Parcel editing involves working with land parcel boundaries and land records information. Other data, such as utility features, may also be related to your land records. When editing parcels, you are working with point, line, and polygon features. These features represent the coordinates of the parcel (points), the parcel boundary (lines), and the parcel feature (polygon).The location of many utilities such as roads, water, and electrical networks are often dependent on the parcel boundary. If parcel boundaries are updated, dependent utilities should also be updated.The parcel editing environment in ArcGIS Desktop 10 provides an intelligent editing environment that is designed specifically for working with land parcels and their related survey information.After completing this course, you will be able to:Define a parcel fabric.Describe the benefits of using a parcel fabric.Apply the steps of the parcel editing workflow.Use a parcel fabric to manage land records data.Create new parcels using the Parcel Editor.

Prior experience of GIS is variable, but a number of PGCE students and in-service teachers reported negative prior experiences with geospatial technology. Common complaints include a course focussed on data students found irrelevant, with learning exercises in the form of list-like instructions. The complexity of desktop GIS software is also often mentioned as off-putting.

In this seminar, you will learn how to use the ArcGIS Data Reviewer Positional Accuracy Assessment Tool (PAAT) to assess data accuracy and interpret the results. You will learn how to embed data validation across workflows, influencing decisions relating to data and enhancing the accuracy of data across your organization.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Basic, Standard, or Advanced)ArcGIS Data Reviewer for Desktop

In this seminar, the presenter introduces essential concepts of ArcGIS Data Reviewer and highlights automated and semi-automated methods to streamline and expedite data validation.This seminar was developed to support the following:ArcGIS Desktop 10.3 (Basic, Standard, or Advanced)ArcGIS Server 10.3 Workgroup (Standard Or Advanced)ArcGIS Data Reviewer for DesktopArcGIS Data Reviewer for Server

Parcel editing involves working with land parcel boundaries and land records information. Creating new parcels and updating parcels are common editing tasks. Updates to your land records data should reflect the real world and, at the same time, maintain a record of historic boundaries and associated information.In this course, you will learn how to use the Parcel Editor tools to update the spatial and attribute information for existing parcels while ensuring the topological integrity of the parcel fabric. You will also learn how to import parcel lines and CAD data to create new parcels and construct multipart parcels to accurately represent real-world parcel features.The parcel editing environment in ArcGIS Desktop 10 provides an intelligent editing environment that is designed specifically for working with land parcels and their related survey information.After completing this course, you will be able to:Update parcel geometry and attributes.Work with multi-part parcels.Manage parcel history.Use other data as a source for parcel lines.Create new parcels using the Parcel Editor.

This deep learning model is used to detect trees in low-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 MaskRCNN and has been trained on data from the DM Dataset preprocessed and collected by the IST Team.

There is no need of high-resolution imagery you can perform all your analysis on low resolution imagery by detecting the trees with the accuracy of 75% and finetune the model to increase your performance and train on your own data.

Licensing requirements ArcGIS Desktop – ArcGIS Image Analyst and ArcGIS 3D Analyst extensions for ArcGIS Pro ArcGIS Enterprise – ArcGIS Image Server with raster analytics configured ArcGIS Online – ArcGIS Image for ArcGIS Online

Using the model Follow 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.

Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.

Input 3-band low-resolution (70 cm) satellite imagery.

Output Feature class containing detected trees

Applicable geographies The model is expected to work well in the U.A.E.

Model architecture This model is based upon the MaskRCNN python package and uses the Resnet-152 model architecture implemented in pytorch.

Training data This model has been trained on the Satellite Imagery created and Labelled by the team and validated on the different locations with more diverse locations.

Accuracy metrics This model has an average precision score of 0.45.

Sample results Here are a few results from the model.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks 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.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

Water bodies are a key element in the landscape. This layer provides a global map of large water bodies for use inlandscape-scale analysis. Dataset SummaryThis layer provides access to a 250m cell-sized raster of surface water created by extracting pixels coded as water in the Global Lithological Map and the Global Landcover Map. The layer was created by Esri in 2014. Analysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometerson a side or an area approximately the size of Europe.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many otherbeautiful and authoritative maps on hundreds of topics. Geonetis a good resource for learning more aboutlandscape layers and the Living Atlas of the World. To get started see theLiving Atlas Discussion Group. TheEsri Insider Blogprovides an introduction to the Ecophysiographic Mapping project.

The arrangement of water in the landscape affects the distribution of many species including the distribution of humans. This layer provides a landscape-scale estimate of the distance from large water bodies. This layer provides access to a 250m cell-sized raster of distance to surface water. To facilitate mapping, the values are in units of pixels. To convert this value to meters multiply by 250. The layer was created by extracting surface water values from the World Lithology and World Land Cover layers to produce a surface water layer. The distance from water was calculated using the ArcGIS Euclidian Distance Tool. The layer was created by Esri in 2014. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

These products were developed to provide scientific and correspondingly spatially explicit information regarding the distribution and abundance of conifers (namely, singleleaf pinyon (Pinus monophylla), Utah juniper (Juniperus osteosperma), and western juniper (Juniperus occidentalis)) in Nevada and portions of northeastern California. Encroachment of these trees into sagebrush ecosystems of the Great Basin can present a threat to populations of greater sage-grouse (Centrocercus urophasianus). These data provide land managers and other interested parties with a high-resolution representation of conifers across the range of sage-grouse habitat in Nevada and northeastern California that can be used for a variety of management and research applications. We mapped conifer trees at 1 x 1 meter resolution across the extent of all Nevada Department of Wildlife Sage-grouse Population Management Units plus a 10 km buffer. Using 2010 and 2013 National Agriculture Imagery Program digital orthophoto quads (DOQQs) as our reference imagery, we applied object-based image analysis with Feature Analyst software (Overwatch, 2013) to classify conifer features across our study extent. This method relies on machine learning algorithms that extract features from imagery based on their spectral and spatial signatures. Conifers in 6230 DOQQs were classified and outputs were then tested for errors of omission and commission using stratified random sampling. Results of the random sampling were used to populate a confusion matrix and calculate the overall map accuracy of 84.3 percent. We provide 5 sets of products for this mapping process across the entire mapping extent: (1) a shapefile representing accuracy results linked to our mapping subunits; (2) binary rasters representing conifer presence or absence at a 1 x 1 meter resolution; (3) a 30 x 30 meter resolution raster representing percentage of conifer canopy cover within each cell from 0 to 100; (4) 1 x 1 meter resolution canopy cover classification rasters derived from a 50 meter radius moving window analysis; and (5) a raster prioritizing pinyon-juniper management for sage-grouse habitat restoration efforts. The latter three products can be reclassified into user-specified bins to meet different management or study objectives, which include approximations for phases of encroachment. These products complement, and in some cases improve upon, existing conifer maps in the western United States, and will help facilitate sage-grouse habitat management and sagebrush ecosystem restoration. These data support the following publication: Coates, P.S., Gustafson, K.B., Roth, C.L., Chenaille, M.P., Ricca, M.A., Mauch, Kimberly, Sanchez-Chopitea, Erika, Kroger, T.J., Perry, W.M., and Casazza, M.L., 2017, Using object-based image analysis to conduct high-resolution conifer extraction at regional spatial scales: U.S. Geological Survey Open-File Report 2017-1093, 40 p., https://doi.org/10.3133/ofr20171093. References: ESRI, 2013, ArcGIS Desktop: Release 10.2: Environmental Systems Research Institute. Overwatch, 2013, Feature Analyst Version 5.1.2.0 for ArcGIS: Overwatch Systems Ltd.

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

This New Zealand solar panel detection Deep Learning Package can detect solar panels from high resolution imagery. This model is trained on high resolution imagery from New Zealand.Solar power is environmentally 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 ProArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing 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.When using the Detect Objects using Deep Learning geoprocessing tool, ticking the Non Maximum Suppression box is recommended, for reference a Max Overlap Ratio of 0.3 was used for the example images below. Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputHigh resolution (7.5 cm) RGB imagery.OutputFeature class containing detected solar panels.Applicable geographiesThe model is expected to work well in New Zealand.Model architectureThis model uses the MaskRCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThis model has an average precision score of 0.83.Sample resultsSome results from the model are displayed below: To learn how to use this model, see this story

Soils and soil moisture greatly influence the water cycle and have impacts on runoff, flooding and agriculture. Soil type and soil particle composition (sand, clay, silt) affect soil moisture and the ability of the soil to retain water. Soil moisture is also affected by levels of evaporation and plant transpiration, potentially leading to near dryness and eventual drought.Measuring and monitoring soil moisture can ensure the fitness of your crops and help predict or prepare for flash floods and drought. The GLDAS soil moisture data is useful for modeling these scenarios and others, but only at global scales. Dataset SummaryThe GLDAS Soil Moisture layer is a time-enabled image service that shows average monthly soil moisture from 2000 to the present at four different depth levels. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. The GLDAS soil moisture data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Depth: This layer has four depth levels. By default they are summed, but you can view each using the multidimensional filter. You must disable time animation on the layer before using its multidimensional filter. It is also possible to toggle between depth layers using raster functions, accessed through the Image Display tab.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

The Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) dataset provides a 7.5 arcsecond (approximately 250 meter resolution) digital elevation model with world-wide coverage at a resolution suitable for regional to continental scale analyses. This layer provides access to a 250m cell-sized raster created from the Global Multi-resolution Terrain Elevation Data 2010 7.5 arcsecond mean elevation product. The dataset represents a compilation and synthesis of 11 different existing raster data sources. The data were published in 2011 by the USGS and the National Geospatial-Intelligence Agency. The dataset is documented in the publication: Danielson and Gesch. 2011. Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010). U.S. Geological Survey Open-File Report 2011–1073, 26 p. Dataset SummaryAnalysis: Restricted single source analysis. Maximum size of analysis is 16,000 x 16,000 pixels. What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Restricted single source analysis means this layer has size constraints for analysis and it is not recommended for use with other layers in multisource analysis. This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks. The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics. Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group. The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Geographic Information Systems (GIS) platform market is poised for substantial growth, projected to reach an estimated market size of $XXX million in 2025, with a Compound Annual Growth Rate (CAGR) of XX% expected throughout the forecast period of 2025-2033. This robust expansion is primarily driven by the increasing demand for sophisticated data visualization, spatial analysis, and location-based services across a multitude of sectors. The government and utilities sector is a significant contributor, leveraging GIS for infrastructure management, urban planning, resource allocation, and emergency response. Commercial applications are also rapidly adopting GIS for customer analytics, supply chain optimization, real estate development, and targeted marketing. The proliferation of web-enabled GIS solutions, including Web Map Services, is democratizing access to geospatial data and tools, fostering innovation and wider adoption beyond traditional GIS professionals. Desktop GIS continues to hold its ground for complex analytical tasks, but the trend towards cloud-based and mobile GIS solutions is accelerating, offering greater flexibility and scalability. Key trends shaping the GIS platform market include the integration of Artificial Intelligence (AI) and Machine Learning (ML) for advanced spatial analytics and predictive modeling, the growing importance of real-time data processing and streaming, and the rise of open-source GIS solutions challenging established players. The increasing availability of high-resolution satellite imagery and IoT sensor data further fuels the need for powerful GIS platforms. However, certain restraints might temper this growth, such as the initial cost of implementation for some advanced solutions, a potential shortage of skilled GIS professionals, and data privacy concerns associated with extensive location data collection. The market is characterized by intense competition among established global players and emerging innovators, all vying to capture market share by offering comprehensive, user-friendly, and technologically advanced GIS solutions. This comprehensive report delves into the dynamic Geographic Information Systems (GIS) Platform market, providing in-depth analysis and forecasts from 2019 to 2033, with a base year of 2025. The study meticulously examines market concentration, key trends, regional dominance, product insights, and the driving forces and challenges shaping this vital industry. We project the market to reach values in the tens of millions and hundreds of millions of dollars across various segments.

This map features the World Ecophysiographic Land Units 2015 layer, focused on the Caribbean. Ecological Land Units are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. Click on the map to learn more about these components for a given location.Ecological Land Units (ELUs) are areas of distinct bioclimate, landform, lithology, and land cover that form the basic components of terrestrial ecosystem structure. The ELU layer was produced by combining the values in four 250-m cell-sized rasters using the ArcGIS Combine tool (Spatial Analyst). In 2015 these four components resulted in 3,639 different combinations or ELUs, which is 284 fewer than 2014 which used older land cover and a different landform methodology.Note: This layer is designed for use as a geoprocessing input layer and to support pop-ups in ArcGIS Online. Because of the large number of unique values in the image service, the legend cannot be used in a meaningful way. Use the World Ecological Land Units Map 2015 tiled map layer for mapping and visualization. These four component datasets represent the most accurate, current, globally comprehensive, and finest spatial and thematic resolution data available for each of the four inputs. Values for each of the four input layers are listed in the table below. BioclimateLandformsLithologyLand CoverArcticPlainsUndefinedBare AreaCold DryHillsUnconsolidated SedimentSparse VegetationCold Semi-DryMountainsCarbonate Sedimentary RockGrassland, Shrub, or ScrubCold Moist Mixed Sedimentary RockMostly CroplandCold Wet Non-Carbonate Sedimentary RockMostly Needleleaf/Evergreen ForestCool Dry EvaporiteMostly Deciduous ForestCool Semi-Dry PyroclasticsSwampy or Often FloodedCool Moist Metamorphic RockArtificial or Urban AreaCool Wet Acidic VolcanicsSurface WaterHot Dry Acidic PlutonicsUndefinedHot Semi-Dry Non-Acidic Volcanics Hot Moist Non-Acidic Plutonics Hot Wet Warm Dry Warm Semi-Dry Warm Moist Warm Wet Dataset SummaryThis layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it cannot be symbolized and displays as an all black layer. To use in pop-ups set the transparency to 100% and configure the pop-up. The pop-up from this layer can be combined with the World Ecological Land Units Map.Layers providing access to the four input layers used to create this map see the following links:World BioclimatesWorld Landforms Improved Hammond MethodWorld LithologyWorld Land Cover ESA 2010The ecophysiographic facets layer is available here and a layer summarizing the local diversity of the ecophysiographic facets is available here. A service is available to the data tables associated with this and other global layers. These data table services can be used by developers to create custom applications. For more information see the World Ecophysiographic Tables.The layer was created by the USGS and Esri in 2015.For more information see the publication:Sayre and others. 2014. A New Map of Global Ecological Land Units — An Ecophysiographic Stratification Approach. Washington, DC: Association of American Geographers. 46 pages. Available onlineWhat can you do with this layer?This layer is suitable for analysis and can be used in ArcGIS Online to support pop-ups. It can be used in ArcGIS Desktop. Because of the large number of unique values in the image service it can not be symbolized and displays as an all white layer. To use in pop-ups set the transparency to 100% and configure the pop-up.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

Melting snowpack is a key part of the spring water budget in many parts of the world. Like a natural reservoir, snowpack stores winter precipitation and releases it as runoff over the course of many months. Where summer rains are scarce snowpack provides crucial base flow without which rivers might go dry. Where summer rains are torrential, this exacerbates the flooding and can lead to the loss of lives. This map contains a historical record showing the water stored in snowpack during each month from March 2000 to the present. It is not a map of snow depth, but of snow water equivalent, which is the amount of water that would be produced if all the snow melted. For fresh snow, this can be anywhere from 5% to 20% the depth of the snow, depending on temperature (snow tends to be fluffier at lower temperatures). As the snow settles and melts, it becomes more dense, up to 40% or 50% in the spring. Temperature, albedo (the reflective property of the snow), density, and volume all affect the melting rate of the snowpack. Additionally, melting rate is influenced by wind, relative humidity, air temperature and solar radiation.Dataset SummaryThe GLDAS Snowpack layer is a time-enabled image service that shows average monthly snowpack from 2000 to present, measured in millimeters of snow water equivalent. It is calculated by NASA using the Noah land surface model, run at 0.25 degree spatial resolution using satellite and ground-based observational data from the Global Land Data Assimilation System (GLDAS-1). The model is run with 3-hourly time steps and aggregated into monthly averages. Review the complete list of model inputs, explore the output data (in GRIB format), and see the full Hydrology Catalog for all related data and information!What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. Is useful for scientific modeling, but only at global scales. The GLDAS snowpack data is useful for modeling, but only at global scales. Time: This is a time-enabled layer. It shows the total evaporative loss during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the sum of all months in the time extent. Minimum temporal resolution is one month; maximum is one year.Important: You must switch from the cartographic renderer to the analytic renderer in the processing template tab in the layer properties window before using this layer as an input to geoprocessing tools.This layer has query, identify, and export image services available. This layer is part of a larger collection of earth observation maps that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about earth observations layers and the Living Atlas of the World. Follow the Living Atlas on GeoNet.

Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map.Dataset Summary This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are:Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000.Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The global Geographic Information Systems (GIS) Platform market is projected to reach a value of USD 4,078.2 million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The growth of the market is driven by increasing adoption of GIS technology in various industries, including government and utilities, commercial use, and others. Desktop GIS and Web Map Service GIS are the two major types of GIS platforms available in the market, with desktop GIS holding a larger market share due to its advanced capabilities and features. Key trends in the GIS platform market include the rise of cloud-based GIS solutions, the integration of artificial intelligence (AI) and machine learning (ML) technologies, and the growing demand for location-based data and services. The market is also influenced by regional factors, with North America and Europe holding significant market shares due to the presence of key players and the adoption of advanced GIS technologies. Asia Pacific is expected to witness the fastest growth in the coming years due to the increasing demand for GIS solutions in rapidly developing countries. Key market participants include Environmental Systems Research Institute, Hexagon, Pitney Bowes, SuperMap, Bentley System, GE, GeoStar, Zondy Crber, and others.