This REST Service provides cached satellite imagery for the City of Tempe. Imagery was flown in late 2022 and early 2023.

This dataset is part of the larger data collection, “Aerial imagery object identification dataset for building and road detection, and building height estimation”, linked to in the references below and can be accessed here: https://dx.doi.org/10.6084/m9.figshare.c.3290519. For a full description of the data, please see the metadata: https://dx.doi.org/10.6084/m9.figshare.3504413.

Imagery data from the United States Geological Survey (USGS); building and road shapefiles are from OpenStreetMaps (OSM) (these OSM data are made available under the Open Database License: http://opendatacommons.org/licenses/odbl/1.0/); and the Lidar data are from U.S. National Oceanic and Atmospheric Administration (NOAA), the Texas Natural Resources Information System (TNRIS).

This hosted tile layer provides aerial imagery for the City of Tempe. Imagery was taken in September 2023 and published April 2024.

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.

AID is a new large-scale aerial image dataset, by collecting sample images from Google Earth imagery. Note that although the Google Earth images are post-processed using RGB renderings from the original optical aerial images, it has proven that there is no significant difference between the Google Earth images with the real optical aerial images even in the pixel-level land use/cover mapping. Thus, the Google Earth images can also be used as aerial images for evaluating scene classification algorithms.

The new dataset is made up of the following 30 aerial scene types: airport, bare land, baseball field, beach, bridge, center, church, commercial, dense residential, desert, farmland, forest, industrial, meadow, medium residential, mountain, park, parking, playground, pond, port, railway station, resort, river, school, sparse residential, square, stadium, storage tanks and viaduct. All the images are labelled by the specialists in the field of remote sensing image interpretation, and some samples of each class are shown in Fig.1. In all, the AID dataset has a number of 10000 images within 30 classes.

The images in AID are actually multi-source, as Google Earth images are from different remote imaging sensors. This brings more challenges for scene classification than the single source images like UC-Merced dataset. Moreover, all the sample images per each class in AID are carefully chosen from different countries and regions around the world, mainly in China, the United States, England, France, Italy, Japan, Germany, etc., and they are extracted at different time and seasons under different imaging conditions, which increases the intra-class diversities of the data.

The dataset comprises aerial imagery of Dubai acquired by MBRSC satellites and annotated with pixel-level semantic segmentation across 6 distinct classes. The dataset comprises a total of 72 images, which are organised into 6 larger tiles. The categories are as follows: Credit: Humans in the Loop is releasing an openly accessible dataset that has been annotated for a collaborative project with the Mohammed Bin Rashid Space Centre in Dubai, United Arab Emirates. Deep Learning Projects for Final… See the full description on the dataset page: https://huggingface.co/datasets/gymprathap/Semantic-Segmentation-Aerial-Imagery-Dataset.

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.Maxar 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 Maxar 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. Maxar 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 Maxar 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.

The U.S. Geological Survey (USGS) Aerial Photography data set includes over 2.5 million film transparencies. Beginning in 1937, photographs were acquired for mapping purposes at different altitudes using various focal lengths and film types. The resultant black-and-white photographs contain less than 5 percent cloud cover and were acquired under rigid quality control and project specifications (e.g., stereo coverage, continuous area coverage of map or administrative units). Prior to the initiation of the National High Altitude Photography (NHAP) program in 1980, the USGS photography collection was one of the major sources of aerial photographs used for mapping the United States. Since 1980, the USGS has acquired photographs over project areas that require photographs at a larger scale than the photographs in the NHAP and National Aerial Photography Program collections.

The global satellite imagery market is experiencing robust growth, driven by increasing demand across diverse sectors. The market's expansion is fueled by several key factors. Firstly, advancements in sensor technology are leading to higher-resolution imagery with improved accuracy and detail, enhancing applications in various fields. Secondly, the decreasing cost of satellite launches and data processing is making satellite imagery more accessible and cost-effective for a wider range of users. Thirdly, the rise of cloud computing and sophisticated analytical tools facilitates efficient data storage, processing, and analysis, unlocking valuable insights from vast datasets. Finally, increasing government investments in space exploration and national security are boosting demand for high-quality satellite imagery. We estimate the market size in 2025 to be approximately $2.5 billion, considering average growth rates within the geospatial intelligence sector. The market is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of around 8% through 2033, driven by continued technological innovation and expanding applications in areas such as precision agriculture, urban planning, environmental monitoring, and disaster response. However, the market faces some restraints. Data security and privacy concerns surrounding the use of satellite imagery need to be addressed through robust regulatory frameworks and ethical guidelines. Competition among numerous established players and new entrants is also intense, putting pressure on pricing and margins. Furthermore, potential disruptions from weather events and technological failures can affect data acquisition and availability. Despite these challenges, the long-term outlook for the satellite imagery market remains positive, with significant potential for growth and innovation. The emergence of new technologies like smallsat constellations and AI-powered analytics is poised to further accelerate market expansion in the coming years. Key players like Maxar Technologies, Airbus, and Planet Labs are strategically positioning themselves to capitalize on these trends through technological advancements, strategic partnerships, and acquisitions.

This dataset contains remote sensing data for every village in the state of Bihar, India. For most of these villages, the data contains the corresponding electrification rate as reported by the Garv data platform from the Indian government as of July 2017. This dataset contains satellite imagery, political boundaries, lights at night imagery, rainfall measurements, and vegetation indices data for 45,220 villages and the electrification rate data for 32,817 of those villages. This dataset may be of particular interest to those investigating how electricity access maps to infrastructure and agricultural production. This dataset was compiled as part of the Summer 2017 Duke University Data+ team, titled "Electricity Access in Developing Countries from Aerial Imagery."

This is a link to the USGS Global Visualization Viewer which can be used to locate and download a variety of remotely sensed data including the ASTER multispectral data that was used in the Utah FORGE project.

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

This map features satellite imagery for the world and high-resolution aerial imagery for many areas. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Imagery map service description.

Effective land use management is crucial for balancing development against environmental sustainability, preservation of biodiversity, and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks to better inform policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region, spanning 25,000 km\(^2\), specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with three supplementary sets. Each image in the main dataset is annotated with 33 fine-grained, multi-labeled classes and approximate segmentation masks of the classes, and the three supplementary datasets cover spatially coincident satellite imagery and aerial imagery five years prior and five years later from the main dataset.

This is a link to the USGS Global Visualization Viewer which can be used to locate and download a variety of remotely sensed data including the ASTER multispectral data that was used in the Utah FORGE project.

The global satellite imagery and image processing services market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 7% from 2025 to 2033, reaching approximately $25 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are providing higher-resolution imagery with improved accuracy and faster processing times, enabling more detailed analysis for various applications. Secondly, the rising adoption of cloud-based platforms for image processing and analytics is streamlining workflows and reducing costs for users. This is particularly crucial for smaller businesses and organizations that previously lacked access to sophisticated image processing capabilities. Thirdly, the growing need for precise geographical information across diverse sectors, including environmental monitoring, precision agriculture, urban planning, and disaster response, fuels market demand. The defense and security sector remains a significant contributor, with increasing reliance on satellite imagery for intelligence gathering and surveillance. Market segmentation reveals significant opportunities within specific application areas. The environmental sector, utilizing satellite imagery for deforestation monitoring, climate change analysis, and pollution detection, is a rapidly growing segment. Similarly, the energy and power sector leverages satellite imagery for pipeline monitoring, renewable energy resource assessment, and infrastructure management. Within image processing types, the demand for advanced data analytics is soaring, with growing adoption of artificial intelligence and machine learning for automated feature extraction and predictive analysis. While regulatory hurdles and the high initial investment cost of satellite technologies pose some challenges, the overall market outlook remains positive, driven by technological advancements, increasing data accessibility, and rising demand for location-based intelligence. Competition is intensifying amongst established players and new entrants, leading to innovation and affordability in the market.

This two foot pixel resolution black and white aerial photography was flown on various dates in July and August 1939. They were scanned in 2001, and georeferenced in 2002. This data should NOT be used at a scale larger than 1 inch = 400 feet. Due to the lack of sufficient camera calibration information, errors will increase towards the margin of each underlying photo, although this effect has been minimized by cropping individual photos to make this mosaic. Since these photos were scanned from paper prints, local distortions (from the media stretching and/or shrinking) may be present as well as pen marks and fading. Caution should be used in interpreting features in this photography with reference to current conditions. In particular, many roads and road intersections have been realigned in the more than 60 years since this photography was taken. This historic aerial photography was captured in digital form as the result of a cooperative project between the Illinois State Geological Survey and the Geographic Information Systems (GIS) and Mapping Division of the Lake County Department of Information Technology. It is part of a statewide program to preserve the oldest known extensive aerial photography for future generations. The original photography was performed by the U.S. Department of Agriculture as part of a nation-wide program for use in agricultural assessment. Since the original negatives became unstable and were destroyed by the National Archives in the 1980s, only paper prints remain. A set of paper prints representing the best available quality was assembled from the collections of several agencies.

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 2023 - April 2024.

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 2023 - April 2024 • 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.

If you require the 12-bit imagery (R, G, B, NIR bands), send your request to imagery@linz.govt.nz

On February 24, 1995, President Clinton signed an Executive Order, directing the declassification of intelligence imagery acquired by the first generation of United States photo-reconnaissance satellites, including the systems code-named CORONA, ARGON, and LANYARD. More than 860,000 images of the Earth's surface, collected between 1960 and 1972, were declassified with the issuance of this Executive Order. Image collection was driven, in part, by the need to confirm purported developments in then-Soviet strategic missile capabilities. The images also were used to produce maps and charts for the Department of Defense and for other Federal Government mapping programs. In addition to the images, documents and reports (collateral information) are available, pertaining to frame ephemeris data, orbital ephemeris data, and mission performance. Document availability varies by mission; documentation was not produced for unsuccessful missions.

This dataset is a supplementary dataset to https://zenodo.org/records/11484867. The images in this dataset are spatially coincident, taken ~5 years after the referenced dataset. Below is the description of the referenced dataset

Effective land use management is crucial for balancing the development against environmental sustainability, preservation of biodiversity and resilience to climate change impacts. Despite this, there is a notable scarcity of comprehensive aerial imagery datasets for refining and improving machine learning frameworks for informed policy making. In this paper, we introduce a substantial aerial imagery dataset from New Zealand curated for the Waikato region spanning 25,000 km^2, specifically to address this gap and empower global research efforts. The dataset comprises of a main set, containing more than 140,000 images, with 3 supplementary sets. Each image is annotated with 33 fine-grained, multi-labelled classes and approximate segmentation masks of the classes, with 3 supplementary datasets covering spatially coincident satellite imagery and aerial imagery 5 years prior and 5 years later from the main dataset.