In this seminar, you will learn about the spatial analysis tools built directly into the ArcGIS.com map viewer. You will learn of the spatial analysis capabilities in ArcGIS Online for Organizations, whether for analyzing your own data, data that's publicly available on ArcGIS Online, or a combination of both. You will learn the overall features and benefits of ArcGIS Online Analysis, how to get started, and how to choose the right approach in order to solve a specific spatial problem.

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.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

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.

In this seminar, you will learn how to use Web AppBuilder to create powerful GIS apps that run on any device without writing a single line of code. You will also learn how to quickly build web apps with your data, selection of widgets, and the theme you choose, to make them available to your organization.This seminar was developed to support the following:ArcGIS OnlineWeb AppBuilder for ArcGISWeb AppBuilder for ArcGIS (Developer Edition) 1.0

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.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

Landforms are large recognizable features such as mountains, hills and plains; they are an important determinant of ecological character, habitat definition and terrain analysis. Landforms are important to the distribution of life in natural systems and are the basis for opportunities in built systems, and therefore landforms play a useful role in all natural science fields of study and planning disciplines.Dataset SummaryPhenomenon Mapped: LandformsUnits: MetersCell Size: 231.91560581932 metersSource Type: ThematicPixel Type: 8-bit unsigned integerData Coordinate System: WGS 1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: May 2016ArcGIS Server URL: https://landscape7.arcgis.com/arcgis/In February 2017, Esri updated the World Landforms - Improved Hammond Method service with two display functions: Ecological Land Units landform classes and Ecological Facets landform classes. This layer represents Ecological Facets landform classes. You can view the Ecological Land Units landform classes by choosing Image Display, and changing the Renderer. This layer was produced using the Improved Hammond Landform Classification Algorithm produced by Esri in 2016. This algorithm published and described by Karagulle et al. 2017: Modeling global Hammond landform regions from 250-m elevation data in Transactions in GIS.The algorithm, which is based on the most recent work in this area by Morgan, J. & Lesh, A. 2005: Developing Landform Maps Using Esri’s Model Builder., Esri converted Morgan’s model into a Python script and revised it to work on global 250-meter resolution GMTED2010 elevation data. Hammond’s landform classification characterizes regions rather than identifying individual features, thus, this layer contains sixteen classes of landforms:Nearly flat plainsSmooth plains with some local reliefIrregular plains with moderate relief Irregular plains with low hillsScattered moderate hillsScattered high hillsScattered low mountainsScattered high mountainsModerate hillsHigh hills Tablelands with moderate reliefTablelands with considerable reliefTablelands with high relief Tablelands with very high relief Low mountainsHigh mountainsTo produce these classes, Esri staff first projected the 250-meter resolution GMTED elevation data to the World Equidistant Cylindrical coordinate system. Each cell in this dataset was assigned three characteristics: slope based on 3-km neighborhood, relief based on 6 km neighborhood, and profile based on 6-km neighborhood. The last step was to overlay the combination of these three characteristics with areas that are exclusively plains. Slope is the percentage of the 3-km neighborhood occupied by gentle slope. Hammond specified 8% as the threshold for gentle slope. Slope is used to define how flat or steep the terrain is. Slope was classified into one of four classes: Percent of neighborhood over 8% of slopeSlope Classes0 - 20%40021% -50%30051% - 80%200>81% 100Local Relief is the difference between the maximum and minimum elevation within in the 6-km neighborhood. Local relief is used to define terrain how rugged or the complexity of the terrain's texture. Relief was assigned one of six classes:Change in elevationRelief Class ID0 – 30 meters1031 meter – 90 meters2091 meter – 150 meters30151 meter – 300 meters40301 meter – 900 meters50>900 meters60The combination of slope and relief begin to define terrain as mountains, hills and plains. However, the difference between mountains or hills and tablelands cannot be distinguished using only these parameters. Profile is used to determine tableland areas. Profile identifies neighborhoods with upland and lowland areas, and calculates the percent area of gently sloping terrain within those upland and lowland areas. A 6-km circular neighborhood was used to calculate the profile parameter. Upland/lowland is determined by the difference between average local relief and elevation. In the 6-km neighborhood window, if the difference between maximum elevation and cell’s elevation is smaller than half of the local relief it’s an upland. If the difference between maximum elevation and cell’s elevation is larger than half of the local relief it’s a lowland. Profile was assigned one of five classes:Percent of neighborhood over 8% slope in upland or lowland areasProfile ClassLess than 50% gentle slope is in upland or lowland0More than 75% of gentle slope is in lowland150%-75% of gentle slope is in lowland250-75% of gentle slope is in upland3More than 75% of gentle slope is in upland4Early reviewers of the resulting classes noted one confusing outcome, which was that areas were classified as "plains with low mountains", or "plains with hills" were often mostly plains, and the hills or mountains were part of an adjacent set of exclusively identified hills or mountains. To address this areas that are exclusively plains were produced, and used to override these confusing areas. The hills and mountains within those areas were converted to their respective landform class.The combination of slope, relief and profile merged with the areas of plains, can be better understood using the following diagram, which uses the colors in this layer to show which classes are present and what parameter values produced them:What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop. This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This layer shows the locations and details of the training centres in Hong Kong. It is a subset of data made available by the Employees Retraining Board under the Government of Hong Kong Special Administrative Region (the “Government”) at https://portal.csdi.gov.hk ("CSDI Portal"). The source data is processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort. For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

This web map shows the locations of Chinese Medicine Clinics cum Training and Research Centres in Hong Kong. It is a set of data made available by the Hospital Authority at https://portal.csdi.gov.hk ("CSDI Portal").The source data is processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

This webmap is a subset of Global Landcover 1992 - 2020 Image Layer. You can access the source data from here. This layer is a time series of the annual ESA CCI (Climate Change Initiative) land cover maps of the world. ESA has produced land cover maps for the years 1992-2020. These are available at the European Space Agency Climate Change Initiative website.Time Extent: 1992-2020Cell Size: 300 meterSource Type: ThematicPixel Type: 8 Bit UnsignedData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: ESA Climate Change InitiativeUpdate Cycle: Annual until 2020, no updates thereafterWhat can you do with this layer?This layer may be added to ArcGIS Online maps and applications and shown in a time series to watch a "time lapse" view of land cover change since 1992 for any part of the world. The same behavior exists when the layer is added to ArcGIS Pro.In addition to displaying all layers in a series, this layer may be queried so that only one year is displayed in a map. This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro with a query set to display just one year. Then, an area count of land cover types may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from other years to show a trend.To sum up area by land cover using this service, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Different Classifications Available to MapFive processing templates are included in this layer. The processing templates may be used to display a smaller set of land cover classes.Cartographic Renderer (Default Template)Displays all ESA CCI land cover classes.*Forested lands TemplateThe forested lands template shows only forested lands (classes 50-90).Urban Lands TemplateThe urban lands template shows only urban areas (class 190).Converted Lands TemplateThe converted lands template shows only urban lands and lands converted to agriculture (classes 10-40 and 190).Simplified RendererDisplays the map in ten simple classes which match the ten simplified classes used in 2050 Land Cover projections from Clark University.Any of these variables can be displayed or analyzed by selecting their processing template. In ArcGIS Online, select the Image Display Options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left hand menu. From the Processing Template pull down menu, select the variable to display.Using TimeBy default, the map will display as a time series animation, one year per frame. A time slider will appear when you add this layer to your map. To see the most current data, move the time slider until you see the most current year.In addition to displaying the past quarter century of land cover maps as an animation, this time series can also display just one year of data by use of a definition query. For a step by step example using ArcGIS Pro on how to display just one year of this layer, as well as to compare one year to another, see the blog called Calculating Impervious Surface Change.Hierarchical ClassificationLand cover types are defined using the land cover classification (LCCS) developed by the United Nations, FAO. It is designed to be as compatible as possible with other products, namely GLCC2000, GlobCover 2005 and 2009.This is a heirarchical classification system. For example, class 60 means "closed to open" canopy broadleaved deciduous tree cover. But in some places a more specific type of broadleaved deciduous tree cover may be available. In that case, a more specific code 61 or 62 may be used which specifies "open" (61) or "closed" (62) cover.Land Cover ProcessingTo provide consistency over time, these maps are produced from baseline land cover maps, and are revised for changes each year depending on the best available satellite data from each period in time. These revisions were made from AVHRR 1km time series from 1992 to 1999, SPOT-VGT time series between 1999 and 2013, and PROBA-V data for years 2013, 2014 and 2015. When MERIS FR or PROBA-V time series are available, changes detected at 1 km are re-mapped at 300 m. The last step consists in back- and up-dating the 10-year baseline LC map to produce the 24 annual LC maps from 1992 to 2015.Source dataThe datasets behind this layer were extracted from NetCDF files and TIFF files produced by ESA. Years 1992-2015 were acquired from ESA CCI LC version 2.0.7 in TIFF format, and years 2016-2018 were acquired from version 2.1.1 in NetCDF format. These are downloadable from ESA with an account, after agreeing to their terms of use. https://maps.elie.ucl.ac.be/CCI/viewer/download.phpCitationESA. Land Cover CCI Product User Guide Version 2. Tech. Rep. (2017). Available at: maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdfMore technical documentation on the source datasets is available here:https://cds.climate.copernicus.eu/cdsapp#!/dataset/satellite-land-cover?tab=doc*Index of all classes in this layer:10 Cropland, rainfed11 Herbaceous cover12 Tree or shrub cover20 Cropland, irrigated or post-flooding30 Mosaic cropland (>50%) / natural vegetation (tree, shrub, herbaceous cover) (<50%)40 Mosaic natural vegetation (tree, shrub, herbaceous cover) (>50%) / cropland (<50%)50 Tree cover, broadleaved, evergreen, closed to open (>15%)60 Tree cover, broadleaved, deciduous, closed to open (>15%)61 Tree cover, broadleaved, deciduous, closed (>40%)62 Tree cover, broadleaved, deciduous, open (15-40%)70 Tree cover, needleleaved, evergreen, closed to open (>15%)71 Tree cover, needleleaved, evergreen, closed (>40%)72 Tree cover, needleleaved, evergreen, open (15-40%)80 Tree cover, needleleaved, deciduous, closed to open (>15%)81 Tree cover, needleleaved, deciduous, closed (>40%)82 Tree cover, needleleaved, deciduous, open (15-40%)90 Tree cover, mixed leaf type (broadleaved and needleleaved)100 Mosaic tree and shrub (>50%) / herbaceous cover (<50%)110 Mosaic herbaceous cover (>50%) / tree and shrub (<50%)120 Shrubland121 Shrubland evergreen122 Shrubland deciduous130 Grassland140 Lichens and mosses150 Sparse vegetation (tree, shrub, herbaceous cover) (<15%)151 Sparse tree (<15%)152 Sparse shrub (<15%)153 Sparse herbaceous cover (<15%)160 Tree cover, flooded, fresh or brakish water170 Tree cover, flooded, saline water180 Shrub or herbaceous cover, flooded, fresh/saline/brakish water190 Urban areas200 Bare areas201 Consolidated bare areas202 Unconsolidated bare areas210 Water bodies

You can also add data from your ArcGIS Online or ArcGIS Enterprise portal, or another portal such as the Living Atlas of the World. In this tutorial, you'll add data from Living Atlas, from your project geodatabase, and from a folder connection.Estimated time: 30 minutesSoftware requirements: ArcGIS ProArcGIS Online or ArcGIS Enterprise portal connection

Bottom classification polygons represent the general type of sediment on the seafloor. Determined from acoustic data analysis and grab sampling.This is a MD iMAP hosted service layer. Find more information at https://imap.maryland.gov.Feature Service Layer Link:https://mdgeodata.md.gov/imap/rest/services/Geoscientific/MD_Soils/MapServer/10

Navigator for ArcGIS is a mobile app that gets your field workforce where it needs to be, unlocking efficiency gains and improving reliability. Learn how it works offline in seamless interaction with ArcGIS field apps. Experience how to use the data provided, your own custom data, or both to search and navigate directly to your organization's assets.This seminar was developed to support ArcGIS Online and Navigator for ArcGIS.

This layer shows the location of Families Clinics and Education and Training Centre in Family Medicine in Hong Kong. It is a subset of the data made available by the Department of Health under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

This vector dataset provides points 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 GolfCoursePolygon 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.

In this seminar, the presenters introduce some of the most popular configurable apps and guide you through the steps to share a web map as a configurable app and then modify app elements to reflect your brand, purpose, and audience. You will see how configurable apps can complete the user experience of your map as well as how to embed your finished app in a website or another app, such as a story map.This seminar was developed to support the following:ArcGIS Online

This scene highlights layers for Berlin, Germany available in ArcGIS to support your work in 3D. Use these layers in conjunction with your own layers to create new scenes focused on a specific topic or area of interest to you.What's in this scene? Terrain: Includes a global 3D terrain layer to provide elevation context. Your layers are placed in relationship to this terrainBasemap: Includes one of the ArcGIS Basemaps regularly used in in your mapping workScene Layers: Includes a layer of 3D buildings to help understand your data within the context of the built environment. The layer is a file type optimized for rendering in 3D.Create your own sceneOpen this item using the Open in Scene Viewer buttonChoose basemap: Select one of the ArcGIS basemaps from the Basemap GalleryAdd your own unique layersCreate slides to direct users to interesting places in your scene - See MoreSave and share the results of your work with others in your organization and the publicFor more see these helpful videosMashup 3D Content Using ArcGIS OnlineAuthor Web Scenes Using ArcGIS Online

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.