ArcGIS Pro: What's New and Road Ahead - Esri...Matt Ballard Esri - Solution Engineer ArcGIS Pro:...
Transcript of ArcGIS Pro: What's New and Road Ahead - Esri...Matt Ballard Esri - Solution Engineer ArcGIS Pro:...
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Matt BallardEsri - Solution Engineer
ArcGIS Pro: What’s New and the Road Ahead
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Agenda
• Introduction to ArcGIS Pro
• What’s new in ArcGIS Pro demonstrations
• What’s coming in ArcGIS Pro 2.4 and beyond
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What is ArcGIS Pro
• 2D/3D Integration• 64-bit• Context sensitive• Ribbon framework• Connected to the Portal
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What’s New in ArcGIS Pro 2.3• Map Authoring
- Heatmap Renderer Enhancements
- Polygon Outline Color Ramps
- Clip to Shape
- Multiple Definition Expressions
• Multi Scale Mapping- Vector tile drawing improvements
- Dictionary symbology
• Scene Layers- Building Scene layer
- Mobile Scene Package
- Partial updates of edited scene layers
• Text- Arcade data access
- Dimensions
- Label abbreviation
- Contour Annotation Tool
- Annotation Map Notes
- Anno in Mobile Map Packages
• Layout / Arcpy.mp- Spellcheck in Layout
- Reference Grids
- Map Series GP tools (completed)
- Clip to Shape
- Legend Patch Shapes
- Map Series SDK
• Geodatabase- Contingent Attribute Values
- Attribute Rule Enhancements
- Branch Versioning Enhancements
• Map Exploration- Reports
- Measure Tool UX,
- Animate EAT
- Flicker
• Content Management- Open in ArcGIS Pro & "Start Pro without a template
- LocateXT tools
- Catalog view improvements
• Catalog- Catalog home tab upgrades- Project item contextual tabs- Table preview - Expanding favorites
• Sharing- Publishing to standalone ArcGIS Server with Python- Support WFS, WMS, WCS, and KML
- Create Scene Layer Package
• Editing- Dimensions - Association support- Ground to Grid - Split By Feature- Annotation - Follow Feature- Stereo Editing Improvements- Divide Polygon- Fillet- Midpoint Constructor - Streaming- Generalize
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What’s New in ArcGIS Pro 2.3 - Page 2• Raster
- Deep Learning for Image Classification
- Working in Cloud Stores
• Geoprocessing- Search enhancements
- New GP options
- ModelBuilder Diagram Improvements
- Scatter plot matrix
- Bubble plot
• Spatial Statistics- Generalized Linear Regression
- Geographically Weighted Regression
- Enhancements to RF
• Python- ArcPy Package (off cycle)
- Python Upgrade
- Python Backstage Enhancements
• Revit as a Datasource- Improve 3D/2D georeference experience
- Add additional Revit Geometry/attributes
• Geostatistical Analyst- 3D Interpolation Framework
- 3D Interpolation Tools
- Visualize 2D surface (slice)
• Network Analysis- Edit network dataset properties
- Web tools for NA
- NA Python API Enhancements
• 3D Analysis- Surface feedback in stereo
- Geoprocessing enhancements
• Motion Video- Export to PowerPoint- Record Live Video
- Show Features on Video
- Save Metadata as Features
- Edit Features on Video Window
• Geocoding- Build Next Generation Locators
• Data Reviewer- Rule Authoring (constraint)
- Workflow Manager integration
• Workflow Manager- Functional Equivalency
• Business Analyst- Custom Data
- Infographics
- Territory Design - constraints support
- New GP Tools
• Pipeline Referencing- Network Editing using REST Services
- Pipeline Referencing geoprocessing tools
- Conflict Prevention
• Topo Mapping- Layout Data Generalization
- Data Management Tools
• Maritime- S-101 Edition 4 Updates from IHO
- Improve S-101 Exporter Performance
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What’s new in ArcGIS Pro
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ArcGIS
Classification
Clustering
Prediction
Deep Learning
ArcGIS Includes Machine Learning Tools
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• Pixel & Object Based• Image Segmentation
• Maximum Likelihood
• Random Trees• Support Vector Machine
• Empirical Bayesian Kriging• Areal Interpolation
• EBK Regression Prediction
• Ordinary Least Squares Regression and Exploratory Regression
• Geographically Weighted Regression
Classification
PredictionClustering
• Spatially Constrained Multivariate Clustering
• Multivariate Clustering
• Density-based Clustering
• Hot Spot Analysis• Cluster and Outlier Analysis
• Space Time Pattern Mining
• Export Training Data for Deep Learning • Classify Pixels using Deep Learning
• Detect Objects using Deep Learning
• Non Maximum Suppression
Deep Learning
Machine Learning Tools in ArcGIS
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• Pixel & Object Based• Image Segmentation
• Maximum Likelihood
• Random Trees• Support Vector Machine
• Empirical Bayesian Kriging• Areal Interpolation
• EBK Regression Prediction
• Ordinary Least Squares Regression and Exploratory Regression
• Geographically Weighted Regression
Classification
PredictionClustering
• Spatially Constrained Multivariate Clustering
• Multivariate Clustering
• Density-based Clustering
• Hot Spot Analysis• Cluster and Outlier Analysis
• Space Time Pattern Mining
• Export Training Data for Deep Learning • Classify Pixels using Deep Learning
• Detect Objects using Deep Learning
• Non Maximum Suppression
Deep Learning
Machine Learning Tools in ArcGIS
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ArcGIS ProfessionalArcGIS User
collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
zip
URI
dlpk shared item
feature / image services
zip
1
2
3
45
5
6
6
6
Machine Learning Workflow for ImageryDeep Learning in ArcGIS
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receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
ArcGIS User
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
zip
URI
dlpk shared item
feature / image services
zip
2
3
45
5
6
6
6
ArcGIS Professional
1collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
Machine Learning Workflow for ImageryBuilding a Training Sample Dataset
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receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
ArcGIS ProfessionalArcGIS User
collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
dlpk shared item
feature / image services
5
5
6
6
6
zip
URI
1
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
zip
2
3
4
Machine Learning Workflow for ImageryTraining the Model
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ArcGIS User
collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
zip
URI
zip
1
2
dlpk shared item
feature / image services
ArcGIS Professional
6
6
6
3
45
5
receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
Machine Learning Workflow for ImageryPerforming and Enabling Analysis
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receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
ArcGIS Professional
collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
zip
URI
zip
1
2
3
45
5
dlpk shared item
feature / image services
ArcGIS User
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
6
6
6
Machine Learning Workflow for ImageryFinding Answers
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ArcGIS ProfessionalArcGIS User
collaborates with Data Scientist and uses the
Export Training Data for Deep Learning GP tool in
Pro or on the server
receives model definition packages and configures them for use in ArcGIS
may run inference and publish results
may leverage the ArcGIS API for Python and hosted
jupyter notebooks
uses training data to develop model
delivers deep learning model package
Data Scientist
runs Detect Objects Using Deep Learning GP tool in
Pro or on the server
runs Classify Pixels Using Deep Learning GP tool in
Pro or on the server
consumes model inference results if ArcGIS
Professional prepares them
zip
URI
dlpk shared item
feature / image services
zip
1
2
3
45
5
6
6
6
Machine Learning Workflow for ImageryDeep Learning in ArcGIS
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Well Pad Detection Demonstration
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What’s new in ArcGIS Pro
3D Lease Viewer
3D Empirical Bayesian Kriging
Exploratory Analysis
Drilling Constraints
3D Wellbore Planning
Automated Well Stick Creation
Well Development Planning
3D Analysis and Visualization
Exprodat Unconventionals Analyst
GeoAnalytics on the Desktop
New Charting Tools
Heat Map Symbology in ArcGIS Pro
Map Exploration
Map Flicker
Well Pad Detection
Detected Well Pads
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*Subject to change
What’s coming next
• Animated Symbols• Materials• Multipatch editing in stereo• Heat chart• ModelBuilder to Python• Geoprocessing scheduler• Layer blend modes• Presentations• GPS support• Dynamic feature clustering• Voxel layer sharing• Python notebooks• New Extensions
Near-term Mid-termImprovements
• Parcel Management• Offset printing• Parallel desktop processing using
Spark• Dynamic Feature binning• Projects in the Enterprise• Raster Pixel Editor• Tie Point Manager• Geoprocessing leveraging spatial
databases• Voxel layer• Materials (point symbols)• Animated Water Symbol• Create New Network Dataset
• Terrain Editing• 3D Mesh as ground• High Fidelity rendering
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*Subject to change
What’s coming next
• Animated Symbols• Materials• Multipatch editing in stereo• Heat chart• ModelBuilder to Python• Geoprocessing scheduler• Layer blend modes• Presentations• GPS support• Dynamic feature clustering• Voxel layer sharing• Python notebooks• New Extensions
Near-term Mid-termImprovements
• Parcel Management• Offset printing• Parallel desktop processing using
Spark• Dynamic Feature binning• Projects in the Enterprise• Raster Pixel Editor• Tie Point Manager• Geoprocessing leveraging spatial
databases• Voxel layer• Materials (point symbols)• Animated Water Symbol• Create New Network Dataset
• Terrain Editing• 3D Mesh as ground• High Fidelity rendering
![Page 20: ArcGIS Pro: What's New and Road Ahead - Esri...Matt Ballard Esri - Solution Engineer ArcGIS Pro: What’s New and the Road Ahead \爀屲\爀䔀猀爀椀 䌀漀爀瀀漀爀愀琀攀](https://reader031.fdocuments.net/reader031/viewer/2022013020/5e970bedadad1a54191c906e/html5/thumbnails/20.jpg)
ArcGIS Pro Resources
Training Classes Videos Learn.ArcGIS.com Blogs
Books GeoNet Help Developer Resources
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ArcGIS Pro: What’s new and the road ahead
Matt BallardSolution Engineer – Natural Resources [email protected](713) 205-3850
Thank you!Questions?