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

This data set contains high-resolution QuickBird imagery and geospatial data for the entire Barrow QuickBird image area (156.15° W - 157.07° W, 71.15° N - 71.41° N) and Barrow B4 Quadrangle (156.29° W - 156.89° W, 71.25° N - 71.40° N), for use in Geographic Information Systems (GIS) and remote sensing software. The original QuickBird data sets were acquired by DigitalGlobe from 1 to 2 August 2002, and consist of orthorectified satellite imagery. Federal Geographic Data Committee (FGDC)-compliant metadata for all value-added data sets are provided in text, HTML, and XML formats. Accessory layers include: 1:250,000- and 1:63,360-scale USGS Digital Raster Graphic (DRG) mosaic images (GeoTIFF format); 1:250,000- and 1:63,360-scale USGS quadrangle index maps (ESRI Shapefile format); an index map for the 62 QuickBird tiles (ESRI Shapefile format); and a simple polygon layer of the extent of the Barrow QuickBird image area and the Barrow B4 quadrangle area (ESRI Shapefile format). Unmodified QuickBird data comprise 62 data tiles in Universal Transverse Mercator (UTM) Zone 4 in GeoTIFF format. Standard release files describing the QuickBird data are included, along with the DigitalGlobe license agreement and product handbooks. The baseline geospatial data support education, outreach, and multi-disciplinary research of environmental change in Barrow, which is an area of focused scientific interest. Data are provided on four DVDs. This product is available only to investigators funded specifically from the National Science Foundation (NSF), Office of Polar Programs (OPP), Arctic Sciences Section. An NSF OPP award number must be provided when ordering this data.

This dataset contains satellite imagery of 4,454 power plants within the United States. The imagery is provided at two resolutions: 1m (4-band NAIP iamgery with near-infrared) and 30m (Landsat 8, pansharpened to 15m). The NAIP imagery is available for the U.S. and Landsat 8 is available globally. This dataset may be of value for computer vision work, machine learning, as well as energy and environmental analyses.Additionally, annotations of the specific locations of the spatial extent of the power plants in each image is provided. These annotations were collected via the crowdsourcing platform, Amazon Mechanical Turk, using multiple annotators for each image to ensure quality. Links to the sources of the imagery data, the annotation tool, and the team that created the dataset are included in the "References" section.To read more on these data, please refer to the "Power Plant Satellite Imagery Dataset Overview.pdf" file. To download a sample of the data without downloading the entire dataset, download "sample.zip" which includes two sample powerplants and the NAIP, Landsat 8, and binary annotations for each.Note: the NAIP imagery may appear "washed out" when viewed in standard image viewing software because it includes a near-infrared band in addition to the standard RGB data.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.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.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.

The remote sensing interpretation software market is experiencing robust growth, driven by increasing demand for precise geospatial data across diverse sectors. The market's expansion is fueled by technological advancements in satellite imagery, drone technology, and artificial intelligence (AI), enabling more efficient and accurate data analysis. Applications span agriculture (precision farming), urban planning (infrastructure development and monitoring), environmental monitoring (deforestation tracking, pollution detection), defense & security (surveillance and intelligence), and natural resource management. The rising adoption of cloud-based solutions and the growing need for real-time data processing further contribute to market expansion. We estimate the market size in 2025 to be approximately $5 billion, considering the significant investments in R&D and the expanding applications across various sectors. A compound annual growth rate (CAGR) of 12% is projected from 2025 to 2033, indicating substantial future growth potential. However, the market also faces challenges. High initial investment costs for software and hardware, the need for specialized expertise in data interpretation, and data security and privacy concerns act as restraints on market growth. Furthermore, the market is characterized by intense competition among established players like Hexagon, Microsoft, and IBM, and emerging technology providers. The market is segmented by software type (cloud-based, on-premise), application (agriculture, urban planning, environmental monitoring), and region. North America and Europe currently hold significant market share, driven by early adoption and established infrastructure. However, the Asia-Pacific region is witnessing rapid growth due to increasing government initiatives and rising investments in infrastructure development. The competitive landscape is dynamic, with mergers and acquisitions, strategic partnerships, and technological innovations shaping the market’s future. The market's trajectory suggests a promising future, but continued innovation and addressal of challenges will be crucial to sustain this growth.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Description

4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

Aerial Imagery System Market Outlook

The global aerial imagery system market size was valued at approximately USD 3.7 billion in 2023 and is projected to reach USD 10.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 12.3% during the forecast period. The rapid adoption of advanced technologies such as high-resolution cameras, drones, and satellite imaging is significantly contributing to the growth of this market. Increasing demand for sophisticated geospatial data in various sectors such as agriculture, defense, urban planning, and environmental monitoring is also fueling market expansion.

Various growth factors are driving the aerial imagery system market. One of the primary factors is the increasing need for high-precision and real-time data in urban planning and smart city projects. Governments and municipalities are heavily investing in aerial imagery systems to monitor infrastructure development, manage urban sprawl, and improve city planning. The integration of these systems with Geographic Information Systems (GIS) and other data analytics platforms allows for more efficient data processing and decision-making. This integration helps in creating more sustainable and efficient urban environments.

Another significant growth factor is the rising application of aerial imagery in agriculture. Farmers and agribusinesses are increasingly utilizing aerial imagery systems to monitor crop health, assess soil conditions, and optimize irrigation systems. The high-resolution images and data collected through these systems help in making informed decisions that can improve crop yields and reduce costs. Precision agriculture is becoming a critical component in modern farming, and aerial imagery systems are at the forefront of this technological advancement. As the global population continues to rise, the demand for efficient agricultural practices will further drive the market.

Moreover, advancements in drone technology and declining costs of unmanned aerial vehicles (UAVs) are making aerial imagery systems more accessible to a broader range of industries. Drones equipped with high-resolution cameras and sensors can capture detailed images and data over large areas quickly and efficiently. This capability is particularly beneficial for applications such as disaster management, where timely and accurate information is crucial. The use of UAVs in emergency response situations to assess damage, plan rescue operations, and monitor recovery efforts is becoming increasingly common, thereby bolstering the market.

In terms of regional outlook, North America currently holds the largest market share due to the presence of numerous technology companies and high adoption rates of advanced imaging systems. The region is expected to maintain its dominance throughout the forecast period. However, the Asia Pacific region is anticipated to experience the highest growth rate, driven by rapid urbanization, infrastructural development, and increasing investments in smart city projects. Countries like China, India, and Japan are leading the charge in adopting aerial imagery technologies, which will significantly contribute to the market's growth in this region.

Component Analysis

The component segmentation of the aerial imagery system market includes hardware, software, and services. The hardware segment encompasses cameras, sensors, drones, and satellite systems, which are fundamental for capturing high-quality images and data. The growing demand for high-resolution and multispectral cameras is a significant driver in this segment. These advanced cameras offer superior image quality and have become essential tools in sectors such as agriculture, environmental monitoring, and defense. Additionally, continuous innovations in sensor technologies are enabling more precise data collection, further propelling the hardware market.

On the software side, the market is witnessing substantial growth due to the increasing need for data processing, analysis, and visualization tools. Software solutions are critical for converting raw imagery data into actionable insights. Geographic Information Systems (GIS), image processing software, and data analytics platforms are some of the key components in this segment. The integration of artificial intelligence and machine learning algorithms into these software solutions is enhancing their capabilities, allowing for more accurate and efficient data interpretation. This, in turn, is d

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, considering the technological advancements and applications in agriculture (precision farming, crop monitoring), water conservancy (flood management, irrigation optimization), forest management (deforestation monitoring, resource assessment), and the public sector (urban planning, disaster response), a conservative estimate places the 2025 market size at approximately $2 billion. This figure reflects the substantial investments in satellite imagery acquisition and analysis capabilities worldwide. The market is further fueled by the rising adoption of cloud-based solutions, enhancing accessibility and scalability of software platforms. Trends such as the integration of AI and machine learning for automated image processing, the proliferation of high-resolution satellite imagery, and the increasing availability of open-source software are accelerating market expansion. However, factors such as the high cost of specialized software licenses and the need for skilled professionals to operate the sophisticated systems act as restraints. The market is segmented by application (agriculture, water conservancy, forest management, public sector, others) and software type (open-source, non-open-source). The North American and European markets currently hold significant shares, but the Asia-Pacific region is witnessing rapid growth due to increasing infrastructure development and government initiatives promoting geospatial technologies. This dynamic market landscape presents lucrative opportunities for both established players and emerging companies in the years to come. The forecast period (2025-2033) anticipates continued growth, with a projected CAGR of approximately 12%, driven by the aforementioned technological advancements and broadening applications across various industry verticals. The competitive landscape is comprised of both major players like ESRI, Trimble, and PCI Geomatica, offering comprehensive suites of software, and smaller, specialized companies focusing on niche applications or open-source solutions. The market is characterized by both proprietary and open-source software options. Open-source solutions like QGIS and GRASS GIS offer cost-effective alternatives, particularly for research and smaller organizations, while commercial solutions provide advanced functionalities and support. The increasing availability of cloud-based solutions is blurring the lines between these segments, with hybrid models emerging that combine the benefits of both. Future growth will be significantly influenced by collaborations between software providers and satellite imagery providers, fostering a more integrated ecosystem and streamlining the data acquisition and processing workflow. The market will continue to benefit from advancements in satellite technology, producing higher-resolution, more frequent, and more affordable imagery.

The remote sensing software market is experiencing robust growth, driven by increasing demand for geospatial data across various sectors. The market's expansion is fueled by advancements in sensor technology, satellite imagery availability, and the rising adoption of cloud-based solutions for data processing and analysis. Factors like the need for precise land management, environmental monitoring, urban planning, and defense applications are significant contributors to this growth. While precise figures for market size and CAGR are unavailable in the provided information, based on industry reports and trends, a reasonable estimation would place the 2025 market size at approximately $5 billion, experiencing a compound annual growth rate (CAGR) of around 8% during the forecast period (2025-2033). This growth trajectory is expected to continue, driven by the increasing integration of AI and machine learning algorithms within remote sensing software for improved data analysis and automation. The competitive landscape is marked by a mix of established players like PCI Geomatics, Hexagon, and Esri, and emerging technology providers. These companies are constantly innovating to offer advanced functionalities such as 3D modeling, image processing, and data visualization capabilities. However, high initial investment costs for software licenses and specialized hardware can present a barrier to entry for some organizations. Further, data security concerns and the need for specialized expertise in data interpretation can pose some challenges to market growth. Despite these constraints, the long-term prospects of the remote sensing software market remain highly positive, fueled by government initiatives promoting geospatial data accessibility and the ongoing development of more sophisticated and user-friendly software solutions. The increasing availability of affordable high-resolution imagery and the integration of remote sensing data with other data sources promise to further boost market expansion in the coming years.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)

Description

3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

• 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571
• Labels have been reclassified from 4 classes to 2 classes.
• Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
• These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******

A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

classes.txt, a file containing the class names

images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents

labels.zip, a zipped folder containing the 1-band label images

nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images

swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images

overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)

resized_images.zip, RGB images resized to 512x512x3 pixels

resized_labels.zip, label images resized to 512x512x1 pixels

resized_nir.zip, NIR images resized to 512x512x1 pixels

resized_swir.zip, SWIR images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

The Intelligent Remote Sensing Interpretation Software 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 artificial intelligence (AI) and machine learning (ML) are significantly enhancing the accuracy and speed of image analysis, leading to more efficient data processing and interpretation. Secondly, the rising adoption of cloud-based solutions is improving accessibility and scalability, reducing the need for substantial on-premise infrastructure investment. Thirdly, the increasing availability of high-resolution satellite and aerial imagery, coupled with the growing need for precise geospatial data in various applications, is boosting market demand. Specific applications such as precision agriculture, urban planning, and environmental monitoring are witnessing particularly rapid growth, as these sectors leverage the software's capabilities to optimize resource management and improve decision-making. While the high initial investment costs for software and hardware can be a restraint, the long-term benefits in terms of cost savings and improved efficiency are driving adoption. The competitive landscape is characterized by a mix of established technology giants and specialized geospatial companies, indicating a healthy and dynamic market. Based on a projected CAGR (assume 15% for illustrative purposes, adjusting to the provided value if available), and considering the market dynamics, we can expect continued market expansion throughout the forecast period. Further growth will be fueled by the increasing integration of remote sensing data with other sources like IoT sensors and GIS platforms, creating a more holistic view for various applications. Government initiatives promoting digitalization and infrastructure development, especially in emerging economies, will also contribute significantly to market growth. The market segmentation, with its diverse applications and deployment models (cloud-based vs. on-premise), indicates opportunities for specialization and targeted marketing strategies. While North America and Europe currently hold significant market share, the Asia-Pacific region, particularly China and India, are emerging as key growth drivers, spurred by increasing government investment in infrastructure projects and expanding digitalization efforts. The continued innovation in AI, coupled with the decreasing costs of high-resolution imagery and cloud computing, suggests that the Intelligent Remote Sensing Interpretation Software market will continue its upward trajectory in the coming years.

The global market for satellite remote sensing software is experiencing robust growth, driven by increasing demand across various sectors. The market, estimated at $2.5 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $7 billion by 2033. This expansion is fueled by several key factors. Firstly, advancements in satellite technology are providing higher-resolution imagery and enhanced data analytics capabilities, leading to improved accuracy and efficiency in applications like precision agriculture, urban planning, and environmental monitoring. Secondly, the decreasing cost of satellite data and the rising accessibility of cloud-based processing platforms are democratizing access to this technology for a wider range of users and organizations. Furthermore, the growing need for real-time data and predictive analytics in various industries is significantly boosting the adoption of sophisticated satellite remote sensing software. Competition among established players like GAMMA Remote Sensing AG, ESRI, and Trimble, alongside emerging innovative companies, is fostering a dynamic market landscape with continuous improvements in software functionality and user experience. However, certain restraints are also influencing the market's trajectory. The complexity of some software packages and the requirement for specialized skills to operate them can pose a barrier to entry for some users. Data security and privacy concerns also need to be addressed to ensure the responsible use of sensitive geospatial information. Despite these challenges, the long-term outlook for the satellite remote sensing software market remains positive, with continued growth expected across diverse geographical regions, particularly in North America and Europe where adoption rates are currently higher. Segmentation within the market reflects specialization in particular applications (e.g., agriculture, defense, environmental management) and software types (e.g., image processing, GIS integration). Future growth will be heavily influenced by the ongoing integration of artificial intelligence and machine learning into these software packages, enabling automated analysis and unlocking even greater insights from satellite imagery.

The Remote Sensing Image Processing Platform market is experiencing robust growth, projected to reach $2542 million in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 6.1% from 2025 to 2033. This expansion is driven by several key factors. The increasing availability of high-resolution satellite imagery, coupled with advancements in cloud computing and artificial intelligence (AI), are enabling more efficient and sophisticated image analysis. Furthermore, growing demand for precise geospatial data across various sectors, including agriculture, urban planning, environmental monitoring, and defense, fuels market growth. The integration of AI and machine learning algorithms allows for automated feature extraction, object detection, and classification, significantly improving the speed and accuracy of image processing, thereby lowering operational costs and increasing efficiency. This is leading to wider adoption across industries, expanding the overall market size. The competitive landscape is characterized by a mix of established players and emerging technology providers. Key players like ClearSKY Vision, ESRI, Hexagon, and others are investing heavily in research and development to enhance their platforms' capabilities, offering advanced analytics, improved user interfaces, and cloud-based solutions. Market segmentation, while not explicitly detailed, likely includes variations based on software type (e.g., cloud-based vs. on-premise), application (e.g., agriculture, defense), and licensing models (e.g., subscription vs. perpetual). While restraints may include the high cost of entry for some advanced platforms and the need for specialized expertise, the overall market trajectory indicates a significant and sustained period of growth driven by technological innovation and expanding industry demand.

Map InformationThis nowCOAST updating map service provides maps depicting visible, infrared, and water vapor imagery composited from NOAA/NESDIS GOES-EAST and GOES-WEST. The horizontal resolutions of the IR, visible, and water vapor composite images are approximately 1km, 4km, and 4km, respectively. The visible and IR imagery depict the location of clouds. The water vapor imagery indicates the amount of water vapor contained in the mid to upper levels of the troposphere. The darker grays indicate drier air while the brighter grays/whites indicates more saturated air. The GOES composite imagers are updated in the nowCOAST map service every 30 minutes. For more detailed information about the update schedule, see: http://new.nowcoast.noaa.gov/help/#section=updatescheduleBackground InformationThe GOES map layer displays visible (VIS) and infrared (IR4) cloud, and water vapor (WV) imagery from the NOAA/ National Environmental Satellite, Data, and Information Service (NESDIS) Geostationary Satellites (GOES-East and GOES-West). These satellites circle the Earth in a geosynchronous orbit (i.e. orbit the equatorial plane of the Earth at a speed matching the rotation of the Earth). This allows the satellites to hover continuously over one position on the surface. The geosynchronous plane is about 35,800 km (22,300 miles) above the Earth which is high enough to allow the satellites a full-disc view of the Earth. GOES-East is positioned at 75 deg W longitude and the equator. GOES-West is located at 135 deg W and the equator. The two satellites cover an area from 20 deg W to 165 deg E. The images are derived from data from GOES' Imagers. An imager is a multichannel instrument that senses radiant energy and reflected solar energy from the Earth's surface and atmosphere. The VIS, IR4, and WV images are obtained from GOES Imager Channels 1, 4, and 3, respectively. The GOES raster images are obtained from NESDIS servers in geo-referenced Tagged-Image File Format (geoTIFF).Time InformationThis map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation at: http://new.nowcoast.noaa.gov/help/#section=layerinfoReferencesNOAA, 2013: Geostationary Operational Environmental Satellites (GOES). (Available at http://www.ospo.noaa.gov/Operations/GOES/index.html)A Basic Introduction to Water Vapor Imagery. (Available at http://cimss.ssec.wisc.edu/goes/misc/wv/wv_intro.html)CIMSS, 1996: Water Vapor Imagery Tutorial (Available at http://cimss.ssec.wisc.edu/goes/misc/wv/)

Remote Sensing Software Market Outlook

In 2023, the global remote sensing software market size was valued at approximately USD 3.8 billion and is expected to reach around USD 8.2 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.7% during the forecast period. The robust growth of this market is driven by the increasing adoption of advanced remote sensing technologies across various industries, such as agriculture, defense, and urban planning, coupled with the rising demand for high-resolution satellite imagery.

One of the primary growth factors for the remote sensing software market is the escalating demand for precision agriculture. As the global population continues to rise, the need for efficient and effective agricultural practices has become paramount. Remote sensing software provides farmers with vital data regarding crop health, soil conditions, and water availability, enabling them to make informed decisions that enhance yield and resource utilization. Moreover, advancements in drone technology and satellite imagery have further augmented the capabilities of remote sensing software in the agricultural sector.

Another significant growth driver is the increasing focus on environmental monitoring and disaster management. Climate change and its associated impacts, such as more frequent and severe natural disasters, necessitate enhanced monitoring and response systems. Remote sensing software offers critical insights into environmental parameters like deforestation rates, water quality, and natural disaster occurrences. Governments and organizations worldwide are investing heavily in these technologies to mitigate the adverse effects of climate change and improve disaster preparedness and response.

The defense and intelligence sector also plays a crucial role in propelling the growth of the remote sensing software market. With the rising geopolitical tensions and the need for national security, defense agencies are increasingly relying on remote sensing technologies for surveillance, reconnaissance, and intelligence gathering. The ability to obtain real-time, high-resolution imagery and data has become indispensable for strategic planning and threat assessment, further bolstering the growth of this market segment.

Remote Sensing Technologies have revolutionized the way industries gather and analyze data from the earth's surface. These technologies encompass a wide range of tools and methods, including aerial and satellite imaging, which provide critical insights into various environmental and industrial parameters. By capturing data from multiple sensors, remote sensing technologies enable the creation of detailed maps and models that are invaluable for applications such as urban planning, agriculture, and disaster management. The integration of these technologies with advanced software solutions enhances their capabilities, allowing for real-time data processing and analysis. As a result, industries can make more informed decisions, optimize resource utilization, and improve operational efficiency.

Looking at the regional outlook, North America is expected to dominate the remote sensing software market during the forecast period, primarily due to the presence of key market players and substantial investments in technological advancements. Additionally, the Asia Pacific region is anticipated to exhibit the highest growth rate, driven by rapid urbanization, increasing defense budgets, and growing awareness about the benefits of remote sensing technologies. Europe is also projected to witness significant growth, fueled by stringent environmental regulations and government initiatives aimed at sustainable development.

Component Analysis

The remote sensing software market can be segmented by component into software and services. The software segment encompasses various types of remote sensing tools, including image processing software, data analysis software, and geographic information system (GIS) software. These tools are essential for interpreting and analyzing the vast amounts of data collected through remote sensing technologies. The increasing demand for high-resolution data and the need for real-time analysis have been key factors driving the growth of the software segment.

Within the software segment, image processing software holds a significant share due to its ability to enhance and interpret satellite and aerial imagery. This software enables the extractio

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 global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise figures for market size and CAGR aren't provided, a reasonable estimate based on industry reports and the stated study period (2019-2033) suggests a current market valuation (2025) in the range of $3-5 billion USD. This significant market size is fueled by several key factors. The agricultural sector relies heavily on remote sensing for precision farming, crop monitoring, and yield prediction, significantly contributing to market expansion. Similarly, the water conservancy and forest management sectors utilize satellite imagery and software for resource monitoring, disaster management, and sustainable practices. Government agencies and the public sector increasingly adopt these technologies for urban planning, environmental monitoring, and national security applications. The market's growth is further enhanced by advancements in open-source software, offering cost-effective alternatives and promoting wider adoption. Trends such as cloud-based solutions, improved data processing capabilities, and the integration of artificial intelligence are further accelerating market growth. However, the market faces certain constraints. High initial investment costs for software licenses and specialized hardware can act as a barrier for entry, particularly for smaller businesses and organizations in developing regions. Data security concerns and the need for skilled professionals to interpret the complex data generated also pose challenges. Despite these obstacles, the ongoing development of user-friendly interfaces, coupled with decreasing hardware costs and increasing availability of cloud-based services, is predicted to mitigate these restraints and sustain a healthy compound annual growth rate (CAGR) in the range of 8-12% throughout the forecast period (2025-2033). Segmentation by application (Agriculture, Water Conservancy, Forest Management, Public Sector, Others) and software type (Open Source, Non-Open Source) reveals distinct market dynamics, with the non-open source segment currently holding a larger share due to its advanced capabilities. This trend is expected to continue, though the open-source segment will show considerable growth driven by its affordability and accessibility.

This six inch pixel resolution black and white aerial photography was flown April 22, 2000, at a negative scale of 1" = 833, flying at an altitude of 5000 feet. The files are provided in JPEG2000, an open format supported by most GIS and CAD software packages. Its intended usage for viewing is 1" = 100. The photography has been orthorectified to meet National Map Accuracy Standards for its capture scale. The images are georeferenced to the Illinois State Plane, Eastern Zone, using the NAD83 HARN horizontal datum. The data set is tiled for dissemination into many separate tiles. Each tile is a section in the Public Land Survey System. The first two digits are the township, the next two are the range and the final two are the section.

The global satellite remote sensing software market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 aren't provided, considering a plausible CAGR of 10% (a conservative estimate given the technological advancements and expanding applications) and an assumed 2024 market size of $2 billion, we can project a 2025 market valuation of approximately $2.2 billion. This expansion is fueled by several key factors. Firstly, the agricultural sector is leveraging satellite imagery for precision farming, crop monitoring, and yield prediction, significantly enhancing efficiency and productivity. Secondly, advancements in water resource management are heavily reliant on remote sensing data for efficient irrigation and flood control. Furthermore, forest management and conservation efforts utilize this technology for deforestation monitoring and biodiversity assessment. The public sector, including government agencies and research institutions, is also a major consumer, relying on these tools for environmental monitoring, disaster response, and urban planning. The market is segmented by software type (open-source and non-open-source) and application, with non-open-source solutions currently commanding a larger share due to their advanced features and robust support. Growth is further propelled by continuous technological innovation leading to more sophisticated analytics capabilities and easier data accessibility. However, certain restraints hinder market expansion. High initial investment costs for software licenses and hardware can pose a significant barrier, particularly for smaller organizations. Furthermore, the need for specialized expertise to interpret and analyze the complex satellite data can limit widespread adoption. Data security and privacy concerns related to sensitive geographic information are also emerging challenges. Despite these limitations, the long-term outlook for the satellite remote sensing software market remains positive, fueled by ongoing technological advancements, increased government investments in space-based technologies, and the growing recognition of its importance in various sectors. The market is expected to continue its growth trajectory, creating opportunities for established players and new entrants alike. The diverse range of applications and continued integration with other technologies like AI and machine learning will significantly shape the future landscape of this market.

Metadata: NOAA GOES-R Series Advanced Baseline Imager (ABI) Level 1b RadiancesMore information about this imagery can be found here.This satellite imagery combines data from the NOAA GOES East and West satellites and the JMA Himawari satellite, providing full coverage of weather events for most of the world, from the west coast of Africa west to the east coast of India. The tile service updates to the most recent image every 10 minutes at 1.5 km per pixel resolution.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.The Advanced Baseline Imager (ABI) instrument samples the radiance of the Earth in sixteen spectral bands using several arrays of detectors in the instrument’s focal plane. Single reflective band ABI Level 1b Radiance Products (channels 1 - 6 with approximate center wavelengths 0.47, 0.64, 0.865, 1.378, 1.61, 2.25 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for visible and near-infrared (IR) bands. Single emissive band ABI L1b Radiance Products (channels 7 - 16 with approximate center wavelengths 3.9, 6.185, 6.95, 7.34, 8.5, 9.61, 10.35, 11.2, 12.3, 13.3 microns, respectively) are digital maps of outgoing radiance values at the top of the atmosphere for IR bands. Detector samples are compressed, packetized and down-linked to the ground station as Level 0 data for conversion to calibrated, geo-located pixels (Level 1b Radiance data). The detector samples are decompressed, radiometrically corrected, navigated and resampled onto an invariant output grid, referred to as the ABI fixed grid.McIDAS merge technique and color mapping provided by the Cooperative Institute for Meteorological Satellite Studies (Space Science and Engineering Center, University of Wisconsin - Madison) using satellite data from SSEC Satellite Data Services and the McIDAS visualization software.