The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.

The Earth Observation Satellites Ground Stations market is experiencing robust growth, driven by increasing demand for high-resolution satellite imagery and data across various sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors. Firstly, the proliferation of small satellites and constellations is generating a massive volume of data requiring efficient ground infrastructure for reception and processing. Secondly, advancements in sensor technology are leading to higher-resolution imagery with enhanced analytical capabilities, stimulating demand across diverse applications like precision agriculture, environmental monitoring, urban planning, and disaster management. Finally, the increasing adoption of cloud-based solutions for data storage, processing, and analytics is streamlining workflows and lowering barriers to entry for users. Major players like Amazon Web Services, Microsoft Azure, and specialized providers like K-Sat and Infostellar are actively shaping the market landscape through their innovative offerings and strategic partnerships. However, several challenges remain. High infrastructure costs associated with setting up and maintaining ground stations, particularly those equipped to handle large volumes of data from advanced sensors, pose a significant hurdle for smaller players. Furthermore, regulatory complexities surrounding data ownership, access, and cross-border transfer can hinder market growth. Competition amongst established players and new entrants is also intensifying, driving the need for continuous innovation and cost optimization. The market segmentation reveals a strong emphasis on both government and commercial applications, with significant regional variations reflecting the differing levels of technological adoption and investment in space infrastructure across the globe. The forecast period of 2025-2033 promises a dynamic market characterized by ongoing technological advancement, strategic collaborations, and fierce competition, ultimately benefitting end-users with access to increasingly sophisticated earth observation data and services.

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the GOES-R ABI Reprocess User Guide.


NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape.
All of the imagery files are Cloud Optimised GeoTIFFs using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by STAC metadata. The imagery is organised by region and survey.

The Earth Observation Satellites Ground Stations market is experiencing robust growth, driven by increasing demand for high-resolution imagery and data across diverse sectors. The market, valued at approximately $5 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of around 8% throughout the forecast period (2025-2033). This expansion is fueled by several key factors. Firstly, the escalating need for precise environmental monitoring, particularly in areas like climate change research, disaster management, and precision agriculture, is significantly boosting demand. Secondly, advancements in satellite technology, leading to improved image resolution, data processing capabilities, and lower launch costs, are making Earth observation data more accessible and affordable. Finally, the expanding adoption of cloud-based data processing and analytics platforms is streamlining data management and analysis, further propelling market growth. Significant regional variations exist, with North America and Europe currently holding substantial market shares, primarily due to well-established space agencies and robust research infrastructure. However, the Asia-Pacific region is poised for rapid growth, driven by increasing government investments in space exploration and technological advancements. Competition in the Earth Observation Satellites Ground Stations market is intense, with a mix of established players and emerging companies vying for market share. Major technology providers like Amazon Web Services and Azure are playing a significant role, offering cloud-based platforms for data processing and storage. Specialized companies focusing on ground station operations and data analytics are also contributing significantly. The market is segmented by application (Aerospace, Meteorological, Biological Research, Military, Others) and type (Active Imaging, Passive Imaging). While Active Imaging currently dominates, Passive Imaging is witnessing faster growth due to its cost-effectiveness and suitability for specific applications. Challenges include the high initial investment required for setting up ground stations, stringent regulatory frameworks governing satellite data acquisition and processing, and the potential for data security breaches. Nevertheless, the long-term outlook for the Earth Observation Satellites Ground Stations market remains positive, driven by continuous technological innovation and the growing importance of Earth observation data across various sectors.

The Space Cloud Computing market is experiencing rapid growth, driven by increasing demand for data processing and storage capabilities in space-based applications. The market's expansion is fueled by several key factors, including the proliferation of Low Earth Orbit (LEO) constellations, the need for real-time data analytics in satellite imagery and remote sensing, and the rising adoption of cloud-native architectures in space missions. Major players like Amazon Web Services (AWS), Microsoft, and numerous space technology companies are investing heavily in developing robust and secure cloud solutions tailored for the unique challenges of the space environment, including latency, bandwidth limitations, and radiation hardening. The market is segmented by application (e.g., Earth observation, satellite communication, navigation), service type (e.g., IaaS, PaaS, SaaS), and deployment model (e.g., on-orbit, ground-based). While the initial investment in infrastructure and development is substantial, the long-term cost savings and operational efficiencies offered by space cloud computing are attracting significant investments. Looking ahead, the market is poised for significant expansion. The increasing number of commercial space launches and the growing adoption of advanced technologies like artificial intelligence (AI) and machine learning (ML) in space data analysis will further drive market growth. However, challenges remain, including the need for stringent cybersecurity measures to protect sensitive space-based data and the need for greater standardization and interoperability among different space cloud platforms. Government initiatives and collaborations between space agencies and private sector companies will play a crucial role in overcoming these challenges and unlocking the full potential of the Space Cloud Computing market. Based on reasonable estimations and industry trends, we anticipate sustained and robust growth through 2033.

Road Network Mapping from Multispectral Satellite Imagery: Leveraging Deep Learning and Spectral Bands Submitted to AGILE24 Abstract Updating road networks in rapidly changing urban landscapes is an important but difficult task, often challenged by the complexity and errors of manual mapping processes. Traditional methods that primarily use RGB satellite imagery struggle with obstacles in the environment and varying road structures, leading to limitations in global data processing. This paper presents an innovative approach that utilizes deep learning and multispectral satellite imagery to improve road network extraction and mapping. By exploring U-Net models with DenseNet backbones and integrating different spectral bands we apply semantic segmentation and extensive post-processing techniques to create georeferenced road networks. We trained two identical models to evaluate the impact of using images created from specially selected multispectral bands rather than conventional RGB images. Our experiments demonstrate the positive impact of using multispectral bands, by improving the results of the metrics Intersection over Union (IoU) by 6.5%, F1 by 5.4%, and the newly proposed relative graph edit distance (relGED) and topology metrics by 2.2% and 2.6% respectively. Data To use the code in this repository, download the required data from SpaceNet Challenge 3 (https://spacenet.ai/spacenet-roads-dataset/) via AWS. The SpaceNet Dataset by SpaceNet Partners is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. SpaceNet was accessed on 05.01.2023 from https://registry.opendata.aws/spacenet Software The analysis and results of this research were achieved with Python and several software packages such as: - tensorflow - networkx - Pillow, cv2 - GDAL, rasterio, shapely - APLS For a fully reproducible environment and software versions refer to 'environment.yml'. All data is licensed under CC BY 4.0, all software files are licensed under the MIT License. Reproducibility To execute the scripts and train your model, first refer to the 'Data' section of this file to download the data from the providers. Apply the preprocessing steps from 'preprocessing.py', but consider that to avoid redundancy, preprocessing steps not included in this repository are the conversion of geojson road data into training images, the reduction of satellite images to an 8-bit format, and their conversion into '.png' files. These steps can be achieved by applying and, if necessary, modifying the APLS library which is publicly available under https://github.com/CosmiQ/apls. Apply preprocessing to both RGB and MS images. To generate the latter execute the 'ms_channel_seperation.py' script while specifying the wanted multispectral channels. Execute the 'train_model.py' script to train your semantic segmentation model, and apply post-processing procedures with 'postprocessing.py'. Generate the metrics results by executing 'evaluation.py'. To save storage space, not all the used data is made available in this repository. Please refer to the 'Data' section of this file to access and download the data from the providers. Exemplary preprocessed training data (100 split images of Las Vegas) is included in the folders './data/tiled512/small_test_sample/ms/' and './data/tiled512/small_test_sample/rgb/'. Post-processed results are provided in the corresponding folders './results/UNetDense_MS_512/' and './results/UNetDense_RGB_512/'. These include the stitched and recombined images, without any post-processing applied to them, as well as the extracted and post-processed graphs as '.pickle' files. This provided data was used to calculate the metrics Intersection over Union (IoU), F1 score, relGED, and topology metric as presented in the paper. The figures included in the paper can be reproduced by saving images created during the preprocessing, training, and post-processing steps. To generate the plots of resulting graphs, refer to the corresponding functions and enable the boolean parameter 'plot'. Bounding boxes seen in the figures were drawn manually and only serve an explanatory purpose. Please be advised that file paths and folder structure have to be adapted manually in the scripts to suit the users folder structure. Be aware of selecting uniform file paths and storing the results in folders named after their model. Furthermore, the code is not meant to be executed from the terminal, running the individual scripts in an IDE is recommended.

SpaceNet, launched in August 2016 as an open innovation project offering a repository of freely available imagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal options to obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasets developed by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).

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

Sentinel-2 satellite imagery dataset providing high-resolution optical data for land monitoring, agriculture, and environmental applications.

This repository contains the global forest mask derived from the global maps of canopy height (CH) and canopy cover (CC) described by:

Weber, M.; Beneke, C.; Wheeler, C. Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery. Remote Sens. 2025, 17, 1594. https://doi.org/10.3390/rs17091594

The source variables for this derived product are available at 10.5281/zenodo.15269923. This dataset consists of GeoTIFF files covering a latitude range from 57° S to 67° N in splits of 3° x 3° per file. Each file contains a single band corresponding to the binary output of the operation

fm = [(CH - CH_sd) > 5 m] & [(CC - CC_sd) > 10%]

corresponding to the widely accepted definition of forest by the UN Food and Agriculture Organization (FAO) [1]. We subtract one standard deviation from the source variables, as estimated by the model, in order to compensate for the slight over-estimation at low values of CH and CC resulting in a more conservative classification of forest. Further details are given in the publication mentioned above.

In addition, an alpha band is included indicating the valid pixels. Non-valid pixels are masked based on the following conditions:

1. is water or
2. is urban build up

We recommend applying this alpha mask in addition to the data mask.

The full dataset can also be retrieved from a public S3 bucket on AWS as a Requester-Pays service. Note that no transfer costs are incurred if downloading to an AWS resource within the same region (us-west-2). For further details on data transfer costs we refer to the AWS documentation. We encourage users to create their own AWS account (if not already existing) and transfer individual files within the same region by:

aws s3 cp s3://eda-appsci-open-access/forestmask/2023/earthdaily_forestmask_{lon}_{lat}-[forest, alpha].tif DESTINATION_PATH --request-payer requester

or the full dataset by:

aws s3 sync s3://eda-appsci-open-access/forestmask/2023/ DESTINATION_PATH --request-payer requester

A complete list of files avaialable in the S3 bucket is provided by filelist.txt.

[1] Food and Agriculture Organization. (2000, November 2). FRA 2000 on definitions of forest and forest change (FRA Working Paper No. 33). Forest Resources Assessment Programme. Rome. Retrieved from FAO website https://www.fao.org/4/ad665e/ad665e00.htm

This web map features the Sentinel-2 imagery layer with a focus on the Caribbean. The Sentinel-2 imagery layer is updated daily with new satellite imagery scenes that have been published to Sentinel-2 on AWS collections. Visit the Sentinel-2 imagery layer item for more details.

Here we provide a mosaic of the Copernicus DEM 30m for Europe and the corresponding hillshade derived from the GLO-30 public instance of the Copernicus DEM. The CRS is the same as the original Copernicus DEM CRS: EPSG:4326. Note that GLO-30 Public provides limited coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs.

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. The original GLO-30 provides worldwide coverage at 30 meters (refers to 10 arc seconds). Note that ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs. Note that the vertical unit for measurement of elevation height is meters.

The Copernicus DEM for Europe at 30 m in COG format has been derived from the Copernicus DEM GLO-30, mirrored on Open Data on AWS, dataset managed by Sinergise (https://registry.opendata.aws/copernicus-dem/).

Processing steps: The original Copernicus GLO-30 DEM contains a relevant percentage of tiles with non-square pixels. We created a mosaic map in https://gdal.org/drivers/raster/vrt.html format and defined within the VRT file the rule to apply cubic resampling while reading the data, i.e. importing them into GRASS GIS for further processing. We chose cubic instead of bilinear resampling since the height-width ratio of non-square pixels is up to 1:5. Hence, artefacts between adjacent tiles in rugged terrain could be minimized: gdalbuildvrt -input_file_list list_geotiffs_MOOD.csv -r cubic -tr 0.000277777777777778 0.000277777777777778 Copernicus_DSM_30m_MOOD.vrt

The pixel values were scaled with 1000 (storing the pixels as integer values) for data volume reduction. In addition, a hillshade raster map was derived from the resampled elevation map (using r.relief, GRASS GIS). Eventually, we exported the elevation and hillshade raster maps in Cloud Optimized GeoTIFF (COG) format, along with SLD and QML style files.

Surface reflectance is the fraction of incoming solar radiation that is reflected from Earth's surface. Variations in satellite measured radiance due to atmospheric properties have been corrected for, so images acquired over the same area at different times are comparable and can be used readily to detect changes on Earth’s surface. DE Africa provides Sentinel 2 Level-2A surface reflectance data from European Commission's Copernicus Programme. Sentinel-2 is an Earth observation mission that systematically acquires optical imagery at up to 10 m spatial resolution. The mission is based on a constellation of two identical satellites in the same orbit, 180° apart for optimal coverage and data delivery. Together, they cover all Earth's land surfaces, large islands, inland and coastal waters every 3-5 days. Each of the Sentinel-2 satellites carries a wide swath high-resolution multispectral imager with 13 spectral bands. This product has a temporal coverage of 2017 to current and is updated as new images are acquired. Images in different spectral bands are provided at spatial resolutions of 10, 20 or 60 m. The surface reflectance values are scaled to be between 0 and 10,000. Sentinel-2 Level-2A data are provided by the European Space Agency (ESA). Data prior to 2017 are processed from Level-1C to Level-2A with ESA's Sen2Cor software by Sinergise. All images are converted to Cloud Optimised GeoTIFF format by Element 84, Inc. For more information on the Sentinel-2 Level-2A surface reflectance product, see https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/level-2a/algorithm This product is accessible through OGC Web Service (https://ows.digitalearth.africa/), for analysis in DE Africa Sandbox JupyterLab (https://github.com/digitalearthafrica/deafrica-sandbox-notebooks/wiki) and for direct download from AWS S3 (https://data.digitalearth.africa/).

The State of Indiana Geographic Information Office (GIO) has published a State-wide Digital Aerial Imagery Catalog consisting of orthoimagery files from 2016-2019 in Cloud-Optimized GeoTIFF (COG) format on the AWS Registry of Open Data Account. These COG formatted files support the dynamic imagery services available from the GIO ESRI-based imagery solution. The Open Data on AWS is a repository of publicly available datasets for access from AWS resources. These datasets are owned and maintained by the Indiana GIO. These images are licensed by Creative Commons 0 (CC0). Cloud Optimized GeoTIF behaves as a GeoTIFF in all products; however, the optimization becomes apparent when incorporating them into web services.

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. This "leaf-on" imagery andtypically ranges from 30 centimeters to 100 centimeters in resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format, on naip-source Amazon S3 bucket as 4-band (RGB + NIR) in uncompressed Raw GeoTiff format and naip-visualization as 3-band (RGB) Cloud Optimized GeoTiff format. More details on NAIP

This web map features the Sentinel-2 imagery layer with a focus on Africa. The Sentinel-2 imagery layer is updated daily with new satellite imagery scenes that have been published to Sentinel-2 on AWS collections. Visit the Sentinel-2 imagery layer item for more details.

This map contains a number of world-wide dynamic image services providing access to various Landsat scenes covering the landmass of the World for visual interpretation. Landsat 8 collects new scenes for each location on Earth every 16 days, assuming limited cloud coverage. Newest and near cloud-free scenes are displayed by default on top. Most scenes collected since 1st January 2015 are included. The service also includes scenes from the Global Land Survey* (circa 2010, 2005, 2000, 1990, 1975).The service contains a range of different predefined renderers for Multispectral, Panchromatic as well as Pansharpened scenes. The layers in the service can be time-enabled so that the applications can restrict the displayed scenes to a specific date range. This ArcGIS Server dynamic service can be used in Web Maps and ArcGIS Desktop, Web and Mobile applications using the REST based image services API. Users can also export images, but the exported area is limited to maximum of 2,000 columns x 2,000 rows per request.Data Source: The imagery in these services is sourced from the U.S. Geological Survey (USGS) and the National Aeronautics and Space Administration (NASA). The data for these services reside on the Landsat Public Datasets hosted on the Amazon Web Service cloud. Users can access full scenes from https://github.com/landsat-pds/landsat_ingestor/wiki/Accessing-Landsat-on-AWS, or alternatively access http://landsatlook.usgs.gov to review and download full scenes from the complete USGS archive.For more information on Landsat 8 images, see http://landsat.usgs.gov/landsat8.php.*The Global Land Survey includes images from Landsat 1 through Landsat 7. Band numbers and band combinations differ from those of Landsat 8, but have been mapped to the most appropriate band as in the above table. For more information about the Global Land Survey, visit http://landsat.usgs.gov/science_GLS.php.For more information on each of the individual layers, see http://www.arcgis.com/home/item.html?id=d9b466d6a9e647ce8d1dd5fe12eb434b ; http://www.arcgis.com/home/item.html?id=6b003010cbe64d5d8fd3ce00332593bf ; http://www.arcgis.com/home/item.html?id=a7412d0c33be4de698ad981c8ba471e6

Global Lightning Imager Level 2 (GOES-East) 10 minutes aggregrated flash events. GOES satellites provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites hover continuously over one position on the surface. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric 'triggers' for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. Supplemental information Data originates from open data see: https://registry.opendata.aws/noaa-goes/ For more information on the data structure see: https://github.com/NOAA-Big-Data-Program/nodd-data-docs/tree/main/GOES

Index Tiles ONLY, for actual orthophotos see layer Gisborne 0.1m Rural Aerial Photos (2023-2024)

Orthophotography within the Gisborne region captured in the 2023-2024 flying season, co-captured with LiDAR.

Imagery was captured for the National Institute of Water and Atmospheric Research (NIWA) by Landpro between 1 Nov 2023 and 30 Jan 2024.

Data comprises: • 25189 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:1000 tile layout. • Tile layout in NZTM projection containing relevant information.

Imagery supplied as 10cm pixel resolution (0.1m GSD).

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