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.

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

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.

Learn the building blocks of a query expression and how to select features that meet one or more attribute criteria.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.

Geographic Information System (GIS) Market Outlook

The global Geographic Information System (GIS) market size was valued at approximately USD 8.1 billion in 2023 and is projected to reach around USD 16.3 billion by 2032, growing at a CAGR of 8.2% during the forecast period. One of the key growth factors driving this market is the increasing adoption of GIS technology across various industries such as agriculture, construction, and transportation, which is enhancing operational efficiencies and enabling better decision-making capabilities.

Several factors are contributing to the robust growth of the GIS market. Firstly, the increasing need for spatial data in urban planning, infrastructure development, and natural resource management is accelerating the demand for GIS solutions. For instance, governments and municipalities globally are increasingly relying on GIS for planning and managing urban sprawl, transportation systems, and utility networks. This growing reliance on spatial data for efficient resource allocation and policy-making is significantly propelling the GIS market.

Secondly, the advent of advanced technologies like the Internet of Things (IoT), Artificial Intelligence (AI), and machine learning is enhancing the capabilities of GIS systems. The integration of these technologies with GIS allows for real-time data analysis and predictive analytics, making GIS solutions more powerful and valuable. For example, AI-powered GIS can predict traffic patterns and help in effective city planning, while IoT-enabled GIS can monitor and manage utilities like water and electricity in real time, thus driving market growth.

Lastly, the rising focus on disaster management and environmental monitoring is further boosting the GIS market. Natural disasters like floods, hurricanes, and earthquakes necessitate the need for accurate and real-time spatial data to facilitate timely response and mitigation efforts. GIS technology plays a crucial role in disaster risk assessment, emergency response, and recovery planning, thereby increasing its adoption in disaster management agencies. Moreover, environmental monitoring for issues like deforestation, pollution, and climate change is becoming increasingly vital, and GIS is instrumental in tracking and addressing these challenges.

Regionally, the North American market is expected to hold a significant share due to the widespread adoption of advanced technologies and substantial investments in infrastructure development. Asia Pacific is anticipated to witness the fastest growth, driven by rapid urbanization, industrialization, and supportive government initiatives for smart city projects. Additionally, Europe is expected to show steady growth due to stringent regulations on environmental management and urban planning.

Component Analysis

The GIS market by component is segmented into hardware, software, and services. The hardware segment includes devices like GPS, imaging sensors, and other data capture devices. These tools are critical for collecting accurate spatial data, which forms the backbone of GIS solutions. The demand for advanced hardware components is rising, as organizations seek high-precision instruments for data collection. The advent of technologies such as LiDAR and drones has further enhanced the capabilities of GIS hardware, making data collection faster and more accurate.

In the software segment, GIS platforms and applications are used to store, analyze, and visualize spatial data. GIS software has seen significant advancements, with features like 3D mapping, real-time data integration, and cloud-based collaboration becoming increasingly prevalent. Companies are investing heavily in upgrading their GIS software to leverage these advanced features, thereby driving the growth of the software segment. Open-source GIS software is also gaining traction, providing cost-effective solutions for small and medium enterprises.

The services segment encompasses various professional services such as consulting, integration, maintenance, and training. As GIS solutions become more complex and sophisticated, the need for specialized services to implement and manage these systems is growing. Consulting services assist organizations in selecting the right GIS solutions and integrating them with existing systems. Maintenance and support services ensure that GIS systems operate efficiently and remain up-to-date with the latest technological advancements. Training services are also crucial, as they help users maximize the potential of GIS technologies.

Video based training seminar.

The GIS database used in this project serves as a centralized repository for all spatial datasets required for wildfire threat analysis and model training. It includes CAL FIRE’s Wildland Fire Threat layer, which provides pixel-based classifications of wildfire potential across California, as well as transportation infrastructure layers, including primary and secondary roads and railways.To support impact analysis, 1,000-foot buffer zones were generated around each infrastructure feature to define zones of interest for wildfire segmentation. The database is structured for integration into both machine learning workflows and GIS environments, enabling seamless overlay, visualization, and spatial querying within platforms such as ArcGIS Pro or QGIS.

Esri's Water Resources GIS Platform offers a comprehensive suite of tools and resources designed to modernize water resource management. It emphasizes geospatial solutions for monitoring, analyzing, and modeling water systems, helping decision-makers tackle challenges like drought resilience, flood mitigation, and environmental protection. By leveraging the capabilities of ArcGIS, users can transform raw water data into actionable insights, ensuring more efficient and effective water resource management.A central feature of the platform is Arc Hydro, a specialized data model and toolkit developed for GIS-based water resource analysis. This toolset allows users to integrate, analyze, and visualize water datasets for applications ranging from live stream gauge monitoring to pollution control. Additionally, the platform connects users to the ArcGIS Living Atlas of the World, which offers extensive water-related datasets such as rivers, wetlands, and soils, supporting in-depth analyses of hydrologic conditions. The Hydro Community further enhances collaboration, enabling stakeholders to share expertise, discuss challenges, and build innovative solutions together.Esri’s platform also provides training opportunities and professional services to empower users with technical knowledge and skills. Through instructor-led courses, documentation, and best practices, users gain expertise in using ArcGIS and Arc Hydro for their specific water management needs. The combination of tools, datasets, and community engagement makes Esri's water resources platform a powerful asset for advancing sustainable water management initiatives across public and private sectors.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu.We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024.Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area.The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857.For using these data:- The Adobe Suite gives you great software to open .Tif files.- You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.- Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk.- You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files.- The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file.This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

About this itemBack in 2017, I made a Cascade story map to compile GIS career resources for my current and future interns. Fast forward seven years, and I finally rebuilt it as an ArcGIS StoryMap. From job title descriptions to certifications and to salaries, it covers the main areas I find emerging professionals asking about when they're looking at a career in GIS. There are multiple shout outs to the Consortium in it too, of course.😎Author/ContributorJohn NergeOrganizationPersonal workOrg Websitehttps://bit.ly/JohnNerge

Professional Growth Management - Attract, grow, and retain top talent to serve our seniors and their families with passion, pride, and professionalism.

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

vfillDL.zip

dems: LiDAR DTM data partitioned into training, three testing, and two validation datasets. Original DTM data were obtained from 3DEP (https://www.usgs.gov/3d-elevation-program) and the WV GIS Technical Center (https://wvgis.wvu.edu/) . extents: extents of the training, testing, and validation areas. These extents were defined by the researchers. vectors: vector features representing valley fills and partitioned into separate training, testing, and validation datasets. Extents were created by the researchers.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

ArcGIS Pro provides the tools to integrate, visualize, analyze, and share your data. This course introduces you to the powerful capabilities of ArcGIS Pro and how it can be used in your work.Kontakt os for adgang

This course is intended to get you thinking about your future. Learn about where GIS professionals work, what they do, and how their educational choices prepare them for different types of jobs.GoalsDiscover the types of projects that GIS professionals work on.Identify qualities and skills that can help you get a GIS-related job.Choose educational options that match your goals and support your future career plans.