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.

Abstract: Annual average wind resource potential for the state of South Carolina at a 50 meter height.

Purpose: Provide information on the wind resource development potential within the state of South Carolina.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a WGS 84 projection system.

Other Citation Details: The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Abstract: Annual average wind resource potential for Wisconsin at a 50 meter height.

Purpose: Provide information on the wind resource development potential in Wisconsin.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects.

Other Citation Details: This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

To better serve the public and encourage cooperation, CAL FIRE has released the largest dataset of local fire districts within the State of California. This dataset is currently being updated on a yearly basis to incorporate boundary shifts as well as updated Fire Department Identification (FDID) records kept by the Office of the State Fire Marshal.

Annual average wind resource development potential for the state of Texas.

This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile is in a UTM zone 19, datum WGS 84 projection system.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

Abstract: Annual average wind resource potential for the state of Georgia at a 50 meter height.

Purpose: Provide information on the wind resource development potential within the state of Georgia.

Supplemental Information: This data set has been validated by NREL and wind energy meteorological consultants. However, the data is not suitable for micro-siting potential development projects. This shapefile was generated from a raster dataset with a 200 m resolution, in a UTM zone 17, datum WGS 84 projection system.

Other_Citation_Details: The wind power resource estimates were produced by AWS TrueWind using their MesoMap system and historical weather data under contract to Wind Powering America/NREL. This map has been validated with available surface data by NREL and wind energy meteorological consultants.

License Info

This GIS data was developed by the National Renewable Energy Laboratory ("NREL"), which is operated by the Alliance for Sustainable Energy, LLC for the U.S. Department of Energy ("DOE"). The user is granted the right, without any fee or cost, to use, copy, modify, alter, enhance and distribute this data for any purpose whatsoever, provided that this entire notice appears in all copies of the data. Further, the user of this data agrees to credit NREL in any publications or software that incorporate or use the data.

Access to and use of the GIS data shall further impose the following obligations on the User. The names DOE/NREL may not be used in any advertising or publicity to endorse or promote any product or commercial entity using or incorporating the GIS data unless specific written authorization is obtained from DOE/NREL. The User also understands that DOE/NREL shall not be obligated to provide updates, support, consulting, training or assistance of any kind whatsoever with regard to the use of the GIS data.

THE GIS DATA IS PROVIDED "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL DOE/NREL BE LIABLE FOR ANY SPECIAL, INDIRECT OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER, INCLUDING BUT NOT LIMITED TO CLAIMS ASSOCIATED WITH THE LOSS OF DATA OR PROFITS, WHICH MAY RESULT FROM AN ACTION IN CONTRACT, NEGLIGENCE OR OTHER TORTIOUS CLAIM THAT ARISES OUT OF OR IN CONNECTION WITH THE ACCESS OR USE OF THE GIS DATA.

The User acknowledges that access to the GIS data is subject to U.S. Export laws and regulations and any use or transfer of the GIS data must be authorized under those regulations. The User shall not use, distribute, transfer, or transmit GIS data or any products incorporating the GIS data except in compliance with U.S. export regulations. If requested by DOE/NREL, the User agrees to sign written assurances and other export-related documentation as may be required to comply with U.S. export regulations.

The POI Dataset is a digital representation of the physical, geographic and commercial features across all of Santa Clara County. This dataset aims to provide accurate location information in the map. Sources: California Department Of Education (2021), Santa Clara County Combined data (2022).THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.

Comprises de location of fish species records within the Okavango Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. It focuses on the BLM terrestrial core indicators, which include measures of vegetation and soil condition such as plant species cover and composition, plant height, and soil stability. The BLM terrestrial core indicators and methods were identified through a multi-disciplinary process and are described in BLM Technical Note 440 (https://ia800701.us.archive.org/6/items/blmcoreterrestri00mack/BlmCoreTerrestrialIndicatorsAndMethods_88072539.pdf). The Landscape Monitoring Framework (LMF) dataset was collect using the Natural Resource Conservation Services (NRCS) National Resource Inventory (NRI) methodology which mirrors the data collected by the BLM using the Monitoring Manual for Grassland, Shrubland, and Savannah Ecosystems (2nd edition; https://www.landscapetoolbox.org/manuals/monitoring-manual/). Specific instructions for data collectors each year the data were collected can be found at https://grazingland.cssm.iastate.edu/reference-materials. Also see Interpreting Indicators of Rangeland Health (version 5; https://www.landscapetoolbox.org/manuals/iirhv5/).

The monitoring locations were selected using spatially balanced, random sampling approaches and thus provide an unbiased representation of land conditions. However, these data should not be used for statistical or spatial inferences without knowledge of how the sample design was drawn or without calculating spatial weights for the points based on the sample design.

General Definitions

Noxious: Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Each state’s noxious list can be found in tblStateSpecies Table, where the Noxious field is ‘YES’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Non-Noxious: Non-Noxious status and growth form (forb, shrub, etc.) are designated for each BLM Administrative State using the state noxious list and local botany expertise often after consulting the USDA plants database. Non-Noxious status can be found in tblStateSpecies Table, where the Noxious field is ‘NO’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Sagebrush: Sagebrush species are designated for each BLM Administrative State using local botany expertise. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘Sagebrush’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Non-Sagebrush Shrub: Non Sagebrush Shrub species are designated for each BLM Administrative State as the plants determined to be shrubs that are not also Sagebrush. This list can be found for each state in in the tblStateSpecies Table, where SG_Group field is ‘NonSagebrushShrub’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Tall Stature Perennial Grass: Tall Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘TallStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Short Stature Perennial Grass: Short Stature Perennial Grasses status was determined by Sage Grouse biologist and modified slightly in each state and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘ShortStaturePerennialGrass’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Preferred Forb: Preferred forb for Sage Grouse status was determined for each state by Sage Grouse biologist and other local experts and this list can be found in tblStateSpecies in the SG_Group field where SG_Group field is ‘PreferredForb’ and the StateSpecies field has the two letter state code for the desired state (e.g. ‘NM’).
Live: The NRI Methods measure Live vs Dead plant cover – i.e. if a pin drop hits a plant part and that plant part is dead (even if it’s on a living plant) that hit is considered a dead hit. Any occurrence of Live Sagebrush calculations indicates that the measurement is only hits that were live plant parts. If a pin hits both a live and a dead plant part in the same pin drop – that hit is considered live.
Growth Habit: The form of a plant, in this dataset the options are Forb, Graminoid, Sedge, Succulent, Shrub, SubShrub, Tree, NonVascular. The most common growth habit for each state was determined by local botany expertise often after consulting the USDA plants database. The growth habit for each species is a state can be found in tblStateSpecies in the GrowthHabitSub field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.
Duration: The life length of a plant. In this dataset we consider plants to be either Perennial or Annual – Biennial plants are classified as Annuals. The most common duration for each state was determined by local botany expertise often after consulting the USDA plants database. The duration for each species is a state can be found in tblStateSpecies in the Duration field. The values are used to place each plant in a Growth Habit/Duration bin such as Perennial Grass, or Annual Forb, etc.
tblStateSpecies: This table in the database contains the Species Lists for each state. In the instance where a species code does not have a Growth Habit, Growth Habit Sub, or Duration – any occurrence of that code will not be included in calculations that require that information – for example a code that has NonWoody Forb but no information about annual or perennial will not be included in any of the calculations that are perennial or annual forb calculations. Most codes with no information will have the following in the notes – indicating that the only calculation it will be included in is Total Foliar which doesn’t require any growth habit and duration information – “Not used for calculations except Total Foliar.”

A set of 2 features depicting the distribution of two important fish species (Clarias gariepinus and Pseudocrenilabrtus philander) within the Okavango Basin. Source: Africa Water Resources Database (FAO). This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the “GIS Database for the EPSMO Project” document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

School Boundaries data built from parcel database, extracted parcels based on overlay with gschools.shp. Verified site by site to add or delete based on changes since gschools created. FIELDSNAME - School NameSHORT_NAME - Short school name_DISTRICT - School District nameSTATUS - School statusADDRESS - Street number, name, and typeCITY - City nameZIP - ZipcodeMOD_DATE - Date record modified. gschools from http://webhelp.esri.com/arcgisdesktop/9.2/index.cfm?id=5681&pid=5673&topicname=United_States_Geographic_Names_Information_System_Schools

THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.

The MDOT SHA OOTS Sign Structures dataset is an actively maintained database of sign structures in Maryland. The Maryland Department of Transportation, State Highway Administration, Office of Traffic Safety "OOTS" built and maintains the database with help from the MDOT SHA Office of Information Technology. Sign Structures in this open data database are only those set to an active* status. Structures information is further described by detailed attributes; the attributes definitions are listed below in the Data Dictionary section.Points of ContactData Owner: Faramarz "Faz" Sadeghi-Bajgiran for OOTS fbajgiran@mdot.maryland.govData Steward: Elliott Plack for OIT EISD: eplack.consultant@mdot.maryland.gov Technical Support: MDOT SHA OIT Enterprise Information Services - GIS Team: GIS@mdot.maryland.govData DictionarySign structure data are described by a number of attributes, including general ones, inspection details, and more.Str. ID: the Structure ID, a six-digit identifier. The first two digits represent the county. The last four digits are sequential.Status: the structure's status. Most structures are marked active. Structures with a "CFR" status are noted as "Call For Removal" and a slated to be removed or replaced in the noted time period.eDR Number: e-Design Number:Str. Type: the structure type, based on the design. Structures can either be cantilevered (CN), overhead (OH), or a combination (CM).Owned by: the owner. Most are owned by MDOT SHA but certain special ones are owned by another state agency, a federal agency, e.g., NPS, or the tri-state compact, the Woodrow Wilson Bridge Commission.Maintained by: the agency responsible for maintaining/inspecting the structure. Usually, the same as owner but some structures have unique maintenance agreements.Acceptance Year: the year the structure was accepted into the state inventory and a good estimate of the age.End-of-Service: a future year in which the structure should be removed based on criteria.Removal Year: the year the structure was removed, in the case of inactive structures.Inspection ParametersStructures are repeatedly inspected as they are critical assets for navigation and would adversely affect the public if they were to fall. The inspections are closely monitored by OOTS. Inspections are categorized by In-Depth, Routine, or NDT (Non-Destructive Testing). Each inspection regime has the same attributes:Frequency: frequency in which inspections are due.Status: status of the current inspection cycle.CRS: Component Rating Score. A comprehensive, weighted rating score the considers each component of the sign.RAS: Risk Assessment Score: A criticality score based on criteria like traffic volume, speed limit, age, type, and proximity to critical infrastructure.CRS Date: The most recent CRS date.Due Date: The due date of the next inspection of the type.MOT: Maintenance of Traffic (MOT) procedures recommended for the particular structure.Other AttributesDMS ID: if there is a Dynamic Message Sign on the structure, the ID will be listed.# of AB's / Pole: Number of Anchor Bolts per Pole. The number varies by structure type. A reference to the numbering regime is here.AB Dia.: Anchor Bolt diameter in US inches.Span: the span is the length of the structure, in US feet. Span is the distance between posts or from the post to tip for cantilever structures.Clearance: the clearance underneath the structure in US feet.Cross Section Type: the cross-section type of the pole, typically round but there are other less common types.Contractor: the contractor that installed the structure, if known.Contract No: the contract under which the structure was installed.Manufacturer: the manufacture of the structure.Shop Drawings Exist: whether or not the shop drawings exist within MDOT SHA.B-2-P Connection: Base-to-Plate connection typeB-2-P Gusseted: Whether or not the Base-to-Plate is gusseted.TEDD Comments: comments made by the Traffic Engineering Design Division in OOTS. Contact OOTS with any questions.Creator/Editor: There are several automatic fields that track the username that create and last updated the asset, and at what time.Inactive Structures* Inactive structures, e.g., those that have been removed, are also captured in this database but that information is not available here. Contact OOTS if interested in inactive structures.

Location of large irrigation schemes, in Namibia, that share geographical overlapping with the Okavango Basin. Source: Ministry of Agriculture, Water and Forestry of Namibia. This dataset is part of the GIS Database for the Environment Protection and Sustainable Management of the Okavango River Basin project (EPSMO). Detailed information on the database can be found in the GIS Database for the EPSMO Project document produced by Luis Veríssimo (FAO consultant) in July 2009, and here available for download.

The facilities (Hospital) data set is a digital representation of the facilities across all of Santa Clara County. This data set aims to provide accurate location information in the map. THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.

Data repository for solar measurements from 4 WB funded stations in Armenia. The four solar measuring stations and the associated measurement campaign have been financed by the Scaling-Up Renewable Energy Program (SREP) as part of the preparation activities for the Armenia Utility-Scale Solar Project. This project, which is being jointly supported by SREP and the World Bank, will deliver the first utility-scale solar plant in the country. The locations for the measuring stations were selected by the Renewable Resources and Energy Efficiency Fund, the project’s implementing entity, following the recommendations from Effergy, the expert consultant firm. For download access to GIS layers, please visit the Global Solar Atlas: http://globalsolaratlas.info/

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 dataset is a combination of 2 data projects: 1- Data were updated within NYC watershed portions using 1m resolution LiDAR and 1ft orthoimagery collected in 2009 as part of the NYS Digital Ortho Program under contract with NYCDEP under CAT-371.For NYC reservoir areas only: NYCDEP BWS GIS Staff (T. Spies) edited all artificial path and stream transitions to snap exactly to polygon edges representing NYC reservoirs, where those areas were updated by NYCDEP for correct inundation area based on spillway elevation.QA edits to NHD hydrography, including this feature class, were also made where needed based on field verification and correction of the NYCbasin1m boundary.As an additional departure from standard NHD to meet DEP’s needs, DEP GIS staff attributed all flowlines by their respective NYC reservoir basin and NYC water supply “region” as defined in the feature class “NYCbasin1m”. This was done using the “select by location” tool rather than “identity” tool, so as not to split any flowlines across boundaries. Any flowlines crossing basin boundaries in error were corrected by splitting the lines and snapping their endpoints to the appropriate spillway or basin edge instead. After these edits were made, a new geometric network was built to test and ensure all flowlines in the entire dataset were correctly connected so that they can be used for routing.2- Data was updated within portions of Ulster County outside the NYC watershed using NYS 1ft orthoimagery collected in 2013 and multiple Elevation datasets (2013 NYS DEC 1m Lidar Hudson River, 2005 NYS DEC 3m Lidar Ulster Stream Corridors, 1992 USGS 10m Digital Elevation Model (DEM)).Primary quality control was performed visually using enhanced symbology and supporting reference data. A detailed QC checklist is provided in the QC report. Specific emphasis was placed on the areas bordering the NYC Watershed and the areas encompassed by the Town of Woodstock’s local hydrography data. To the extent connections occurred, the data captured on this project was “snapped” to the corresponding locations in the NYC Watershed so that the data could be seamlessly integrated. The hydrography data from the Town of Woodstock, however, was inconsistent when applied to the data capture protocol. Many locally derived features did not appear to be supported by the source data (i.e., they did not exist) and were not included. All visual inspections were made at 1:1000 scale or better. During data capture, the Data Capture Analyst used a separate point feature class named “Flags” to identify locations where there may have been some interpretation or confusion. Later, the QC Analyst also used additional bookmarks in ArcGIS to track locations where additional investigation or interpretation was required. Finally, after an initial pass through the data, the QC Analyst evaluated and resolved all such flags and bookmarks, collaborating with the Data Capture Analyst as necessary to discuss findings and resolve questions.As data was completed, naming convention and separate storage locations were used for data management to ensure that source and modified datasets were clearly separated. In addition, a detailed QC tracking spreadsheet was used to track and manage effort on completing QC and resolving any issues.Finally, after the initial data delivery, several rounds of QC review were performed by Ulster County to include: additional visual inspection of flow line connectivity, geometric network tracking, and utility network analysisMost of the issues that were not readily apparent in the manual QC process were attributed to minor errors in data capture and discovered here. Examples include digitizing lines in the wrong direction (not downstream), existence of multi-part features, and topology errors. In all cases, issues were evaluated and resolved

Private school information. Data derived from a database of private schools published online(2014) by a consortium of parents & other interested parties wanting to make information regarding priviate educational entities available to the public. THE GIS DATA IS PROVIDED "AS IS". THE COUNTY MAKES NO WARRANTIES, EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION, ANY IMPLIED WARRANTIES OR MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE, REGARDING THE ACCURACY, COMPLETENESS, VALUE, QUALITY, VALIDITY, MERCHANTABILITY, SUITABILITY, AND CONDITION, OF THE GIS DATA. USER'S OF COUNTY'S GIS DATA ARE HEREBY NOTIFIED THAT CURRENT PUBLIC PRIMARY INFORMATION SOURCES SHOULD BE CONSULTED FOR VERIFICATION OF THE DATA AND INFORMATION CONTAINED HEREIN. SINCE THE GIS DATA IS DYNAMIC, IT WILL BY ITS NATURE BE INCONSISTENT WITH THE OFFICIAL COUNTY DATA. ANY USE OF COUNTY'S GIS DATA WITHOUT CONSULTING OFFICIAL PUBLIC RECORDS FOR VERIFICATION IS DONE EXCLUSIVELY AT THE RISK OF THE PARTY MAKING SUCH USE.