The size of the Italy Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.17% during the forecast period. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: High Costs and Operational Concerns, Lack of Standardization for Data Integration. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Time Aware is a configurable app template that enables you visualize time enabled layers in a web map using a time slider. This is useful for displaying changes in data over time. Use CasesBuild a stand alone app that presents data changing through time.Build a time aware app and embed it within a story map journal or story map series to include time animation within your story.Configurable OptionsChoose a title, logo, and color scheme.Configure the ability for feature and location search.Customize the color and date time format of the time slider.Enable a legend, scalebar, share dialog, or about window.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis requires time aware data, to learn more see the configure time help topic. An existing time aware feature service can be consumed from this application, however in order to create your own time aware feature service you will either need ArcGIS Enterprise or an ArcGIS Online subscription.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

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.

Points to ArcGIS Server Map Service, which is updated weekly from the Enterprise geodatabase.

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.The boundaries available are: (BUC) Ultra generalised (500m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022

E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022

E37000051
London has changed it’s name to The London Economic Action Partnership on the 31st December 2022

E37000053
Oxfordshire has changed it’s name to OxLEP on the 31st December 2022

E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022

E37000059
Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022

REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BUC_V2/FeatureServer

REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BUC_V2/WFSServer?service=wfs&request=getcapabilities

REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BUC_V2/MapServer

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.The boundaries available are: (BUC) Ultra generalised (500m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.

REST URL of Feature Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/MapServer

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

The DWR Enterprise image server has hundreds of image services, but there is no interface for searching or querying the server. The image server index contains footprints of the geographic extent of each available image service, as well as relevant attributes that describe the image service. There are also related tables for most types of image services that contain information specific to that type of data, such as specification numbers for design drawings or beam types for bathymetry data.

Discover the booming Italian Geospatial Analytics market! Explore its €260 million (2025) valuation, 8.17% CAGR, key drivers, trends, and leading players like ESRI and Hexagon AB. This in-depth analysis projects market growth through 2033 across sectors including agriculture, defense, and utilities. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

The boundaries available are: (BGC) Generalised (20m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022 REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BGC_V2/FeatureServer REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BGC_V2/WFSServer?service=wfs&request=getcapabilities REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BGC_V2/MapServer

The boundaries available are: (BFE) Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022 REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/FeatureServer REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BFE_V2/WFSServer?service=wfs&request=getcapabilities REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/MapServer

NIWA's bathymetry model of New Zealand at a 250m resolution. The 2016 model is a compilation of data digitised from published coastal charts, digital soundings archive, navy collector sheets and digital multibeam data sourced from surveys by NIWA, LINZ, as well as international surveys by vessels from United States of America, France, Germany, Australia, and Japan. All data used is held at NIWA.Image service can be used for analysis in ArcGIS Desktop or ArcGIS Online - no need to download the data, just stream using this service and classify, symbolise, mask, extract or apply map algebra - just like you would with local raster files. https://enterprise.arcgis.com/en/server/latest/publish-services/windows/key-concepts-for-image-services.htmMap information and metadata Offshore representation was generated from digital bathymetry at a grid resolution of 250m. Sun illumination is from an azimuth of 315° and 45° above the horizon.Projection Mercator 41 (WGS84 datum). EPSG: 3994Scale 1:5,000,000 at 41°S. Not to be used for navigational purposes Bibliographic reference Mitchell, J.S., Mackay, K.A., Neil, H.L., Mackay, E.J., Pallentin, A., Notman P., 2012. Undersea New Zealand, 1:5,000,000. NIWA Chart, Miscellaneous Series No. 92Further Information: https://www.niwa.co.nz/our-science/oceans/bathymetry/further-informationLicence: https://www.niwa.co.nz/environmental-information/licences/niwa-open-data-licence-by-nn-nc-sa-version-1_Item Page Created: 2017-11-01 00:55 Item Page Last Modified: 2025-04-05 18:48Owner: NIWA_OpenData

The DWR Enterprise image server has hundreds of image services, but there is no interface for searching or querying the server. The image server index contains footprints of the geographic extent of each available image service, as well as relevant attributes that describe the image service. There are also related tables for most types of image services that contain information specific to that type of data, such as specification numbers for design drawings or beam types for bathymetry data.

Licitación pública del sector Licencias en Zaragoza - Licitación 3. Oportunidad de contratación pública con organismo contratante Administración Pública.

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.

Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelBefore 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.Input1. 8-bit, 3-band high-resolution (10 cm) imagery. The model was trained on 10 cm Vexcel imagery2. Building footprints feature classOutputFeature class containing classified building footprints. Classname field value 1 indicates damaged buildings, and value 2 corresponds to undamaged structuresApplicable geographiesThe model was specifically trained and tested over Maui, Hawaii, in response to the Maui fires in August 2023.Accuracy metricsThe model has an average accuracy of 0.96.Sample resultsResults of the models can be seen in this dashboard.

Updated: As of 7/3/2021 the Arizona Department of Health Services is no longer updated its vaccination data. This item has been deprecated as a result.This table provides a daily log of confirmed COVID-19 vaccinations by Zip Code for the state of Arizona. Data are provided by the Arizona Department of Health Services (ADHS). Data Source: Arizona Department of Health Services (AZDHS) daily COVID-19 vaccinations report by zip code (https://experience.arcgis.com/experience/bcf70a0f5cac4262a411166dbcac9053). Daily Change is calculated by taking the current day’s vaccination value for a given Postal Code and subtracting the prior day’s value. This resulting value is the Daily Change. Based on reporting from ADHS Daily Change may be a positive or negative number or 0 if no change has been reported. Arizona Department of Health Services (AZDHS) data are scheduled for daily updates at 9:00 AM (COVID-19 cases) and 12:00 PM (COVID-19 vaccinations), but the times when the AZDHS releases that days COVID-19 cases and vaccinations may vary. City of Tempe data are updated each afternoon at 3:00 PM to allow for possible AZDHS delays. When there are AZDHS delays in updating the daily data, dashboard data updates may be delayed by 24 hours. The charts and daily values list can be used to confirm the date of the most recent counts on the COVID-19 cases and vaccinations dashboards. If data are not released by the time of the scheduled daily dashboard refresh, that day's values may appear on the dashboard as an addition to the next day's value.---------------------------------------------------Please also see the following items for up-to-date COVID-19 vaccination data:COVID-19 Vaccination Rates by Zip Code (Maricopa County)https://data.tempe.gov/datasets/covid-19-vaccination-rates-by-zip-code-maricopa-county/exploreCOVID-19 Vaccination Rates by City (Maricopa County)https://data.tempe.gov/datasets/covid-19-vaccination-rates-by-city-maricopa-county/explore ---------------------------------------------------Additional InformationSource: Arizona Department of Health Services (AZDHS) daily COVID-19 vaccinations report by zip code (https://experience.arcgis.com/experience/bcf70a0f5cac4262a411166dbcac9053)Contact (author): n/aContact E-Mail (author): n/aContact (maintainer): City of Tempe Open Data TeamContact E-Mail (maintainer): data@tempe.govData Source Type: TablePreparation Method: Data are exposed via ArcGIS Server and its REST API.Publish Frequency: DailyPublish Method: Data are downloaded each afternoon once ADHS updates its public API. Data are transformed and appended to a table in Tempe’s Enterprise GIS.Data Dictionary