A catalogue service that conforms to the HTTP protocol binding of the OpenGIS Catalogue Service ISO Metadata Application Profile specification (version 2.0.2)

ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The primary ENC data in this web service was downloaded from NOAA's public site. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. This map service is specifically configured to use the new custom symbology option in MCS as well as other settings unique to CCB.To learn more about this product visit ArcGIS Maritime.

This sample application was built with the latest Maritime Widgets using Web AppBuilder for ArcGIS. The ENC web service is from ArcGIS Maritime server extension.You can download the sample Maritime widgets from ArcGIS Maritime product support by clicking here.ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The base data for this web service is sample AML data download from here. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.

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 operation view contains services with shipping, maritime boundaries, and weather information for the west coast of the United States. The services in this web map are powered by ArcGIS GeoEvent Extension for Server and contain alerts for ships in certain boundaries, such as nature preserves, or inclement weather.Some of the widgets contained in this operation view are lists that sort the most important data such as those in geofences and those reporting with hazardous cargo. Data contained in this operation view includes:Maritime Boundaries and Port Information:Maritime Boundaries - Various maritime boundaries information provided by the National Oceanic and Atmospheric Administration (NOAAShipping Information:Proximity Alert - Generated buffer information created from an ArcGIS for GeoEvent Extension for Server processor of military vessels.Ship Position- Simulated shipping information obtained from the US Coast Guard (USCG).Weather Information:Meteorological Service of Environment Canada - Web map service with forecast, analysis, and observation layersforunderstanding current meteorological or oceanographic data.NOAA Lightning Strike Density - Time-enabled map service providing maps of experimental lightning strike density data.NOAA Weather Observations - Time-enabled map service providing map depicting the latest surface weather and marine weather observations.NOAA Weather Radar Mosaic - Time-enabled map service providing maps depicting mosaics of base reflectivity images across the United States.NOAA Weather Satellite Information - Time-enabled map service providing maps depicting visible, infrared, and water vapor imagery.

This tile service was created using the mcstpk.exe functionality from ArcGIS Maritime server extension. The tile package was created using publicly available S-57 ENC data downloaded from NOAA's ENC download site on May 6th, 2021.Level 0 - 18 was generated from these S-57 datasets and published to this service. Not all levels however have been published. Those that have been published are available for download.To learn more about this product visit ArcGIS Maritime.

WMS tile layer: https://services.arcgis.com/hkQNLKNeDVYBjvFE/arcgis/rest/services/PIOLP_Uses_of_Soil/MapServer**WFS**: https://dservices.arcgis.com/hkQNLKNeDVYBjvFE/arcgis/services/PIOLP_Uses_of_Land/WFSServer?service=wfs&request=getcapabilities The predominance of residential use is detected, mainly in central urban areas, but large areas of land are also found with the classification of rural or agricultural settlement in rustic soil. The existing specialized land for industrial use is scarce and located next to the port of Santa Cruz de La Palma, Breña Baja and Villa de Mazo in the East, and El Paso and Los Llanos de Aridane in the West. The forecasts of industrial land tend to reinforce the locations of these areas in addition to the creation of new industrial areas in other parts of the island. Regarding tourist use, we also see the scarce territorial indecency of urban land specialized in this use, highlighting Los Cancajos, as the largest extension of tourist urban land and Puerto Naos, along with other locations of lesser importance in Breña Baja or Charco Verde (classified as urban land despite the absence of construction). The forecasts of urbanizable land for tourist use, contained in the local planning or in the PTEOAT, considerably expand the amount of land with this destination, tending to reinforce existing locations, in particular Los Cancajos and Puerto Naos, where the largest extensions and to a lesser extent new locations are expected, in some cases already consolidated. Among the new areas of buildable land, we highlight the tourist locations that coincide between the local planning and the PTEOAT, in Fuencaliente, Barlovento, others that only appear in the local planning such as the large areas of Los Llanos de Aridane and other smaller ones in Villa de Mazo, Puntallana and Garafía. It is worth noting the high incidence of rural and agricultural settlements, which involve the occupation of large areas, often with extremely low densities and even occupying large unbuilt areas. This classification tends to endorse the dynamics of dispersion and residence construction on rustic soil. The more detailed study of the criteria and forms of these settlements is a priority objective of the Plan, as a basis for establishing criteria that regulate this trend, as well as homogeneous bases for the delimitation of rural and agricultural centers by the municipal general plans. Different degrees of evolution are visible in the formation and transformation of the settlements that allow us to foresee the criteria to be established. It highlights the great concentration of uses and activities in the center of the island, and the scarce land occupation in the extremes North and South. A first approach already draws some central areas, to the East and West, where the occupied land is concentrated, as well as the forecasts of new occupations, regardless of the use in question, residential, industrial or tourist.

Extension Services Clayton Performance Review for FY2025 Q1.

ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The base data for this web service is sample AML data download from here. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.

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.

This sample Web Scene demonstrates how you can bring a Maritime Chart Service web service into a 3D scene. For this example, the ENC land features are turned off to allow for the display of 3D land features.ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The primary ENC data in this web service was downloaded from NOAA's public site. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.

Extension Services CPR mini report card for FY2025Q3.

Extension Services CPR report card for FY2024Q4.

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 (50 cm) imagery. The model was trained on 50 cm Airbus 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 model can be seen in this dashboard.

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

ArcGIS Maritime server extension's Maritime Chart Service (MCS) capability is a Server Object Extension that provides both OGC WMS and Esri RESTful web services to quickly view and query your S-57 or S-63 encrypted datasets.The primary IENC data in this service was downloaded from USACE's public site. Other IENC and BENC datasets were downloaded from various other agency sites in Europe. Datasets are not guaranteed to be kept up-to-date and are for demonstration purposes only. To learn more about this product visit ArcGIS Maritime.