Imagery dataset. The labels are partitioned based on corresponding image type: 31 cm native and 15.5 cm HD resolution imagery. In total, there are 2,542 object labels for each image type, following the same naming convention as the corresponding image chips. The corresponding image chips may be accessed at: https://resources.maxar.com/product-samples/15-cm-hd-and-30-cm-view-ready-solar-panels-germany The naming convention for all labels includes the name of the dataset, image type, tile identification number, minimum x bound, minimum y bound, and window size. The minimum bounds correspond to the origin of the chip in the full tile.
Labels are provided in .txt format compatible with the YOLTv4 architecture, where a single row in a label file contains the following information for one solar panel object: category, x-center, y-center, x-width, and y-width. Center and width values are normalized by chip sizes (416 by 416 pixels for native chips and 832 by 832 pixels for HD chips). The geocoordinates for each solar panel object may be determined using the native resolution labels (found in the labels_native directory). The center and width values for each object, along with the relative location information provided by the naming convention for each label, may be used to determine the pixel coordinates for each object in the full, corresponding native resolution tile. The pixel coordinates may be translated to geocoordinates using the EPSG:32633 coordinate system and the following geotransform for each tile:

Tile 1: (307670.04, 0.31, 0.0, 5434427.100000001, 0.0, -0.31) Tile 2: (312749.07999999996, 0.31, 0.0, 5403952.860000001, 0.0, -0.31) Tile 3: (312749.07999999996, 0.31, 0.0, 5363320.540000001, 0.0, -0.31)

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.

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.

Satellite sensor artifacts can negatively impact the interpretation of satellite data. One such artifact is linear features in imagery which can be caused by a variety of sensor issues and can present as either wide, consistent features called banding, or as narrow, inconsistent features called striping. This study used high-resolution data from DigitalGlobe's WorldView-3 satellite collected at Lake Okeechobee, Florida, on 30 August 2017. Primarily designed as a land sensor, this study investigated the impact of vertical artifacts on both at-sensor radiance and a spectral index for an aquatic target. This dataset is not publicly accessible because: NGA Nextview license agreements prohibit the distribution of original data files from WorldView due to copyright. It can be accessed through the following means: National Geospatial Intelligence Agency contract details prevent distribution of Maxar data. Questions regarding Nextvew can be sent so NGANextView_License@nga.mil. Questions regarding the NASA Commercial Data Buy can be sent to yvonne.ivey@nasa.gov. Format: high-resolution data from DigitalGlobe's WorldView-3 satellite. This dataset is associated with the following publication: Coffer, M., P. Whitman, B. Schaeffer, V. Hill, R. Zimmerman, W. Salls, M. Lebrasse, and D. Graybill. Vertical artifacts in high-resolution WorldView-2 and WorldView-3 satellite imagery of aquatic systems. INTERNATIONAL JOURNAL OF REMOTE SENSING. Taylor & Francis, Inc., Philadelphia, PA, USA, 43(4): 1199-1225, (2022).