Land cover describes the surface of the earth. Land-cover maps are useful in urban planning, resource management, change detection, agriculture, and a variety of other applications in which information related to the earth's surface is required. Land-cover classification is a complex exercise and is difficult to capture using traditional means. Deep learning models are highly capable of learning these complex semantics and can produce superior results.There are a few public datasets for land cover, but the spatial and temporal coverage of these public datasets may not always meet the user’s requirements. It is also difficult to create datasets for a specific time, as it requires expertise and time. Use this deep learning model to automate the manual process and reduce the required time and effort significantly.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band very high-resolution (10 cm) imagery.OutputClassified raster with the 8 classes as in the LA county landcover dataset.Applicable geographiesThe model is expected to work well in the United States and will produce the best results in the urban areas of California.Model architectureThis model uses the UNet model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an overall accuracy of 84.8%. The table below summarizes the precision, recall and F1-score of the model on the validation dataset: ClassPrecisionRecallF1 ScoreTree Canopy0.8043890.8461520.824742Grass/Shrubs0.7199930.6272780.670445Bare Soil0.89270.9099580.901246Water0.9808850.9874990.984181Buildings0.9222020.9450320.933478Roads/Railroads0.8696370.8629210.866266Other Paved0.8114650.8119610.811713Tall Shrubs0.7076740.6382740.671185Training dataThis model has been trained on very high-resolution Landcover dataset (produced by LA County).LimitationsSince the model is trained on imagery of urban areas of LA County it will work best in urban areas of California or similar geography.Model is trained on limited classes and may lead to misclassification for other types of LULC classes.Sample resultsHere are a few results from the model.

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.

This deep learning model is used to detect palm trees in high resolution drone or aerial imagery. Palm trees detection can be used for creating an inventory of palm trees, monitoring their health and location, and predicting the yield of palm oil, etc. High resolution aerial and drone imagery can be used for palm tree detection due to its high spatio-temporal coverage.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution RGB imagery (5 - 15 centimeter spatial resolution).OutputFeature class containing detected palm trees.Applicable geographiesThe model is expected to work well globally.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 75 percent.Training dataThis model has been trained on an Esri proprietary palm tree detection dataset.Sample resultsHere are a few results from the model. To view more, see this story.

Elephants are the largest terrestrial living species. They are herbivorous animals and require 100 kilograms to 200 kilograms of food and about 230 liters of water each day. Their home range can expand up to 11,000 square kilometers. Their ability to find food and water sources is gained from traditional knowledge learned over generations. This knowledge, which is important for survival, is lost if elder elephants of the herd perish.Elephants are endangered due to many reasons. They can be killed by poachers for their tusks, or captured and tamed for social status and for the circus. Changes in environment such as global warming, rain patterns, deforestation, and mining can lead to degradation of their habitat, forcing these animals to move to different areas in search of food and water. This can cause conflicts with humans as elephants move into human settlements and farmlands. They can also run into electrical fences and traps.To avoid life-threatening incidents, and for their conservation, monitoring the elephants and their movements is of high importance. It is easier to monitor the elephants using aerial imagery, as it does not require human intervention or disturbance in elephants' natural habitat. Elephant Detection using aerial imagery is more efficient when performed over vast areas. This deep learning model helps automate the task of detecting elephants from high-resolution aerial imagery.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution RGB imagery (3-13 centimeters spatial resolution).OutputFeature class containing detected elephants.Applicable geographiesThe model is expected to work well with aerial imagery of southern African forests (South Africa, Botswana, and Namibia) or similar geographies.Model architectureThis model uses the FasterRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.857 for elephant.Training dataThe model has been trained on the The Aerial Elephant Dataset.LimitationsThis model works well only with high-resolution aerial imagery.This model is trained on imagery of African Bush Elephants. However, it detects all kinds of elephants and is species agnosticSample resultsHere are a few result from the model.CitationsNaudé, Johannes J., & Joubert, Deon. (2019). The Aerial Elephant Dataset [Dataset]. Zenodo.

Vegetative Difference Image gives an easy to interpret visual representation of vegetative increase/decrease across 2 time periods.This raster function template is used to generate a visual product. The results cannot be used for analysis. This templates generates an NDVI in the backend, hence it requires your imagery to have the red and near infrared bands. In the resulting image, greens indicate increase in vegetation, while the magenta indicates decrease in vegetationReferences:Raster functionsWhen to use this raster function templateThis template is particularly useful when trying to intuitively visualize the increase or decrease in vegetation over two time periods. How to use this raster function templateIn ArcGIS Pro, search ArcGIS Living Atlas for raster function templates to apply them to your imagery layer. You can also download the raster function template, attach it to a mosaic dataset, and publish it as an image service. This index supports many satellite sensors, such as Landsat-8, Sentinel-2, Quickbird, IKONOS, Geoeye-1, and Pleiades-1.Applicable geographiesThe template uses a standard vegetation which is designed to work globally.

The professional map services market is experiencing robust growth, driven by increasing demand across diverse sectors. The market, estimated at $50 billion in 2025, is projected to maintain a healthy Compound Annual Growth Rate (CAGR) of 10% through 2033, reaching approximately $120 billion by the end of the forecast period. This expansion is fueled by several key factors, including the proliferation of location-based services (LBS), the rise of autonomous vehicles, and the growing need for precise mapping solutions in urban planning, logistics, and infrastructure development. Furthermore, advancements in technologies like GIS (Geographic Information Systems), satellite imagery, and AI-powered mapping tools are significantly enhancing map accuracy, detail, and functionality, further stimulating market demand. Major players like Google, TomTom, Esri, and Mapbox are continuously innovating, pushing the boundaries of map creation and application, and fostering competition that ultimately benefits consumers and businesses. The market segmentation reveals significant opportunities within specialized applications. High-resolution imagery and 3D mapping solutions are witnessing particularly strong growth, driven by increasing investments in infrastructure projects and the adoption of smart city initiatives. While data security and privacy concerns pose potential restraints, the industry is actively addressing these challenges through the development of secure mapping platforms and data encryption techniques. Regional growth is expected to be uneven, with North America and Europe maintaining significant market shares due to their advanced technological infrastructure and high adoption rates. However, emerging economies in Asia-Pacific are showing promising growth potential, fuelled by rapid urbanization and expanding digital infrastructure.

This deep learning model is used to detect and segment trees in high resolution drone or aerial imagery. Tree detection can be used for applications such as vegetation management, forestry, urban planning, etc. High resolution aerial and drone imagery can be used for tree detection due to its high spatio-temporal coverage.This deep learning model is based on DeepForest and has been trained on data from the National Ecological Observatory Network (NEON). The model also uses Segment Anything Model (SAM) by Meta.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS tools.Input8 bit, 3-band high-resolution (10-25 cm) imagery.OutputFeature class containing separate masks for each tree.Applicable geographiesThe model is expected to work well in the United States.Model architectureThis model is based upon the DeepForest python package which uses the RetinaNet model architecture implemented in torchvision and open-source Segment Anything Model (SAM) by Meta.Accuracy metricsThis model has an precision score of 0.66 and recall of 0.79.Training dataThis model has been trained on NEON Tree Benchmark dataset, provided by the Weecology Lab at the University of Florida. The model also uses Segment Anything Model (SAM) by Meta that is trained on 1-Billion mask dataset (SA-1B) which comprises a diverse set of 11 million images and over 1 billion masks.Sample resultsHere are a few results from the model.CitationsWeinstein, B.G.; Marconi, S.; Bohlman, S.; Zare, A.; White, E. Individual Tree-Crown Detection in RGB Imagery Using Semi-Supervised Deep Learning Neural Networks. Remote Sens. 2019, 11, 1309Geographic Generalization in Airborne RGB Deep Learning Tree Detection Ben Weinstein, Sergio Marconi, Stephanie Bohlman, Alina Zare, Ethan P White bioRxiv 790071; doi: https://doi.org/10.1101/790071

An aerial imagery basemap of New Zealand in NZTM using the latest quality data from Land Information New Zealand.LINZ Aerial Imagery Basemap - NZTM WMTS: https://ecan.maps.arcgis.com/home/item.html?id=39cf07ebf8a2413696d8fd4d80570b84This basemap is also available in Web Mercator (WGS 84) from: https://basedatanz.maps.arcgis.com/home/item.html?id=a4ac021a9f6d4976bfb3cc6d34739b8bThe LINZ Aerial Imagery Basemap details New Zealand in high resolution - from a nationwide view all the way down to individual buildings.This basemap combines the latest high-resolution aerial imagery down to 5cm in urban areas and 10m satellite imagery to provide full coverage of mainland New Zealand, Chathams and other offshore islands.LINZ Basemaps are powered by data from the LINZ Data Service and other authoritative open data sources, providing you with a basemap that is free to use under an open licence.A XYZ tile API (Web Mercator only) is also available for use in web and mobile applications.See more information or provide your feedback at https://basemaps.linz.govt.nz/.For attribution requirements and data sources see: https://www.linz.govt.nz/data/linz-data/linz-basemaps/data-attribution.

The remote sensing data acquisition market is experiencing robust growth, driven by increasing demand across various sectors. Technological advancements, particularly in sensor technology (e.g., hyperspectral imaging, LiDAR) and data processing capabilities (e.g., cloud computing, AI-powered analytics), are significantly enhancing the accuracy, speed, and affordability of data acquisition. This is fueling adoption across diverse applications, including precision agriculture (monitoring crop health, optimizing irrigation), environmental monitoring (deforestation tracking, pollution detection), urban planning (infrastructure assessment, traffic management), and defense & security (surveillance, intelligence gathering). The market's expansion is also facilitated by the decreasing cost of drones and satellite imagery, making remote sensing accessible to a broader range of users. While data security and privacy concerns pose challenges, ongoing developments in encryption and data governance are addressing these issues. We estimate the market size in 2025 to be $15 billion, growing at a Compound Annual Growth Rate (CAGR) of 12% from 2025-2033. This projection reflects the sustained growth trajectory across all major application areas. The competitive landscape is characterized by a mix of established players and emerging technology providers. Companies like Esri and Skywatch are leading the way with comprehensive solutions, while smaller firms are innovating in niche areas. The market is also witnessing increased collaboration between technology providers and data users, driving the development of customized solutions. Regional growth varies, with North America and Europe currently holding significant market shares, but the Asia-Pacific region is projected to exhibit the highest growth rate due to increasing government investments in infrastructure development and environmental monitoring initiatives. Continued innovation in sensor technology, improved data processing algorithms, and the increasing accessibility of data through cloud platforms will remain key drivers of future market growth. However, challenges such as the need for skilled professionals and regulatory hurdles related to data usage will continue to shape the market's trajectory.

The U.S. Geological Survey, in partnership with the National Park Service's Colonial National Historic Park (COLO), used commercially available satellite data and soil samples from around Jamestown Island to evaluate vegetative health and soil conditions on the island to further understand the extent and severity of conditions that threaten archaeological sites and vegetation. 50 sites were initially selected for sampling, however, only 48 of the sites were accessible in either June 2021 or March 2022. The soil samples were collected from 2 depths at 48 different sites around the island. The first sample was collected just below the land surface in the O horizon, and the second sample was collected from a minimum of 0.34 ft below the land surface in the A horizon. Two soil sampling efforts were conducted, one in June 2021 and a second in March 2022 to represent drier and wetter times of the year. Measurements of temperature in degrees Celsius, moisture content in percent volume, and soil conductivity in millisiemens per centimeter, were made using a Dynamax WET-2 sensor. Soil pH was also measured using the U.S. Environmental Protection Agency's 9045D method. Satellite imagery, multispectral and panchromatic images, used in the project come from the GeoEYE, QuickBird 2, WorldView 2, and WorldView 3 satellites operated by the European Space Agency and Digital Globe . USGS used panchromatic and multispectral images of Jamestown Island taken from 2010 – 2018 to create Normalized Difference Vegetative Index (NDVI) and difference of NDVI rasters to evaluate vegetative stress across Jamestown Island over time. The images used were acquired using the USGS's Commercial Remote Sensing Space Policy (CRSSP) Imagery Derived Requirements (CIDR) tool. The search terms used for the CIDR request were for multispectral and panchromatic images of Jamestown Island, VA at a standard (2A) processing level with an image resolution of 1-4m, a max cloud cover of 20%, from 05/01/2008 - 07/28/2022. The search returned 12 images, or scenes, of which 4 were used for the associated publication. The collection dates, satellite platform and panchromatic and multispectral ground sample distances (GSD) respectively are as follows: - 11/28/2010 at 16:24 from WorldView 2; GSD 1.509 ft and 5.906 ft - 06/25/2011 at 15:56 by GeoEye; GSD 1.345ft and 5.413 ft - 10/10/2011 by QuickBird 2; GSD 2.001 ft and 7.874 ft - 2/12/2018 at 16:08 by WorldView 3; GSD 1.017 ft and 4.068 ft The multispectral images were pan-sharpened to increase the resolution for visual light rasters of Jamestown Island using ESRI ArcGIS Pro's Pan-Sharpen tool utilizing the Graham-Schmidt method. Additionally, the 4 multispectral images were used to create normalized difference vegetative index rasters using the ESRI ArcGIS Pro NDVI tool. For images with multiple near-infrared (NIR) bands, the first NIR band was used to create the NDVI rasters. A difference of NDVI raster was created using the Raster Calculator tool in ArcGIS Pro to show change in vegetative heath over time. The 11/28/2010 WorldView 2 and 12/12/2018 WorldView 3 NDVI rasters with water removed from the rasters were used to create the difference of NDVI raster. The GSD for the difference of NDVI raster is 5.906 ft. The original multispectral and panchromatic images could not be published in this data release as the rights for those images belong to European Space Agency or Digital Globe. As such only the derived products, the pan-sharpened image, NDVI rasters, and difference of NDVI raster have been published in this data release.

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.

This app is part of Indicators of the Planet. Please see https://livingatlas.arcgis.com/indicatorsThis layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP and NOAA-20 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireNote about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.

This layer presents detectable thermal activity from VIIRS satellites for the last 7 days. VIIRS Thermal Hotspots and Fire Activity is a product of NASA’s Land, Atmosphere Near real-time Capability for EOS (LANCE) Earth Observation Data, part of NASA's Earth Science Data.Consumption Best Practices:

As a service that is subject to very high usage, ensure peak performance and accessibility of your maps and apps by avoiding the use of non-cacheable relative Date/Time field filters. To accommodate filtering events by Date/Time, we suggest using the included "Age" fields that maintain the number of days or hours since a record was created or last modified, compared to the last service update. These queries fully support the ability to cache a response, allowing common query results to be efficiently provided to users in a high demand service environment.When ingesting this service in your applications, avoid using POST requests whenever possible. These requests can compromise performance and scalability during periods of high usage because they too are not cacheable.Source: NASA LANCE - VNP14IMG_NRT active fire detection - WorldScale/Resolution: 375-meterUpdate Frequency: Hourly using the aggregated live feed methodologyArea Covered: WorldWhat can I do with this layer?This layer represents the most frequently updated and most detailed global remotely sensed wildfire information. Detection attributes include time, location, and intensity. It can be used to track the location of fires from the recent past, a few hours up to seven days behind real time. This layer also shows the location of wildfire over the past 7 days as a time-enabled service so that the progress of fires over that timeframe can be reproduced as an animation.The VIIRS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.Fire points in this service are generally available within 3 1/4 hours after detection by a VIIRS device. LANCE estimates availability at around 3 hours after detection, and esri livefeeds updates this feature layer every 15 minutes from LANCE.Even though these data display as point features, each point in fact represents a pixel that is >= 375 m high and wide. A point feature means somewhere in this pixel at least one "hot" spot was detected which may be a fire.VIIRS is a scanning radiometer device aboard the Suomi NPP, NOAA-20, and NOAA-21 satellites that collects imagery and radiometric measurements of the land, atmosphere, cryosphere, and oceans in several visible and infrared bands. The VIIRS Thermal Hotspots and Fire Activity layer is a livefeed from a subset of the overall VIIRS imagery, in particular from NASA's VNP14IMG_NRT active fire detection product. The downloads are automatically downloaded from LANCE, NASA's near real time data and imagery site, every 15 minutes.The 375-m data complements the 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Hotspots and Fire Activity layer; they both show good agreement in hotspot detection but the improved spatial resolution of the 375 m data provides a greater response over fires of relatively small areas and provides improved mapping of large fire perimeters.Attribute informationLatitude and Longitude: The center point location of the 375 m (approximately) pixel flagged as containing one or more fires/hotspots.Satellite: Whether the detection was picked up by the Suomi NPP satellite (N) or NOAA-20 satellite (1) or NOAA-21 satellite (2). For best results, use the virtual field WhichSatellite, redefined by an arcade expression, that gives the complete satellite name.Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel. This value is based on a collection of intermediate algorithm quantities used in the detection process. It is intended to help users gauge the quality of individual hotspot/fire pixels. Confidence values are set to low, nominal and high. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Nominal confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.Please note: Low confidence nighttime pixels occur only over the geographic area extending from 11 deg E to 110 deg W and 7 deg N to 55 deg S. This area describes the region of influence of the South Atlantic Magnetic Anomaly which can cause spurious brightness temperatures in the mid-infrared channel I4 leading to potential false positive alarms. These have been removed from the NRT data distributed by FIRMS.FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).DayNight: D = Daytime fire, N = Nighttime fireHours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.Additional information can be found on the NASA FIRMS site FAQ.Note about near real time data:Near real time data is not checked thoroughly before it's posted on LANCE or downloaded and posted to the Living Atlas. NASA's goal is to get vital fire information to its customers within three hours of observation time. However, the data is screened by a confidence algorithm which seeks to help users gauge the quality of individual hotspot/fire points. Low confidence daytime fire pixels are typically associated with areas of sun glint and lower relative temperature anomaly (<15K) in the mid-infrared channel I4. Medium confidence pixels are those free of potential sun glint contamination during the day and marked by strong (>15K) temperature anomaly in either day or nighttime data. High confidence fire pixels are associated with day or nighttime saturated pixels.RevisionsMarch 7, 2024: Updated to include source data from NOAA-21 Satellite.September 15, 2022: Updated to include 'Hours_Old' field. Time series has been disabled by default, but still available.July 5, 2022: Terms of Use updated to Esri Master License Agreement, no longer stating that a subscription is required!This layer is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page!

Seabirds are found near marine habitats, such as sea and wetlands, due to food availability. They mostly feed on fish and insects. The seabird population is declining at a much faster rate compared to other birds as the coastal region is sensitive to pollution, commercial fisheries, habitat degradation, mineral extraction, human disturbance, etc. Seabirds are also endangered by predatory species from both land and water. Apart from the geography of their habitat, they do not have much ability to defend their nest or protect their young ones. Breeding and laying of eggs happen in open habitats, such as bare ground and open sandy or rocky areas, on coasts and islands with little or no nest material.The Royal tern and Caspian tern are two of the 350 odd seabird species. These adult terns could be of size 45-60 cm weighing 350-750 gm. Their size puts them in the category of small objects and thus we need very high-resolution imagery to detect them. Recent innovations in drones and AI have enabled us to capture high-resolution imagery over a large geographic area and detect objects of different shapes and sizes. Drones also decrease the disturbance to bird population. Drones are easier to deploy and can perform frequent surveys even after disasters like hurricanes, oil spills, etc. This deep learning model helps automate the task of detecting seabirds (Royal and Caspian terns) from high-resolution aerial imagery. This can help in mapping effective site protection areas for seabirds.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.InputHigh resolution RGB imagery (1.0 cm resolution). OutputFeature class containing detected seabirds.Applicable geographiesThe model is expected to work well with aerial imagery of West African coast or similar geographies.Model architectureThis model uses the Mask R-CNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.76 for seabird.Training dataThe model has been trained on the Aerial Seabirds West Africa.Limitations This model works well only with very high-resolution aerial imagery. It is trained on imagery of colonies of Royal and Caspian tern species in a coastal region.Sample resultsHere are a few results from the model. CitationsKellenberger B, Veen T, Folmer E, Tuia D. 21,000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning. Remote Sensing in Ecology and Conservation. 2021.

The Spain Geospatial Imagery Analytics Market is experiencing robust growth, projected to reach a significant size within the forecast period (2025-2033). Driven by increasing adoption of advanced technologies like AI and machine learning in various sectors, the market is witnessing a Compound Annual Growth Rate (CAGR) of 23%. This growth is fueled by the increasing need for precise and timely geospatial data across diverse verticals. Key sectors such as agriculture, insurance, defense and security, and environmental monitoring are adopting geospatial imagery analytics for improved efficiency and informed decision-making. The market is segmented by type (imagery and video analytics), deployment mode (on-premise and cloud), organization size (SMEs and large enterprises), and vertical. The cloud deployment model is gaining traction due to its scalability and cost-effectiveness. Large enterprises are leading the adoption, but SMEs are increasingly recognizing the value proposition of geospatial analytics, further driving market expansion. While data limitations prevent precise Spain-specific figures, the overall market trends and CAGR strongly suggest substantial growth potential in the Spanish market, mirroring global trends. The competitive landscape features both established tech giants like Google, Microsoft, and Oracle, and specialized geospatial analytics companies such as Hexagon AB, ESRI, and Trimble. The presence of these players ensures a dynamic market with continuous innovation and competitive pricing. The market's future trajectory hinges on continued technological advancements, government support for digital transformation initiatives within various sectors, and the increasing availability of high-resolution satellite and aerial imagery. Challenges include data privacy concerns, the need for skilled professionals, and ensuring data interoperability. Despite these challenges, the Spain Geospatial Imagery Analytics Market is poised for considerable expansion throughout the forecast period, providing significant opportunities for both established players and emerging market entrants. Recent developments include: July 2023: Databricks is the provider of a big data tool named Databricks Lakehouse Platform, which merges data science, data engineering, machine learning, and analytics within a single platform. To provide even more valuable insights to data scientists, spatial analytics is often added to the mix to put large amounts of data in the proper context. A new partnership with Esri brings advanced spatial analytics capabilities in Esri’s ArcGIS software to the Databricks Lakehouse Platform, allowing users to perform spatial analytics at scale., June 2023: Meta Platforms, the parent organization of Facebook and Instagram, announced its plans to grant researchers access to components of its new "human-like" artificial intelligence (AI) model. This advanced model, named I-JEPA, boasts superior accuracy in analyzing and completing unfinished images compared to existing models. Unlike other generative AI models primarily focusing on neighboring pixels, I-JEPA utilizes background knowledge about the world to fill in the missing portions of images.. Key drivers for this market are: Increasing Adoption of Location-based Services, Increasing Demand for Safe and Secure Mining Operations. Potential restraints include: Increasing Adoption of Location-based Services, Increasing Demand for Safe and Secure Mining Operations. Notable trends are: Cloud Segment is Expected to Hold a Significant Share of the Market.

The Argentina Satellite Imagery Services market, valued at $40 million in 2025, is projected to experience robust growth, driven by increasing government investments in infrastructure development, particularly within the geospatial data acquisition and mapping sectors. The rising demand for precise location intelligence across various applications, including natural resource management, surveillance and security, and disaster management, further fuels market expansion. Key applications like precision agriculture and urban planning are also contributing to market growth, as businesses and government agencies leverage satellite imagery for improved decision-making and resource optimization. The presence of established players like ESRI and Airbus, alongside emerging local firms, indicates a competitive yet dynamic market landscape. However, challenges remain, primarily concerning data accessibility, affordability for smaller businesses, and potential regulatory hurdles related to data privacy and security. The construction, transportation, and logistics sectors are expected to witness significant growth in satellite imagery adoption due to the need for efficient infrastructure planning and risk mitigation. Furthermore, the expanding military and defense applications are expected to contribute to market expansion throughout the forecast period. While specific data for Argentina's market segmentation is unavailable, the overall market trajectory mirrors global trends, projecting a Compound Annual Growth Rate (CAGR) of 6.66% from 2025 to 2033. This growth is expected to be further fueled by technological advancements in satellite imagery resolution and analytics. The consistent 6.66% CAGR signifies a steady increase in demand for advanced geospatial solutions. Government initiatives promoting digitalization and smart city development are key catalysts, driving adoption across various sectors. While the market faces challenges, such as high initial investment costs for technology and infrastructure, the long-term benefits of improved decision-making and operational efficiencies outweigh these barriers. The market is expected to mature gradually, with a shift towards cloud-based solutions and advanced analytics becoming increasingly prevalent. The presence of both international and domestic players ensures a competitive market fostering innovation and affordability. This combination of factors positions Argentina's satellite imagery services market for sustained growth in the coming years. Recent developments include: July 2023: Maxar Technologies, a leading provider of comprehensive space services and secure, precise geospatial intelligence, announced the initial launch of its innovative Maxar Geospatial Platform (MGP). This groundbreaking platform offers rapid and user-friendly access to the world's most advanced Earth intelligence. MGP is set to revolutionize geospatial data and analytics by simplifying discovery, procurement, and integration processes. Users of MGP will enjoy seamless access to Maxar's renowned geospatial content, which includes high-resolution satellite imagery, breathtaking imagery base maps, intricate 3D models, analysis-ready datasets, as well as image-based change detection and analytical outputs., March 2023: The Argentinean remote sensing constellation SAOCOM has contributed invaluable data, and the European Space Agency (ESA) engaged Earth observation experts to explore and propose innovative applications for this dataset. The Argentine space agency CONAE, responsible for overseeing and controlling the SAOCOM satellites, is actively working on requests for data delivery proposals. The SAOCOM mission, an integral part of ESA's Third Party Missions program, features two spacecraft, SAOCOM 1A and 1B, designed to collect polarimetric L-band synthetic aperture radar data., , . Key drivers for this market are: Increasing Adoption of Location-based Services, Satellite data usage is increasing. Potential restraints include: Increasing Adoption of Location-based Services, Satellite data usage is increasing. Notable trends are: Natural Resource Management is Expected to Significant Share.

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 geospatial solutions market is experiencing robust growth, projected to reach a substantial market size of $348.8 billion in 2025. While the provided CAGR is missing, considering the rapid advancements in technologies like AI, IoT, and cloud computing driving the adoption of geospatial solutions across diverse sectors, a conservative estimate of the Compound Annual Growth Rate (CAGR) for the forecast period (2025-2033) would be around 8%. This growth is fueled by increasing demand for precise location intelligence in various applications, including utility management, business operations optimization, advanced transportation systems, defense and intelligence initiatives, infrastructure development, and natural resource exploration. The market is segmented by hardware, software, services, and applications, each showing significant growth potential. Hardware components like GPS receivers and sensors are witnessing strong demand, while software solutions are expanding in sophistication to incorporate advanced analytics and AI-powered capabilities. The services segment comprises data acquisition, processing, and analysis, fueling the industry's overall growth trajectory. Key players such as HERE Technologies, Esri, and Hexagon are driving innovation and market expansion through strategic partnerships and technological advancements. The geographic distribution shows strong demand across North America, Europe, and Asia-Pacific, with the latter expected to emerge as a key growth driver due to rapid urbanization and infrastructure development. The market's sustained growth is expected to continue into the future, driven by several factors. The increasing availability of high-resolution satellite imagery and aerial data, coupled with advancements in data processing and analytics, is empowering businesses and governments to make more informed decisions based on location-specific insights. Moreover, the growing adoption of cloud-based geospatial platforms is reducing the cost and complexity of implementing geospatial solutions, further stimulating market expansion. Challenges such as data privacy concerns and the need for skilled professionals to handle complex geospatial data remain; however, the overall growth outlook for the geospatial solutions market remains highly positive, promising significant opportunities for stakeholders across the value chain.

The Israel Geospatial Analytics market is experiencing robust growth, projected to reach $1.69 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 5.93% from 2025 to 2033. This expansion is fueled by increasing government investments in advanced infrastructure projects, a surge in the adoption of precision agriculture techniques, and a growing demand for accurate mapping and location-based services across various sectors including defense, urban planning, and environmental monitoring. The market's evolution is further driven by the proliferation of high-resolution satellite imagery, the development of sophisticated analytical tools powered by artificial intelligence (AI) and machine learning (ML), and the increasing availability of readily accessible geospatial data. Key players such as SAS Institute Inc., General Electric Company, Esri Inc., and others contribute to this dynamic market landscape through innovative solutions and technological advancements. The market segmentation, while not explicitly detailed, likely includes solutions categorized by data acquisition methods (e.g., satellite imagery, LiDAR), analytical capabilities (e.g., spatial statistics, predictive modeling), and industry verticals (e.g., agriculture, defense, utilities). The continued growth hinges on factors such as technological innovation, the increasing affordability of geospatial technologies, and the government's continued support for digital transformation initiatives. Potential restraints could include data privacy concerns, the need for skilled professionals, and the integration complexities associated with various data sources. Despite these challenges, the long-term outlook for the Israel Geospatial Analytics market remains positive, driven by the ever-increasing need for location-intelligence across all aspects of modern life and business. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: High Costs and Operational Concerns, Concerns related to Geoprivacy and Confidential Data. Notable trends are: Surface Analysis is Expected to Hold Significant Share of the Market.

This is a fine-tuned model for New Zealand, derived from a pre-trained model from Esri. It has been trained using LINZ aerial imagery (0.075 m spatial resolution) for Wellington You can see its output in this app https://niwa.maps.arcgis.com/home/item.html?id=1ca4ee42a7f44f02a2adcf198bc4b539Solar power is environment friendly and is being promoted by government agencies and power distribution companies. Government agencies can use solar panel detection to offer incentives such as tax exemptions and credits to residents who have installed solar panels. Policymakers can use it to gauge adoption and frame schemes to spread awareness and promote solar power utilization in areas that lack its use. This information can also serve as an input to solar panel installation and utility companies and help redirect their marketing efforts.Traditional ways of obtaining information on solar panel installation, such as surveys and on-site visits, are time consuming and error-prone. Deep learning models are highly capable of learning complex semantics and can produce superior results. Use this deep learning model to automate the task of solar panel detection, reducing time and effort required significantly.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS Proor ArcGIS Enterprise – ArcGIS Image Server with Raster Analytics configuredor ArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the Esri guide to using their USA Solar Panel detection model (https://www.arcgis.com/home/item.html?id=c2508d72f2614104bfcfd5ccf1429284). Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (5-15 cm) RGB imageryOutputFeature class containing detected solar panelsApplicable geographiesThe model is expected to work well in New ZealandModel architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.9244444449742635NOTE: Use at your own risk_Item Page Created: 2022-02-09 02:24 Item Page Last Modified: 2025-04-05 16:30Owner: NIWA_OpenData