Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on October 19-23, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

In this course, you will learn about some common types of data used for GIS mapping and analysis, and practice adding data to a file geodatabase to support a planned project.Goals Create a file geodatabase. Add data to a file geodatabase. Create an empty geodatabase feature class.

Dataset contains training material on using open source Geographic Information Systems (GIS) to improve protected area planning and management from a workshop that was conducted on August 17-21, 2020. Specifically, the dataset contains lectures on GIS fundamentals, QGIS 3.x, and global positioning system (GPS), as well as country-specific datasets and a workbook containing exercises for viewing data, editing/creating datasets, and creating map products in QGIS. Supplemental videos that narrate a step-by-step recap and overview of these processes are found in the Related Content section of this dataset.

Funding for this workshop and material was funded by the Biodiversity and Protected Areas Management (BIOPAMA) programme. The BIOPAMA programme is an initiative of the Organisation of African, Caribbean and Pacific (ACP) Group of States financed by the European Union's 11th European Development Fund. BIOPAMA is jointly implemented by the International Union for Conservation of Nature {IUCN) and the Joint Research Centre of the European Commission (EC-JRC). In the Pacific region, BIOPAMA is implemented by IUCN's Oceania Regional Office (IUCN ORO) in partnership with the Secretariat of the Pacific Regional Environment Programme (SPREP). The overall objective of the BIOPAMA programme is to contribute to improving the long-term conservation and sustainable use of biodiversity and natural resources in the Pacific ACP region in protected areas and surrounding communities through better use and monitoring of information and capacity development on management and governance.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Quivira National Wildlife Refuge was established in 1955, and a detailed vegetation map was not available for management purposes. With the present development of a biological program and Comprehensive Conservation Plan (CCP), a baseline vegetation map of the refuge was identified as a necessity. Development of the vegetation map and associated report was a multi-step process. Aerial photography (NAIP, 2008) was used with eCognition to create polygons of different plant communities based on the likeness of surrounding pixels in the area. Prior to ground-truthing, the following activities were accomplished: training on vegetation mapping using GIS (previous experience and National Conservation Training Center course), creation of an vegetation association and alliance dichotomous key, development of a refuge plant key and identification skills, and preparation of maps for ground truthing. Once out in the field dominant plants were identified for appropriate vegetation alliance and association classification, plant specimens were collected for the refuge herbarium as necessary and additional observations and photos were gathered for the report. Over the course of the project, classification data was entered into a GIS and polygons were appropriately modified to create the final map. At Quivira, results found a total of 42 alliances and 43 associations.The most dominant plants throughout the refuge in 2008 based on canopy cover were saltgrass, plum, little bluestem and cottonwood. The number of alliances and associations found on the refuge show high species diversity.

This is an instructional document developed for volunteers who follow the Fluvanna History Initiative on One Shared Story's GIS Hub.Training was held at the Fluvanna County Public Library on Sunday September 29, 2019. This effort is being coordinated through an Esri GIS Premium Hub Community with assitance from GIS Corp and funding from the UVA Equity Atlas and the BAMA Works Fund.

800+ GIS Engineers with 25+ years of experience in geospatial, We provide following as Advance Geospatial Services:

Analytics (AI) Change detection Feature extraction Road assets inventory Utility assets inventory Map data production Geodatabase generation Map data Processing /Classifications
Contour Map Generation Analytics (AI) Change Detection Feature Extraction Imagery Data Processing Ortho mosaic Ortho rectification Digital Ortho Mapping Ortho photo Generation Analytics (Geo AI) Change Detection Map Production Web application development Software testing Data migration Platform development

AI-Assisted Data Mapping Pipeline AI models trained on millions of images are used to predict traffic signs, road markings , lanes for better and faster data processing

Our Value Differentiator

Experience & Expertise -More than Two decade in Map making business with 800+ GIS expertise -Building world class products with our expertise service division & skilled project management -International Brand “Mappls” in California USA, focused on “Advance -Geospatial Services & Autonomous drive Solutions”

Value Added Services -Production environment with continuous improvement culture -Key metrics driven production processes to align customer’s goals and deliverables -Transparency & visibility to all stakeholder -Technology adaptation by culture

Flexibility -Customer driven resource management processes -Flexible resource management processes to ramp-up & ramp-down within short span of time -Robust training processes to address scope and specification changes -Priority driven project execution and management -Flexible IT environment inline with critical requirements of projects

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Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

This survey is intended to solicit feedback from the general public for developing free training sessions utilizing data and maps in the EBRGIS Portal.

Maps exist to convey information to people, whether that information is how to get from one point to another or how many oil fields are located in a given region. Effective cartography can convey that information efficiently to map users.In this course, you will be introduced to a five-step workflow for designing and creating maps. This workflow can be applied to any map or output medium (print or digital). This course will cover all steps of the workflow in general terms, emphasizing the first two steps: the cartographic planning process and data evaluation.After completing this course, you will be able to perform the following tasks:Identify and describe the cartographic workflow steps.Explain cartographic design controls and how they drive map creation.Apply the planning step of the cartographic workflow.Evaluate data sources to determine applicability.Discuss why basemap and operational layers are important.Assign the correct coordinate system to data based on the geographic extent and map objective.Assess the level of detail required for a map and apply generalization techniques when appropriate.

ArcGIS Technology for Mapping COVID-19 (Esri Training).Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic. This plan will teach you the core ArcGIS technology necessary to understand, prepare for, and respond to COVID-19 in your community or organization.More information about Esri training..._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This vector dataset provides polygons that represent significant golf course facility locations in Suffolk County. These courses can be publicly (State, County, Town, Village) or privately owned. This dataset can be linked with the GolfCoursePoint feature class by the FACILITYID field. In some cases, there may be multiple Golf Course Points for a single Golf Course Polygon. These data are organized for consumption in desktop and web applications.

Materials created by James Baker in June 2014 for the 108 Mapping Data course of the British Library Digital Scholarship Training Programme.

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.