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.

1) Data Introduction • The Satellite Image Classification Dataset is a benchmark image classification dataset constructed using satellite remote sensing imagery. It includes a total of four land surface classes—cloudy, desert, green_area, and water—collected from various sensor-based images and Google Maps snapshots. The dataset is designed for training and evaluating image-based scene recognition models.

2) Data Utilization (1) Characteristics of the Satellite Image Classification Dataset: • The dataset was collected with the aim of automatic interpretation of satellite imagery and consists of a combination of sensor-based images and map snapshots, offering a realistic representation of real-world conditions. • All images are of fixed resolution and include diverse landform features, making the dataset suitable for classification experiments across different environments and for evaluating model generalization performance.

(2) Applications of the Satellite Image Classification Dataset: • Land surface classification model training: Can be used in experiments to classify various types of terrain such as buildings, farmland, and roads. • Research and application in geospatial information analysis: Useful for developing models that support spatial decision-making through tasks such as land use monitoring, urban structure analysis, and land surface inference.

This application is intended for informational purposes only and is not an operational product. The tool provides the capability to access, view and interact with satellite imagery, and shows the latest view of Earth as it appears from space.For additional imagery from NOAA's GOES East and GOES West satellites, please visit our Imagery and Data page or our cooperative institute partners at CIRA and CIMSS.This website should not be used to support operational observation, forecasting, emergency, or disaster mitigation operations, either public or private. In addition, we do not provide weather forecasts on this site — that is the mission of the National Weather Service. Please contact them for any forecast questions or issues. Using the Maps​What does the Layering Options icon mean?The Layering Options widget provides a list of operational layers and their symbols, and allows you to turn individual layers on and off. The order in which layers appear in this widget corresponds to the layer order in the map. The top layer ‘checked’ will indicate what you are viewing in the map, and you may be unable to view the layers below.Layers with expansion arrows indicate that they contain sublayers or subtypes.Do these maps work on mobile devices and different browsers?Yes!Why are there black stripes / missing data on the map?NOAA Satellite Maps is for informational purposes only and is not an operational product; there are times when data is not available.Why are the North and South Poles dark?The raw satellite data used in these web map apps goes through several processing steps after it has been acquired from space. These steps translate the raw data into geospatial data and imagery projected onto a map. NOAA Satellite Maps uses the Mercator projection to portray the Earth's 3D surface in two dimensions. This Mercator projection does not include data at 80 degrees north and south latitude due to distortion, which is why the poles appear black in these maps. NOAA's polar satellites are a critical resource in acquiring operational data at the poles of the Earth and some of this imagery is available on our website (for example, here ).Why does the imagery load slowly?This map viewer does not load pre-generated web-ready graphics and animations like many satellite imagery apps you may be used to seeing. Instead, it downloads geospatial data from our data servers through a Map Service, and the app in your browser renders the imagery in real-time. Each pixel needs to be rendered and geolocated on the web map for it to load.How can I get the raw data and download the GIS World File for the images I choose?NOAA Satellite Maps offers an interoperable map service to the public. Use the camera tool to select the area of the map you would like to capture and click ‘download GIS WorldFile.’The geospatial data Map Service for the NOAA Satellite Maps GOES satellite imagery is located on our Satellite Maps ArcGIS REST Web Service ( available here ).We support open information sharing and integration through this RESTful Service, which can be used by a multitude of GIS software packages and web map applications (both open and licensed).Data is for display purposes only, and should not be used operationally.Are there any restrictions on using this imagery?NOAA supports an open data policy and we encourage publication of imagery from NOAA Satellite Maps; when doing so, please cite it as "NOAA" and also consider including a permalink (such as this one) to allow others to explore the imagery.For acknowledgment in scientific journals, please use:We acknowledge the use of imagery from the NOAA Satellite Maps application: LINKThis imagery is not copyrighted. You may use this material for educational or informational purposes, including photo collections, textbooks, public exhibits, computer graphical simulations and internet web pages. This general permission extends to personal web pages. About this satellite imageryWhat am I looking at in these maps?What am I seeing in the NOAA Satellite Maps 3D Scene?There are four options to choose from, each depicting a different view of the Earth using the latest satellite imagery available. The first three views show the Western Hemisphere and the Pacific Ocean, as captured by the NOAA GOES East (GOES-16) and GOES West (GOES-17) satellites. These images are updated approximately every 15 minutes as we receive data from the satellites in space. The three views show GeoColor, infrared and water vapor. See our other FAQs to learn more about what the imagery layering options depict.The fourth option is a global view, captured by NOAA’s polar-orbiting satellites (NOAA/NASA Suomi NPP and NOAA-20). The polar satellites circle the globe 14 times a day, taking in one complete view of the Earth in daylight every 24 hours. This composite view is what is projected onto the 3D map scene each morning, so you are seeing how the Earth looked from space one day ago.What am I seeing in the Latest 24 Hrs. GOES Constellation Map?In this map you are seeing the past 24 hours (updated approximately every 15 minutes) of the Western Hemisphere and Pacific Ocean, as seen by the NOAA GOES East (GOES-16) and GOES West (GOES-17) satellites. In this map you can also view three different ‘layers’. The three views show ‘GeoColor’ ‘infrared’ and ‘water vapor’.(Please note: GOES West imagery is currently only available in GeoColor. The infrared and water vapor imagery will be available in Spring 2019.)This maps shows the coverage area of the GOES East and GOES West satellites. GOES East, which orbits the Earth from 75.2 degrees west longitude, provides a continuous view of the Western Hemisphere, from the West Coast of Africa to North and South America. GOES West, which orbits the Earth at 137.2 degrees west longitude, sees western North and South America and the central and eastern Pacific Ocean all the way to New Zealand.What am I seeing in the Global Archive Map?In this map, you will see the whole Earth as captured each day by our polar satellites, based on our multi-year archive of data. This data is provided by NOAA’s polar orbiting satellites (NOAA/NASA Suomi NPP from January 2014 to April 19, 2018 and NOAA-20 from April 20, 2018 to today). The polar satellites circle the globe 14 times a day taking in one complete view of the Earth every 24 hours. This complete view is what is projected onto the flat map scene each morning.What does the GOES GeoColor imagery show?The 'Merged GeoColor’ map shows the coverage area of the GOES East and GOES West satellites and includes the entire Western Hemisphere and most of the Pacific Ocean. This imagery uses a combination of visible and infrared channels and is updated approximately every 15 minutes in real time. GeoColor imagery approximates how the human eye would see Earth from space during daylight hours, and is created by combining several of the spectral channels from the Advanced Baseline Imager (ABI) – the primary instrument on the GOES satellites. The wavelengths of reflected sunlight from the red and blue portions of the spectrum are merged with a simulated green wavelength component, creating RGB (red-green-blue) imagery. At night, infrared imagery shows high clouds as white and low clouds and fog as light blue. The static city lights background basemap is derived from a single composite image from the Visible Infrared Imaging Radiometer Suite (VIIRS) Day Night Band. For example, temporary power outages will not be visible. Learn more.What does the GOES infrared map show?The 'GOES infrared' map displays heat radiating off of clouds and the surface of the Earth and is updated every 15 minutes in near real time. Higher clouds colorized in orange often correspond to more active weather systems. This infrared band is one of 12 channels on the Advanced Baseline Imager, the primary instrument on both the GOES East and West satellites. on the GOES the multiple GOES East ABI sensor’s infrared bands, and is updated every 15 minutes in real time. Infrared satellite imagery can be "colorized" or "color-enhanced" to bring out details in cloud patterns. These color enhancements are useful to meteorologists because they signify “brightness temperatures,” which are approximately the temperature of the radiating body, whether it be a cloud or the Earth’s surface. In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are usually “clear sky,” while pale white areas typically indicate low-level clouds. During a hurricane, cloud top temperatures will be higher (and colder), and therefore appear dark red. This imagery is derived from band #13 on the GOES East and GOES West Advanced Baseline Imager.How does infrared satellite imagery work?The infrared (IR) band detects radiation that is emitted by the Earth’s surface, atmosphere and clouds, in the “infrared window” portion of the spectrum. The radiation has a wavelength near 10.3 micrometers, and the term “window” means that it passes through the atmosphere with relatively little absorption by gases such as water vapor. It is useful for estimating the emitting temperature of the Earth’s surface and cloud tops. A major advantage of the IR band is that it can sense energy at night, so this imagery is available 24 hours a day.What do the colors on the infrared map represent?In this imagery, yellow and orange areas signify taller/colder clouds, which often correlate with more active weather systems. Blue areas are clear sky, while pale white areas indicate low-level clouds, or potentially frozen surfaces. Learn more about this weather imagery.What does the GOES water vapor map layer show?The GOES ‘water vapor’ map displays the concentration and location of clouds and water vapor in the atmosphere and shows data from both the GOES East and GOES West satellites. Imagery is updated approximately every 15 minutes in

What is this dataset?

Nearly 10,000 km² of free high-resolution and matched low-resolution satellite imagery of unique locations which ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities.

Those locations are also enriched with typically under-represented locations in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk.

Each high-resolution image (1.5 m/pixel) comes with multiple temporally-matched low-resolution images from the freely accessible lower-resolution Sentinel-2 satellites (10 m/pixel).

We accompany this dataset with a paper, datasheet for datasets and an open-source Python package to: rebuild or extend the WorldStrat dataset, train and infer baseline algorithms, and learn with abundant tutorials, all compatible with the popular EO-learn toolbox.

Why make this?

We hope to foster broad-spectrum applications of ML to satellite imagery, and possibly develop the same power of analysis allowed by costly private high-resolution imagery from free public low-resolution Sentinel2 imagery. We illustrate this specific point by training and releasing several highly compute-efficient baselines on the task of Multi-Frame Super-Resolution.

Licences

• The high-resolution Airbus imagery is distributed, with authorization from Airbus, under Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0).
• The labels, Sentinel2 imagery, and trained weights are released under Creative Commons with Attribution 4.0 International (CC BY 4.0).
• The source code (will be shortly released on GitHub) under 3-Clause BSD license.

Lebanon has traditionally been a major potato producer with 451,860 tons produced in 2014. Generally, potatoes make up 30% of the total Lebanese agricultural exports where approximately 60% of the potato production is exported to the Arab region, the UK and Brazil. The purpose of this study is to promote precision agriculture techniques in Lebanon that will help local farmers in the central Bekaa Valley with land management decisions. The European Space Agency’s satellite missions Sentinel-2A, launched June 23rd 2015, and the Sentinel-2B, recently launched on March 7th 2017, are multispectral high resolution imaging systems that provide global coverage every 5 days. The Sentinel program is a land monitoring program that includes an aim to improve agricultural practices. The imagery is 13 band data in the visible, near infrared and short wave infrared parts of the electromagnetic spectrum and ranges from 10-20 m including three 60 m bands pixel resolution. Sentinel is freely available data that has the potential to empower farmers with information to respond quickly to maximize crop health. Due to the political and security conflicts in the region, utilizing satellite imagery for Lebanon is more reasonable and realistic than operating Unmanned Aircraft Systems (UAS) for high resolution remote sensing. During the 2017 growing season, local farmers provided detailed information in designated fields on their farming practices, crop health, and pest threats. In parallel, Sentinel-2 imagery was processed to study crop health using the following vegetation indices: Normalized Difference Vegetation Index, Green Normalized Difference Vegetation Index, Soil Adjusted Vegetation Index and Modified Soil Adjusted Vegetation Index 2. Most Lebanese farmers inherit their land from their parents over generations, and as a result most still use traditional farming techniques for irrigation, where decisions are based on prior generations’ practices. However, with the changes in climate conditions within the region, these practices are no longer as efficient as they used to be. Normalized Difference Water Indices are calculated from satellite bands in the near-infrared and short-wave infrared to provide a better understanding about the water stress status of crops within the field. Preliminary results demonstrate that Sentinel-2 data can provide detailed and timely data for farmers to effectively manage fields. Despite the fact that most Lebanese farmers rely on traditional farming methods, providing them with crop health information on their mobile phones and allowing them to test its efficiency has the potential to be a catalyst to help them improve their farming practices.

The India Satellite Imagery Services Market report segments the industry into Application (Geospatial Data Acquisition and Mapping, Natural Resource Management, Surveillance and Security, Conservation and Research, Disaster Management, Intelligence) and End-User (Government, Construction, Transportation and Logistics, Military and Defense, Forestry and Agriculture, Others).

Satellite Remote Sensing Image Market Outlook

In 2023, the global satellite remote sensing image market size was valued at approximately USD 3.5 billion and is projected to reach around USD 7.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 9.1% during the forecast period. The market's robust growth can be attributed to an increasing demand for high-resolution satellite imagery, advancements in satellite technology, and a growing number of applications across various industries.

The primary growth factor for the satellite remote sensing image market is the increasing utilization of satellite imagery in diverse fields such as agriculture, environmental monitoring, and disaster management. Farmers and agricultural professionals are increasingly reliant on satellite images to monitor crop health, forecast yields, and manage resources more effectively. The advanced capabilities of remote sensing technology allow for precise monitoring of vegetation, soil moisture, and crop conditions, thereby aiding in precision agriculture. This trend not only helps in maximizing agricultural output but also in making farming more sustainable by optimizing the use of water and fertilizers.

Environmental monitoring is another significant area driving the market growth. Governments and organizations worldwide are leveraging satellite remote sensing to track environmental changes and address climate change issues. By monitoring deforestation, glacier retreat, sea-level rise, and other ecological metrics, stakeholders can make informed decisions to mitigate adverse impacts. The precision and comprehensive coverage offered by satellite imagery make it indispensable for long-term environmental monitoring and conservation efforts.

Disaster management is a critical application where satellite remote sensing images play a pivotal role. Natural disasters such as hurricanes, earthquakes, floods, and wildfires can be swiftly assessed and managed using high-resolution satellite images. These images provide real-time data that enable authorities to allocate resources efficiently, plan evacuations, and assess damage. The ability to quickly and accurately assess disaster impacts helps in reducing response times and improving the effectiveness of relief operations.

The integration of Atmospheric Satellite technology into the field of remote sensing is poised to revolutionize the way we gather data about the Earth's atmosphere. Atmospheric Satellites, often referred to as 'atmosats', are designed to operate at altitudes higher than traditional aircraft but lower than conventional satellites. This unique positioning allows them to capture detailed atmospheric data, which is crucial for understanding weather patterns, climate change, and environmental phenomena. The ability of atmosats to remain in the same position relative to the Earth's surface provides continuous monitoring capabilities, making them invaluable for applications such as real-time weather forecasting, air quality monitoring, and disaster response. As the demand for precise atmospheric data grows, the role of Atmospheric Satellites in enhancing the accuracy and reliability of satellite remote sensing images becomes increasingly significant.

Regionally, North America and Europe are leading the market due to their advanced satellite technologies and high adoption rates across various industries. The presence of key market players, coupled with extensive investments in space programs, propels the market in these regions. North America, in particular, benefits from increased funding for space exploration and defense applications, while Europe focuses on environmental monitoring and urban planning. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid economic development, expanding agricultural activities, and growing investments in space technology.

Technology Analysis

The technology segment of the satellite remote sensing image market is categorized into Optical, Radar, and Hyperspectral imaging technologies. Each of these technologies offers unique advantages and applications, driving their adoption across various sectors. Optical imaging, which involves capturing images using visible light, is widely used for its high-resolution capabilities and cost-effectiveness. This technology is particularly beneficial for applications requiring detailed visual information, such as urban planning, construction monitoring, and

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

For the purposes of training AI-based models to identify (map) road features in rural/remote tropical regions on the basis of true-colour satellite imagery, and subsequently testing the accuracy of these AI-derived road maps, we produced a dataset of 8904 satellite image ‘tiles’ and their corresponding known road features across Equatorial Asia (Indonesia, Malaysia, Papua New Guinea). Methods

1. INPUT 200 SATELLITE IMAGES

The main dataset shared here was derived from a set of 200 input satellite images, also provided here. These 200 images are effectively ‘screenshots’ (i.e., reduced-resolution copies) of high-resolution true-colour satellite imagery (~0.5-1m pixel resolution) observed using the Elvis Elevation and Depth spatial data portal (https://elevation.fsdf.org.au/), which here is functionally equivalent to the more familiar Google Earth. Each of these original images was initially acquired at a resolution of 1920x886 pixels. Actual image resolution was coarser than the native high-resolution imagery. Visual inspection of these 200 images suggests a pixel resolution of ~5 meters, given the number of pixels required to span features of familiar scale, such as roads and roofs, as well as the ready discrimination of specific land uses, vegetation types, etc. These 200 images generally spanned either forest-agricultural mosaics or intact forest landscapes with limited human intervention. Sloan et al. (2023) present a map indicating the various areas of Equatorial Asia from which these images were sourced.
IMAGE NAMING CONVENTION A common naming convention applies to satellite images’ file names: XX##.png where:

XX – denotes the geographical region / major island of Equatorial Asia of the image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

1. INTERPRETING ROAD FEATURES IN THE IMAGES For each of the 200 input satellite images, its road was visually interpreted and manually digitized to create a reference image dataset by which to train, validate, and test AI road-mapping models, as detailed in Sloan et al. (2023). The reference dataset of road features was digitized using the ‘pen tool’ in Adobe Photoshop. The pen’s ‘width’ was held constant over varying scales of observation (i.e., image ‘zoom’) during digitization. Consequently, at relatively small scales at least, digitized road features likely incorporate vegetation immediately bordering roads. The resultant binary (Road / Not Road) reference images were saved as PNG images with the same image dimensions as the original 200 images.

2. IMAGE TILES AND REFERENCE DATA FOR MODEL DEVELOPMENT

The 200 satellite images and the corresponding 200 road-reference images were both subdivided (aka ‘sliced’) into thousands of smaller image ‘tiles’ of 256x256 pixels each. Subsequent to image subdivision, subdivided images were also rotated by 90, 180, or 270 degrees to create additional, complementary image tiles for model development. In total, 8904 image tiles resulted from image subdivision and rotation. These 8904 image tiles are the main data of interest disseminated here. Each image tile entails the true-colour satellite image (256x256 pixels) and a corresponding binary road reference image (Road / Not Road).
Of these 8904 image tiles, Sloan et al. (2023) randomly selected 80% for model training (during which a model ‘learns’ to recognize road features in the input imagery), 10% for model validation (during which model parameters are iteratively refined), and 10% for final model testing (during which the final accuracy of the output road map is assessed). Here we present these data in two folders accordingly:

'Training’ – contains 7124 image tiles used for model training in Sloan et al. (2023), i.e., 80% of the original pool of 8904 image tiles. ‘Testing’– contains 1780 image tiles used for model validation and model testing in Sloan et al. (2023), i.e., 20% of the original pool of 8904 image tiles, being the combined set of image tiles for model validation and testing in Sloan et al. (2023).

IMAGE TILE NAMING CONVENTION A common naming convention applies to image tiles’ directories and file names, in both the ‘training’ and ‘testing’ folders: XX##_A_B_C_DrotDDD where

XX – denotes the geographical region / major island of Equatorial Asia of the original input 1920x886 pixel image, as follows: ‘bo’ (Borneo), ‘su’ (Sumatra), ‘sl’ (Sulawesi), ‘pn’ (Papua New Guinea), ‘jv’ (java), ‘ng’ (New Guinea [i.e., Papua and West Papua provinces of Indonesia])

– denotes the ith image for a given geographical region / major island amongst the original 200 images, e.g., bo1, bo2, bo3…

A, B, C and D – can all be ignored. These values, which are one of 0, 256, 512, 768, 1024, 1280, 1536, and 1792, are effectively ‘pixel coordinates’ in the corresponding original 1920x886-pixel input image. They were recorded within the names of image tiles’ sub-directories and file names merely to ensure that names/directory were uniquely named)

rot – implies an image rotation. Not all image tiles are rotated, so ‘rot’ will appear only occasionally.

DDD – denotes the degree of image-tile rotation, e.g., 90, 180, 270. Not all image tiles are rotated, so ‘DD’ will appear only occasionally.

Note that the designator ‘XX##’ is directly equivalent to the filenames of the corresponding 1920x886-pixel input satellite images, detailed above. Therefore, each image tiles can be ‘matched’ with its parent full-scale satellite image. For example, in the ‘training’ folder, the subdirectory ‘Bo12_0_0_256_256’ indicates that its image tile therein (also named ‘Bo12_0_0_256_256’) would have been sourced from the full-scale image ‘Bo12.png’.

The first generation of U.S. photo intelligence satellites collected more than 860,000 images of the Earth’s surface between 1960 and 1972. The classified military satellite systems code-named CORONA, ARGON, and LANYARD acquired photographic images from space and returned the film to Earth for processing and analysis. The images were originally used for reconnaissance and to produce maps for U.S. intelligence agencies. In 1992, an Environmental Task Force evaluated the application of early satellite data for environmental studies. Since the CORONA, ARGON, and LANYARD data were no longer critical to national security and could be of historical value for global change research, the images were declassified by Executive Order 12951 in 1995. The first successful CORONA mission was launched from Vandenberg Air Force Base in 1960. The satellite acquired photographs with a telescopic camera system and loaded the exposed film into 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 intelligence community used Keyhole (KH) designators to describe system characteristics and accomplishments. The CORONA systems were designated KH-1, KH-2, KH-3, KH-4, KH-4A, and KH-4B. The ARGON systems used the designator KH-5 and the LANYARD systems used KH-6. Mission numbers were a means for indexing the imagery and associated collateral data. A variety of camera systems were used with the satellites. Early systems (KH-1, KH-2, KH-3, and KH-6) carried a single panoramic camera or a single frame camera (KH-5). The later systems (KH-4, KH-4A, and KH-4B) carried two panoramic cameras with a separation angle of 30° with one camera looking forward and the other looking aft. The original film and technical mission-related documents 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. Mathematical calculations based on camera operation and satellite path were used to approximate image coordinates. Since the accuracy of the coordinates varies according to the precision of information used for the derivation, users should inspect the preview image to verify that the area of interest is contained in the selected frame. Users should also note that the images have not been georeferenced.

Satellite Image Classification

## Overview

Satellite Image Classification is a dataset for object detection tasks - it contains Tanks Vehicles Tents annotations for 101 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [Public Domain license](https://creativecommons.org/licenses/Public Domain).

The size of the Japan Satellite Imagery Services market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 15.25% during the forecast period.Satellite imagery services include image capturing, processing, and analysis of the Earth's surface from satellites orbiting the earth. Such images give valuable information on Earth; these include land use, urban development, environmental monitoring, disaster response, and agriculture.Satellite imagery service thus is of great importance to Japan. The country has a history of space exploration since the early years. She has launched many satellites used for Earth observation. Japanese satellite imagery services are used in all industries. In agriculture, it helps in crop health monitoring, yield potential assessment, and optimization of irrigation. In urban planning, it helps in the development of cities, infrastructure planning, and in disaster management. Environmental monitoring applications include tracking deforestation, monitoring pollution, and assessing impacts from climate change. By providing quick damage assessments and otherwise aiding relief operations, satellite imagery is also important in disaster response. High-resolution satellite imagery and high-end analysis tools will be more in demand in the years ahead with increasing technology advancement. Therefore, the Japanese market for satellite imagery services has a long way ahead. Recent developments include: January 2023: Axelspace announced that the company signed an agreement with New Space Intelligence which is a Japanese satellite imagery analysis service provider company. With this partnership, both companies will work together to promote the expansion of satellite data utilization by developing new applications using satellite imagery., November 2022: Japan Space Imaging Corporation signed an agreement with Satellite Vu in order to launch a unique constellation of satellites to deliver the highest resolution thermal data from space. The company will provide its customer and partners with preferred access to Satellite Vu's imagery, products, and services.. Key drivers for this market are: Infrastructural Development in Japan, Increasing Requirement for Mapping and Navigation System. Potential restraints include: Regulatory and Legal Challenges. Notable trends are: Infrastructural Development in Japan.

The purpose of the SNF Study was to develop the techniques to make the link from biophysical measurements made on the ground to aircraft radiometric measurements and then to scale up to satellite observations. Therefore, satellite image data were acquired for the Superior National Forest study site. These data were selected from all the scenes available from Landsat 1 through 5 and SPOT platforms. Image data substantially contaminated by cloud cover or of poor radiometric quality was not acquired. Of the Landsat scenes, only one Thematic Mapper (TM) scene was acquired, the remainder were Multispectral Scanner (MSS) images. Some of the acquired image data had cloud cover in portions of the scene or other problems with the data. These problems and other comments about the images are summarized in the data set. This data set contains a listing of the scenes that passed inspection and were acquired and archived by Goddard Space Flight Center. Though these image data are no longer available from either the Goddard Space Flight Center or the ORNL DAAC, this data set has been included in the Superior National Forest data collection in order to document which satellite images were used during the project.

Commercial Satellite Imagery Market size was valued at USD 2.76 Billion in 2024 and is projected to reach USD 6.34 Billion by 2032, growing at a CAGR of 12.07% during the forecast period 2026-2032.Commercial Satellite Imagery Market: Definition/ OverviewThe commercial satellite imagery business captures photographs of Earth from orbit using private company-operated satellites. This data, also known as Earth observation photography, is subsequently evaluated and sold for a variety of commercial applications. Commercial satellites' capabilities are constantly being improved as technology advances. New sensors may gather data at higher resolutions, resulting in extremely detailed pictures of the Earth. Furthermore, these sensors may collect numerous data sets simultaneously, providing a more complete picture of a single region. This rich data enables businesses to monitor environmental changes, track infrastructure development, and even examine agricultural health.Commercial satellite imagery is increasingly being used for purposes other than mapping and urban planning. Agriculture and other industries use satellite data to enhance agricultural production and detect disease outbreaks. Real-time images can aid in disaster response by assessing damage and coordinating relief efforts. Even the insurance business uses satellite data to assess risk and manage claims. This widening spectrum of applications creates a wider and more diverse customer base for commercial satellite imagery businesses.Looking ahead, the commercial satellite imagery business looks promising. As constellation missions like SpaceX's Starlink send more satellites into orbit, data collecting will grow more frequent and detailed. Artificial intelligence advancements will improve the process of evaluating this data, resulting in important insights for a broader range of sectors. This confluence of circumstances predicts that the commercial satellite imaging market will continue to grow significantly in the coming years.

Ground-based readings of temperature and rainfall, satellite imagery, aerial photographs, ground verification data and Digital Elevation Model (DEM) were used in this study. Ground-based meteorological information was obtained from Bangladesh Meteorological Department (BMD) for the period 1977 to 2015 and was used to determine the trends of rainfall and temperature in this thesis. Satellite images obtained from the US Geological Survey (USGS) Center for Earth Resources Observation and Science (EROS) website (www.glovis.usgs.gov) in four time periods were analysed to assess the dynamics of mangrove population at species level. Remote sensing techniques, as a solution to lack of spatial data at a relevant scale and difficulty in accessing the mangroves for field survey and also as an alternative to the traditional methods were used in monitoring of the changes in mangrove species composition, . To identify mangrove forests, a number of satellite sensors have been used, including Landsat TM/ETM/OLI, SPOT, CBERS, SIR, ASTER, and IKONOS and Quick Bird. The use of conventional medium-resolution remote sensor data (e.g., Landsat TM, ASTER, SPOT) in the identification of different mangrove species remains a challenging task. In many developing countries, the high cost of acquiring high- resolution satellite imagery excludes its routine use. The free availability of archived images enables the development of useful techniques in its use and therefor Landsat imagery were used in this study for mangrove species classification. Satellite imagery used in this study includes: Landsat Multispectral Scanner (MSS) of 57 m resolution acquired on 1st February 1977, Landsat Thematic Mapper (TM) of 28.5 m resolution acquired on 5th February 1989, Landsat Enhanced Thematic Mapper (ETM+) of 28.5 m resolution acquired on 28th February 2000 and Landsat Operational Land Imager (OLI) of 30 m resolution acquired on 4th February 2015. To study tidal channel dynamics of the study area, aerial photographs from 1974 and 2011, and a satellite image from 2017 were used. Satellite images from 1974 with good spatial resolution of the area were not available, and therefore aerial photographs of comparatively high and fine resolution were considered adequate to obtain information on tidal channel dynamics. Although high-resolution satellite imagery was available for 2011, aerial photographs were used for this study due to their effectiveness in terms of cost and also ease of comparison with the 1974 photographs. The aerial photographs were sourced from the Survey of Bangladesh (SOB). The Sentinel-2 satellite image from 2017 was downloaded from the European Space Agency (ESA) website (https://scihub.copernicus.eu/). In this research, elevation data acts as the main parameter in the determination of the sea level rise (SLR) impacts on the spatial distribution of the future mangrove species of the Bangladesh Sundarbans. High resolution elevation data is essential for this kind of research where every centimeter counts due to the low-lying characteristics of the study area. The high resolution (less than 1m vertical error) DEM data used in this study was obtained from Water Resources Planning Organization (WRPO), Bangladesh. The elevation information used to construct the DEM was originally collected by a Finnish consulting firm known as FINNMAP in 1991 for the Bangladesh government.

The size of the India Satellite Imagery Services market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 17.43% during the forecast period.The satellite image services primarily include acquisition, processing, analysis, and interpretation to extract useful information. This high-resolution information obtained and captured from Earth-orbiting satellites indicates aspects pertaining to land use and development in urban areas, agriculture, natural resources, and climate change.Indian satellite image services are achieving an exponential growth rate as they meet the increasing demand of various sectors. These sat data are increasingly being used by governments for urban planning, disaster management, and border surveillance. Agriculture uses satellite data to monitor crop growth, estimate yields, and carry out precision farming, while resource exploration and environmental impact assessments are common applications of satellite imagery in the mining and energy sectors. Telecommunications and the GIS industries depend on satellite imagery to plan networks and map areas.The growth of the Indian market is due to the focus of the Indian government on space technology and its initiatives to encourage the use of satellite data. There is vast potential and promising applications of satellite imagery services in the country of India, as there has been a rising advancement in technology along with sophistication of techniques in data analysis. Recent developments include: January 2023: The Indian Space Research Organization's National Remote Sensing Center released satellite images of Joshimath, a town in Uttarakhand that is slowly sinking due to land subsidence, and the images show that a rapid subsidence of 5.4 cm was observed in a span of twelve days between December last week and January first week., June 2022: Pataa Navigations, an India-based software firm, and Indian National Space Promotion and Authorisation Centre (IN-SPACe) signed an MoU to enable access to ISRO's Geospatial Services and APIs for the creation of an addressing system during the opening of the In-Space headquarters. The company would launch an addressing revolution in India by providing access to satellite image-based digital addresses. Through this MoU, the partnership would be for the ISRO portals Bhuvan, VEDAS, and MOSDAC services.. Key drivers for this market are: Government Initiatives to Foster the Growth of Satellite Imagery Services in India, Increasing Importance on Disaster Management and Mitigation Efforts. Potential restraints include: Affordability and Accessibility might restrain the Market Growth, Limited Standardization and Interoperability. Notable trends are: Government Initiatives to Foster the Growth of Satellite Imagery Services in India.

The France Satellite Imagery Services Market is Segmented by Application (Geospatial Data Acquisition and Mapping, Natural Resource Management, Surveillance and Security, Conservation and Research, Disaster Management), End-User (Government, Construction, Transportation and Logistics, Military and Defense, Forestry and Agriculture). The Market Sizes and Forecasts are Provided in Terms of Value (USD) for all the Above Segments.

The Nordics satellite imagery services market is projected to grow from $0.22 million in 2025 to $0.96 million by 2033, exhibiting a CAGR of 13.62% during the forecast period. The increasing adoption of satellite imagery for various applications, such as geospatial data acquisition and mapping, natural resource management, and surveillance and security, is driving the market growth. Moreover, the expanding construction and transportation & logistics sectors in the region are further boosting the demand for satellite imagery services. Key trends shaping the Nordics satellite imagery services market include:

Growing adoption of cloud-based platforms and services for satellite imagery processing and analysis: This trend is enabling end-users to access satellite imagery data and services without the need for significant upfront investments in infrastructure. Increasing availability of high-resolution satellite imagery: The launch of new satellites and the development of new image processing technologies are making it possible to obtain high-resolution satellite imagery, which is essential for a variety of applications, such as mapping and land use planning. Emergence of new applications for satellite imagery: Satellite imagery is increasingly being used for a variety of new applications, such as environmental monitoring, disaster management, and precision agriculture. These new applications are creating new opportunities for growth in the Nordics satellite imagery services market. Recent developments include: May 2023 - Business Finland granted EUR 30 million (USD 32.75 million) loan funding for ICEYE's product development project based on innovative new sensor and space technology that will provide real-time and reliable information to support decision-making worldwide. The project aims to create a unique information and software platform, design and develop technology for next-generation satellites, and apply the high-accuracy information from satellites globally for natural catastrophe analysis, modeling, and decision-making., March 2023 - Norway's International Climate and Forest Initiative (NICFI) announced that NICFI's satellite data program is extended until September 2023. Norway's International Climate and Forest Initiative (NICFI) grant free access to high-resolution satellite imagery of the tropics to anyone, anywhere, to monitor tropical deforestation. Through Norway's International Climate & Forests Initiative, users can access the planet's high-resolution, analysis-ready satellite images of the world's tropics to help reduce and combat climate change and reverse the loss of tropical forests.. Key drivers for this market are: Increasing Demand among Various End-user Industries, notablly in Forestry Sector, Adoption of Big Data and Imagery Analytics. Potential restraints include: High Cost of Satellite Imaging Data Acquisition and Processing. Notable trends are: Forestry and Agriculture is Analyzed to Hold Significant Market Share.

Datasets UOPNOA and UOS2.

Each dataset contains images and labels to train and test a semantic segmentation model for crop classification / land use with satellite or aircraft imagery. The region of intereset is the northern

• UPNOA is made out of PNOA aircraft imagery and uses RGB images. (34.000 images)
• UOS2 is made out of Sentinel-2 satellite imagery and uses 13 bands or channels per image. (2.000 images)

Ground truth masks were made from SIGPAC data from the northern part of the Iberian Peninsula plateau in Spain.

Originally trained with UNet and DeepLabv3+

Please cite the original paper, which can be found at:

https://doi.org/10.3390/rs13122292

BibTex:

@article{pedrayes2021evaluation,
 title={Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery},
 author={Pedrayes, Oscar D and Lema, Dar{\'\i}o G and Garc{\'\i}a, Daniel F and Usamentiaga, Rub{\'e}n and Alonso, {\'A}ngela},
 journal={Remote Sensing},
 volume={13},
 number={12},
 pages={2292},
 year={2021},
 publisher={Multidisciplinary Digital Publishing Institute}
}

The Brazil Satellite Imagery Services Market report segments the industry into By Application (Geospatial Data Acquisition and Mapping, Natural Resource Management, Surveillance and Security, Conservation and Research, Disaster Management, Intelligence) and By End-User (Government, Construction, Transportation and Logistics, Military and Defense, Forestry and Agriculture, Other End-Users).