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 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.

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 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.

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.

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).

The State of Indiana Geographic Information Office (GIO) has published a State-wide Digital Aerial Imagery Catalog consisting of orthoimagery files from 2016-2019 and 2021 – 2022 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.

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 repository contains the global datasets of aboveground biomass, canopy height and cover accompanying the publication:

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 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 6 bands corresponding to the model outputs with the following order:

1. aboveground biomass density (Mg/ha), AGBD
2. canopy height (cm), CH
3. canopy cover (%), CC
4. standard error of AGBD
5. standard error of CH
6. standard error of CC

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

Due to the storage limit on Zenodo, we provide a sub-sample of 9 globally distributed files (48 GB) of the full dataset (5 TB) in this repository.

The files have the following naming convention: earthdaily_agbd_{lon}_{lat}-[data, alpha].tif
We provide the full list of files contained in this dataset in filelist.txt. The sub-sample of files contained in this repository are listed in filelist_sample.txt.

The full dataset can 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/biomass/earthdaily_agbd_{lon}_{lat}-[data, alpha].tif DESTINATION_PATH --request-payer requester

or the full dataset by:

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

For access without an AWS account, please contact the corresponding author (Manuel Weber).

Files in this repository:

• AGBD_CH_CC_sample_2023.zip; archive of 9 globally distributed sample GeoTIFFs
• filelist_sample.txt; S3 paths of the sample files
• filelist.txt; S3 paths of the full dataset

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.

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/).

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.

RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resolution dataset built to test the value of synthetic data from an overhead perspective. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie’s novel simulation platform and features 50,000 synthetic satellite images with ~630,000 aircraft annotations.

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

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

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

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

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

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

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

Available Data Sets:

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

Data Format

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

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

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

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

NOAA/NESDIS Global Mosaic of Geostationary Satellite Imagery (GMGSI) visible (VIS), shortwave infrared (SIR), longwave infrared (LIR) imagery, and water vapor imagery (WV) are composited from data from several geostationary satellites orbiting the globe, including the GOES-East and GOES-West Satellites operated by U.S. NOAA/NESDIS, the Meteosat-10 and Meteosat-9 satellites from theMeteosat Second Generation (MSG) series of satellites operated by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the Himawari-9 satellite operated by the Japan Meteorological Agency (JMA). GOES-East is positioned at 75 deg W longitude over the equator. GOES-West is located at 137.2 deg W longitude over the equator. Both satellites cover an area from the eastern Atlantic Ocean to the central Pacific Ocean region. The Meteosat-10 satellite is located at 0 deg E longitude to cover Europe and Africa regions. The Meteosat-9 satellite is located at 45.5 deg E longitude to cover the Indian Ocean region. The Himawari-9 satellite is located at 140.7 deg E longitude to cover the Asia-Oceania region. The visible imagery indicates cloud cover and ice and snow cover. The shortwave, or mid-infrared, indicates cloud cover and fog at night. The longwave, or thermal infrared, depicts cloud cover and land/sea temperature patterns. The water vapor imagery indicates the amount of water vapor contained in the mid to upper levels of the troposphere, with the darker grays indicating drier air and the brighter grays/whites indicating more saturated air. GMGSI composite images have an approximate 8 km (5 mile) horizontal resolution and are updated every hour.

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

The State of Indiana Geographic Information Office (GIO) has published a State-wide Digital Aerial Imagery Catalog consisting of orthoimagery files from 2016-2019 and 2021 – 2022 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.

Abstract: During 2001-2006, 6 giant icebergs (B15A, B15J, B15K, C16 and C25) adrift in the southwestern Ross Sea, Antarctica, were instrumented with global positioning system (GPS) receivers, magnetic compasses and automatic weather stations (AWS), to monitor their behavior in the near-coastal environment and to record their exit into the Southern Ocean. The GPS and AWS data were collected on a 20-minute interval, Many of the station data timeseries are continuous for periods of up to 7 years, with icebergs C16 and B15J having the longest records.

The data is considered useful for examining the processes of iceberg drift (and other behaviors) on time scales that are shorter than what is possible through satellite image iceberg tracking. Data are available in comma-delimited ASCII format and Matlab native mat files.

Orthophotography within the Waikato Region captured in the summer flying seasons between 2020 and 2024. The area of capture is located within the upper North Island and encompasses all or part of 11 territorial authorities. It also includes parts of Bay of Plenty, Hawke's Bay and Manawatū-Whanganui.

Imagery was captured for Waikato Regional Aerial Photography Service (WRAPS) 2021 by Aerial Surveys Ltd.

Data comprises: • 3733 ortho-rectified RGB GeoTIFF images in NZTM projection, tiled into the LINZ Standard 1:5000 tile layout • Tile layout in NZTM projection containing relevant information.

The supplied imagery is in terms of New Zealand Transverse Mercator (NZTM) map projection. Please refer to the tile index layer for specific details, naming conventions, etc.

Imagery supplied as 30cm pixel resolution (0.3m GSD), 3-band (RGB) uncompressed GeoTIFF. The final spatial accuracy is ±0.5 at 95% confidence level in clear flat areas.

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

Index tiles for this dataset are available as layer Waikato 0.3m Rural Aerial Photos Index Tiles (2021-2023)