This registry exists to help people discover and share datasets that are available via AWS resources. See recent additions and learn more about sharing data on AWS.
See all usage examples for datasets listed in this registry tagged with aerial imagery.
You are currently viewing a subset of data tagged with aerial imagery.
If you want to add a dataset or example of how to use a dataset to this registry, please follow the instructions on the Registry of Open Data on AWS GitHub repository.
Unless specifically stated in the applicable dataset documentation, datasets available through the Registry of Open Data on AWS are not provided and maintained by AWS. Datasets are provided and maintained by a variety of third parties under a variety of licenses. Please check dataset licenses and related documentation to determine if a dataset may be used for your application.
If you have a project using a listed dataset, please tell us about it. We may work with you to feature your project in a blog post.
aerial imagerycoastalcomputer visiondisaster responseearth observationearthquakesgeospatialimage processingimaginginfrastructurelandmachine learningmappingnatural resourceseismologytransportationurbanwater
The Low Altitude Disaster Imagery (LADI) Dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2023. Two key distinctions are the low altitude, oblique perspective of the imagery and disaster-related features, which are rarely featured in computer vision benchmarks and datasets.
aerial imagerycogearth observationgeospatialsatellite imagerystac
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 Zea...
aerial imagerycogdisaster responseearth observationsatellite imagery
OpenAerialMap is a collection of high-resolution openly licensed satellite and aerial imagery.
aerial imageryearth observationelevationgeospatiallidar
The KyFromAbove initiative is focused on building and maintaining a current basemap for Kentucky that can meet the needs of its users at the state, federal, local, and regional level. A common basemap, including current color leaf-off aerial photography and elevation data (LiDAR), reduces the cost of developing GIS applications, promotes data sharing, and add efficiencies to many business processes. All basemap data acquired through this effort is being made available in the public domain. KyFromAbove acquires aerial imagery and LiDAR during leaf-off conditions in the Commonwealth. The imagery...
aerial imageryearth observationelevationgeospatialland coverlidar
The State of Vermont has partnered with Amazon's Open Data Initative to make a wide range of geospatial data available in the public domain. Vermont acquires aerial imagery and LiDAR during leaf-off conditions. The imagery typically ranges from 30-centimeter to 15-centimeter in resolution and is available from Vermont's Amazon S3 bucket in a Cloud Optimized GeoTiff (COG) format. LiDAR data has been acquired and is available as USGS Quality Level-1 (QL1) and Level-2 (QL2) compliant datasets in COG format. Geospatial datasets derived from imagery and/or lidar are also available as COGs, ...
aerial imageryagricultureclimatecogearth observationgeospatialimage processingland covermachine learningsatellite imagery
Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.
aerial imageryagriculturecogearth observationgeospatialnatural resourceregulatory
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
aerial imageryagriculturecogearth observationgeospatialimagingmappingnatural resourcesustainability
The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005. Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID. Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana. All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the resolution of the imagery. The naming of the tiles reflects the lower left coordinate from the...
aerial imageryclimatecogdisaster responseweather
In order to support NOAA's homeland security and emergency response requirements, the National Geodetic Survey Remote Sensing Division (NGS/RSD) has the capability to acquire and rapidly disseminate a variety of spatially-referenced datasets to federal, state, and local government agencies, as well as the general public. Remote sensing technologies used for these projects have included lidar, high-resolution digital cameras, a film-based RC-30 aerial camera system, and hyperspectral imagers. Examples of rapid response initiatives include acquiring high resolution images with the Emerge/App...
aerial imageryagriculturecomputer visiondeep learningmachine learning
Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 201...
aerial imagerycogconservationdeep learningearth observationenvironmentalgeospatialimage processingland cover
Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA).
aerial imagerydemographicsdisaster responsegeospatialimage processingmachine learningpopulationsatellite imagery
Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV and Cloud-optimized GeoTIFF files. This refines CIESIN’s Gridded Population of the World using machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census data are allocated to blocks where imagery appears to contain buildings.
aerial imageryagriculturecomputer visiondeep learningmachine learning
Dataset associated with the 2021 AAAI Paper- Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery. The dataset contains 3 image sequences of aerial imagery from 386 farm parcels which have been annotated for nutrient deficiency stress.
aerial imagerycogearth observationgeospatialimagingmapping
The New Jersey Office of GIS, NJ Office of Information Technology manages a series of 11 digital orthophotography and scanned aerial photo maps collected at various years ranging from 1930 to 2017. Each year’s worth of imagery are available as Cloud Optimized GeoTIFF (COG) files and some years are available as compressed MrSID and/or JP2 files. Additionally, each year of imagery is organized into a tile grid scheme covering the entire geography of New Jersey. Many years share the same tiling grid while others have unique grids as defined by the project at the time.