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.

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.

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

GUI-based software coded in PYTHON to automate image stitching and alignment processes from a set of tile images for the high throughput image analytics by implementing a series of algorithms: 1) deskewing the image acquired in an oblique view angle, 2) row alignment of the geometrically drifted image due to acquisition errors by detecting the crop row using Hough Transformation, and 3) options for omnidirectional overlap trimming and resizing. Resources in this dataset:Resource Title: iStitch: GUI-based Image Stitching Software. File Name: iStitch.zip

The Photogrammetry Software market is experiencing robust growth, projected to reach $2.9 billion in 2025 and exhibiting a Compound Annual Growth Rate (CAGR) of 16.98% from 2025 to 2033. This expansion is fueled by several key factors. The increasing adoption of drones and advanced imaging sensors provides significantly more affordable and accessible data acquisition for various applications. This, combined with the rising demand for precise 3D models and accurate measurements across diverse industries such as construction, surveying, and mapping, is a major catalyst for market growth. Furthermore, advancements in software algorithms and processing power are enabling faster and more efficient processing of large datasets, leading to quicker turnaround times and cost savings. The market is also witnessing an increase in cloud-based solutions, which offer enhanced scalability, accessibility, and collaboration capabilities. Competitive landscape analysis reveals a blend of established players like Fugro and Nearmap, along with innovative startups like Dronegenuity and Aerobotics, driving both innovation and market consolidation. The market segmentation, although not explicitly provided, can be logically inferred. We can anticipate segments based on software type (e.g., desktop, cloud-based), application (e.g., surveying, construction, agriculture), and industry vertical (e.g., infrastructure, mining, environmental monitoring). Geographic segmentation will also play a role, with regions like North America and Europe expected to hold significant market share initially, followed by growth in Asia-Pacific and other emerging markets driven by infrastructure development and urbanization. While restraining factors may include the high initial investment costs for some software solutions and the need for skilled professionals, the overall growth trajectory of the market is anticipated to remain positive, driven by the significant advantages offered by photogrammetry in various sectors. Recent developments include: • May 2023: Inspired Flight Technologies and Phase One launched a novel plug-and-play solution that combines aerial photography with flexible operations to satisfy various surveying and inspection demands. Phase One is a major global developer and manufacturer of medium- and large-format aerial photography systems. At the same time, Inspired Flight Technologies is a commercial small uncrewed aerial systems (UAS) company., • March 2023: UP42, a geospatial developer platform and marketplace, significantly expanded its aerial imagery and elevation data portfolio through a partnership with Vexcel, a photogrammetric and remote sensing company. Vexcel's aerial data collection initiative is significant worldwide, capturing ultra-high-resolution imagery (at 7.5 to 15 cm resolution) and geospatial data in more than 30 countries., . Key drivers for this market are: Rise of Location-based Services, Increasing Demand from Diversified Applications. Potential restraints include: Rise of Location-based Services, Increasing Demand from Diversified Applications. Notable trends are: The Government is Expected to be the Largest End User of Aerial Imaging.

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

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy.Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection.For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.caAvailable Products:ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServerhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022Central Ontario Orthophotography Project (COOP) 2021South-Western Ontario Orthophotography Project (SWOOP) 2020Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020South Central Ontario Orthophotography Project (SCOOP) 2018North-Western Ontario Orthophotography Project (NWOOP) 2017Central Ontario Orthophotography Project (COOP) 2016South-Western Ontario Orthophotography Project (SWOOP) 2015Algonquin Orthophotography Project (2015)Additional Documentation:Ontario Web Raster Services User Guide (Word)Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every yearContact:Geospatial Ontario (GEO), geospatial@ontario.ca

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.

The photogrammetry software market is experiencing robust growth, driven by increasing adoption across diverse sectors like construction, engineering, and surveying. The market's expansion is fueled by several key factors: the rising availability of high-resolution imagery from drones and satellites, advancements in processing power enabling faster and more accurate 3D model generation, and a growing demand for precise spatial data for various applications, including infrastructure development, urban planning, and precision agriculture. The market is segmented based on software type (cloud-based vs. desktop), application (mapping, modeling, measurement), and end-user industry. Major players like Hexagon, Trimble, and Autodesk dominate the market with their comprehensive solutions, while smaller companies are innovating with specialized features and cost-effective options. Competition is fierce, with companies focusing on improving accuracy, ease of use, and integration with other software and hardware. We project a continued, albeit moderated, growth rate given the maturity of the technology, the rising cost of entry for new players, and cyclical trends within construction. This projection is based on publicly available information regarding competitor activity and technological advancements in the sector. While the market shows strong potential for expansion, certain factors pose challenges. The high initial investment costs for software and hardware can be a barrier for smaller businesses. The need for specialized skills to operate the software and interpret the generated data also requires substantial training resources. Furthermore, data privacy concerns and regulations surrounding the use of aerial imagery must be navigated effectively. Nonetheless, the advantages of accurate and detailed 3D models – including reduced project costs, improved decision-making, and enhanced safety – continue to drive demand. The ongoing development of more user-friendly interfaces and automation features will likely expand the market further by making the technology accessible to a broader range of users. The market's future hinges on continuous innovation, efficient software development, and industry collaboration to further address these challenges.

Introduction

Medical Image Analysis Software Statistics: Medical Image Analysis Software plays a vital role in contemporary healthcare by assisting healthcare experts in precise diagnosis, treatment strategy development, and research activities. It improves image clarity, identifies particular areas of interest, and delivers exact measurements.

Notable trends encompass its smooth incorporation into healthcare systems, reliance on cloud-based solutions, and the rise of personalized medical approaches. Obstacles encompass data security and adherence to regulations.

Nonetheless, the software's future looks bright, fueled by technological advancements and the increasing need for precise medical solutions.

The Geospatial Imagery Analytics System market is experiencing robust growth, driven by increasing adoption of cloud-based solutions, advancements in AI and machine learning for image processing, and rising demand across diverse sectors like agriculture, urban planning, and defense. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by the ability of geospatial imagery analytics to provide actionable insights from satellite, aerial, and drone imagery, leading to improved decision-making across various applications. The market is segmented based on various factors, and key players like Google, Trimble, and Maxar Technologies are constantly innovating to enhance their offerings and capture market share. The integration of advanced analytics capabilities is transforming the landscape, enabling more sophisticated applications like precision agriculture, infrastructure monitoring, and disaster response. Factors such as high initial investment costs and the need for skilled professionals to interpret the data can pose challenges. However, the continuous decline in data storage and processing costs, coupled with the rising availability of skilled professionals, is mitigating these restraints. The market's future prospects are promising, with increasing government investments in infrastructure projects and a rising focus on sustainable development initiatives globally further boosting demand for geospatial imagery analytics solutions. Technological advancements, particularly in the field of hyperspectral imagery and 3D modeling, are expected to further fuel market expansion in the coming years. The global reach of cloud-based platforms is also contributing to the market’s growth, making these sophisticated tools accessible to a broader user base.

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

The Image Quality Analysis Software market is experiencing robust growth, driven by the increasing adoption of digital imaging technologies across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This expansion is fueled by several key factors. Firstly, the burgeoning demand for high-quality images in sectors like aerospace (for drone inspection and satellite imagery), automotive (for automated quality control), and medical (for diagnostics and treatment planning) is driving significant investment in sophisticated image analysis solutions. Secondly, advancements in artificial intelligence (AI) and machine learning (ML) are enabling the development of more accurate, efficient, and automated image quality assessment tools. The shift towards cloud-based solutions further enhances accessibility and scalability, contributing to market growth. Finally, stringent regulatory requirements in various industries mandating quality assurance procedures are also boosting demand. However, the market also faces certain challenges. The high cost of advanced software and specialized hardware can be a barrier to entry for some smaller businesses. Furthermore, the complexity of implementing and integrating these solutions into existing workflows can present a hurdle. Despite these restraints, the increasing need for objective and quantitative image quality assessment across various applications will continue to propel market growth. The market is segmented by application (aerospace, automotive, medical, entertainment, media, industrial) and type (local, cloud-based). The cloud-based segment is expected to witness faster growth due to its inherent flexibility and scalability. Key players in the market are leveraging strategic partnerships, acquisitions, and technological innovations to maintain a competitive edge. Geographic expansion, particularly in emerging economies, presents lucrative opportunities for market players.

The US geospatial imagery analytics market is experiencing robust growth, fueled by increasing adoption across diverse sectors. The global market's substantial size of $5.38 billion in 2025 and a Compound Annual Growth Rate (CAGR) of 24.14% project significant expansion through 2033. While precise figures for the US market segment are unavailable, a reasonable estimation, considering the US's significant technological advancement and market dominance in related fields, would place its 2025 market size at approximately $2.0 billion. This substantial value is driven by several key factors. The rising demand for precise location intelligence across various sectors such as insurance (risk assessment and fraud detection), agriculture (precision farming and yield optimization), defense and security (surveillance and intelligence gathering), and environmental monitoring (disaster management and climate change analysis) are primary growth catalysts. Technological advancements like improved sensor technologies, enhanced image processing algorithms, and the proliferation of cloud-based solutions further accelerate market expansion. The increasing availability of high-resolution satellite imagery and the development of sophisticated analytics platforms are also contributing to the market's growth trajectory. However, the market faces certain restraints. High initial investment costs for implementing geospatial imagery analytics solutions, especially for SMEs, can pose a barrier to entry. Moreover, concerns regarding data privacy and security, along with the complexity of data analysis and interpretation, can hinder wider adoption. Despite these challenges, the long-term outlook remains positive. The continuous development of user-friendly software, the decreasing cost of data storage and processing, and growing government initiatives promoting the use of geospatial technologies are expected to mitigate these limitations and propel the market toward sustained growth. The market segmentation by deployment (on-premise and cloud), organization size (SMEs and large enterprises), and vertical industries presents diverse opportunities for growth and specialization within the US market. The competitive landscape is characterized by a mix of established technology giants and specialized geospatial analytics providers, each vying for a share of this rapidly expanding market. Recent developments include: May 2023: CAPE Analytics, a player in AI-powered geospatial property intelligence, has extended its partnership with The Hanover Insurance Group, which provides independent agents with the best insurance coverage and prices. Integrating geospatial analytics and inspection and rating models into Hanover's underwriting procedure is the central component of the partnership expansion. The company's rating plans will benefit from this strategic move, improving workflows, new and renewal underwriting outcomes, and pricing segmentation., March 2023 : Carahsoft Technology Corp., The Trusted Government IT Solutions Provider, and Orbital Insight, a player in geospatial intelligence, announced a partnership. By the terms of the agreement, Carahsoft will act as Orbital Insight's Master Government Aggregator, making the leading AI-powered geospatial data analytics available to the public sector through Carahsoft's reseller partners and contracts for Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO) ValuePoint, National Cooperative Purchasing.. Key drivers for this market are: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Potential restraints include: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Notable trends are: Small Satellities will Boost Market Growth.

In May 2013, the Grand Canyon Monitoring and Research Center (GCMRC) of the U.S. Geological Survey’s (USGS) Southwest Biological Science Center (SBSC) acquired airborne multispectral high resolution data for the Colorado River in Grand Canyon in Arizona, USA. The imagery data consist of four bands (blue, green, red and near infrared) with a ground resolution of 20 centimeters (cm). These data are available to the public as 16-bit geotiff files. They are projected in the State Plane (SP) map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). The assessed accuracy for these data is based on 91 Ground Control Points (GCPs), and is reported at 95% confidence as 0.64 meters (m) and a Root Mean Square Error (RMSE) of 0.36m. The airborne data acquisition was conducted under contract by Fugro Earthdata Inc. using two fixed wing aircraft from May 25th to 30th, 2013 at altitudes between 2440 meters to 3350 meters above mean sea level. The data delivered by Fugro Earthdata Inc. were checked for smear, shadow extent and water clarity as described for previous image acquisitions in Davis (2012). We then produced a corridor-wide mosaic using the best possible tiles with the least amount of smear, the smallest shadow extent, and clearest, most glint-free water possible. During the mosaic process adjacent tiles sometimes had to be spectrally adjusted to account for differences in date, time, sun angle, weather, and environment. We used the same method as described in Davis (2012) for the spectral adjustment. A horizontal accuracy assessment was completed by Fugro Earthdata Inc. using 188 GCPs provided by GCMRC. The GCPs were marked during the image acquisition with 1m2 diagonally alternated black and white plastic panels centered on control points throughout the river corridor in the GCMRC survey control network (Hazel and others, 2008). The Root Mean Square Error (RMSE) accuracy reported by Fugro Earthdata Inc. is 0.17m Easting and 0.15m Northing, or better, depending on the acquisition zone. The 16-bit image data are stored as four band images in embedded geotiff format, which can be read and used by most geographic information system (GIS) and image-processing software. The TIFF world files (tfw) are provided, however they are not needed for many software to read an embedded geotiff image. The image files are projected in the State Plane (SP) 2011, map projection using the central Arizona zone (202) and the North American Datum of 1983 (NAD83). A complete detailed description of the methods can be found in the associated USGS Data Series 1027 for these data, https://pubs.er.usgs.gov/publication/ds1027.

The Medical Imaging Analysis Software Market report segments the industry into By Image Type (2D Image, 3D Image, 4D Image), By Modality (Tomography, Ultrasound Imaging, Radiographic Imaging, X-ray Imaging, Magnetic Resonance Imaging (MRI), Other Modalities), By Software Type (Integrated Software, Standalone Software), By End User (Hospital, Diagnostic Center, Research Center), and Geography.

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.

Douglas County, Kansas natural color, six inch resolution, and rectified orthoimagery. Aerial imagery reference basemap projected in WGS84 coordinate systems, with zoom levels configured for ArcGIS Online | Google Maps | Bing Maps for use in mapping applications.Digital orthophotography produced from aerial photography captured in Spring of 2022 as requested by Douglas County Kansas, Shawnee County Kansas, Jefferson County Kansas, City of Topeka Kansas, and the City of Lawrence Kansas. Collected 03-15-2022, 03-16-2022, 03-19-2022.Data meets ASPRS @ 1:100 scale, limiting RMSE within 1.0'. Surdex collected ground survey and airborne GPS data at the time of image acquisition and was used exclusively to control the product to ground coordinates.Aircraft was deployed to eastern Kansas in early March 2022 for digital image acquisition during requested time frame. Ac Cessna Conquest equipped with a Leica ADS100 (Digital Mapping Camera) captured 4 band imagery over the entire project area of interest. Imagery was captured at an altitude of 6,200' above mean terrain, allowing 0% cloud conditions. Three of the four bands (RGB natural color) of raw camera data were processed by Surdex using Leica X Pro software coupled with Surdex proprietary image handling software for color correction and dodging. Post processing of Airborne and Ground GPS data was performed with Waypoint Consulting's GrafNav software. Aerial Triangulation was completed using Leica X Pro Automatic Triangulation and rectification of imagery to the client supplied DEM was completed using Surdex proprietary software . Image mosaicking, QC and final tiled products were produced using proprietary Surdex software.

The computer vision in geospatial imagery market is experiencing robust growth, driven by the increasing availability of high-resolution satellite and aerial imagery, coupled with advancements in artificial intelligence and machine learning algorithms. This convergence allows for automated analysis of vast geospatial datasets, unlocking valuable insights across diverse sectors. The market's expansion is fueled by the rising need for precise and timely information in applications like precision agriculture, urban planning, environmental monitoring, and infrastructure management. Energy sector applications, including pipeline inspection and renewable energy resource assessment, are also significant contributors to market growth. The adoption of smart camera-based systems is gaining traction, offering advantages in portability and real-time processing compared to traditional PC-based solutions. However, challenges remain, including the high cost of specialized hardware and software, the need for skilled professionals to interpret the complex outputs, and data privacy concerns related to the use of imagery data. The market is segmented by application (energy, environmental monitoring, and others) and by type (PC-based and smart camera-based systems), with North America currently holding a significant market share due to early adoption and technological advancements. Future growth will be significantly influenced by technological innovation, government regulations promoting data sharing and accessibility, and the increasing demand for data-driven decision-making in various industries. Despite challenges, the market is poised for continued expansion over the forecast period (2025-2033). The increasing affordability of computer vision technologies, coupled with the ongoing development of more user-friendly software solutions, will likely contribute to broader adoption across various sectors. The integration of cloud-based platforms is also expected to facilitate data processing and analysis, lowering barriers to entry for smaller businesses. Geographic expansion, particularly in developing economies with burgeoning infrastructure projects and agricultural needs, will be another key driver of market growth. Competition among established technology companies and emerging players will continue to intensify, leading to innovation in both hardware and software solutions. A focus on developing robust and reliable algorithms capable of handling complex and noisy data will be paramount for future market success.

ZyPro is a deep learning framework designed to support the PhD research project “AI in the Sky: Advancing Wildlife Survey Methods in Africa with Deep Learning and Aerial Imagery.” The research aims to improve wildlife monitoring using semantic segmentation and object detection on remote sensing imagery. ZyPro provides tools for training, testing, and predicting with U-Net-based neural networks, as well as specialized modules for remote sensing image processing, such as large image handling, multi-channel data processing, image clipping, tiling, and data augmentation. This software project includes custom loss functions, flexible training pipelines, and various utilities to facilitate high-quality analysis of remote sensing data.