Charles Brigham

Geospatial Innovation, Science and Technology

Understanding where we are headed

United Nations World Geospatial Information Congress, Deqing, China

0

ArcGIS Is AdvancingMany New and Extensive Capabilities

Where are we

• AI—Artificial Intelligence

• Machine Learning – An AI Branch

- Problems

- Classification

- Regression

- Clustering

- Anomaly Detection

ArcGIS

Overview

• Imagery

• Big Data

• Machine Learning

• Blockchain

• Bringing it all together for the 2030 Agenda

- Federated System for the SDGs

Professional

Imagery &

Geospatial

Analysts

Server

A Comprehensive Imagery PlatformEmpowering users to make informed decisions

System of

Record

Manage and process

all your imagery

System of

Insight

Extract Information

from Imagery

System of

Engagement

Share imagery products and

information to those that need it

Content:

ArcGISOnline

Partners, Org

• Providing Massively Parallel Processing

• . . . Creating New Insights

Parallelized Analyticson Vector and Raster Data

Lightning Strikes

Spatiotemporal Analysis

Big Data GeoAnalytics and Visualization of Massive Data

Solar Suitability

GeoAnalytics

Server

Image

Server

Supporting Single Scenes . . .

. . . and Massive Collections

Imagery Capabilities for Ortho Mapping, Image Management and Analytics

• Powerful Analysis Tools (100+)

• All Major Sensors

• Standard Workflows

Supporting

• Easy to get started, it is “out of the box analytics”

- Install Image Server on nodes -> start Raster Analytic services -> go

- Your own infrastructure, Amazon, Azure

- Scale up & down as required

• Great user experience

- ArcGIS Pro: visual modeler to design simple and complex models

- Portal Map Viewer: Run Raster Analytics Tools

- Python and REST based API for developers

• Results are immediately available in your Web GIS

- No publishing workflow required

- All outputs are written in parallel to ArcGIS distributed storage

Big Data Raster Analytics

Global SRTM 90m

0

100

200

300

400

500

600

700

800

1 2 4 8 16

790

425

252

126

80

Min

ute

s

Raster Analytics Processorsesri virtual machine

• 16GB RAM, 8 cores, NAS storage

13.12 hours

minutes

terrain suitability model• compute slope

• compute aspect

• remap

• overlay

global terrain suitability raster

Global Terrain Suitability

WebGIS (w/ RasterAnalytics) on Amazon

• 8 c3.2xlarge instances (8 vCPUs, 16GB RAM)

Mean Rainfall

Mean Temperature

Elevation

Landcover

30m National Solar Plant Suitability Raster

Raster Analytics

9 minutes

ArcGIS Desktop (ArcMap or ArcGIS Pro)

~4 hours

suitability model

Solar Power Plant Suitability

(foreach) input scene

top of atmosphere

correction

modified soil adjusted

vegetation index

remap to classes

mask

no data

output thematic raster

Esri Web GIS on AWS

Distributed Raster Analytics Cluster

• single node

• AWS c3.8xlarge

• 60GB RAM, 32 cores, 500GB SSD

• 200 Raster Analytics Processors

Infrastructure ProcessingInput Collection Output

Landsat GLS 1990

• 7422 Multispectral Scenes

• S3 storage

Thematic Rasters

• 7422 Thematic Rasters

• Distributed Raster Datastore

50+ scenes per minute

Image Processing: Landsat

ArcGIS ProArcGIS

Server

ArcPy Extension

Big Data

Where Have We Applied This?

Infrastructure Projects

Nation Wide Technology Change Along Highways

Wireless Telco

Phone -> Sensor → Traffic Info

How do we do this?Geoanalytics Server

GeoAnalytics Cluster

ArcGIS Enterprise

GeoAnalytics Server xN

Read

Save

Result

Hosting ServerPortal DataStore

How do we do this?Out of the Box Geoanalytics Tasks

ArcGIS Pro

Courtesy Nathan Shephard, Esri; Rob van Ede, Geological Survey of the Netherlands, Niels van der Vaart, Esri Netherlands; Holger Lipke, Esri Deutschland GMBH

Blockchain for Logistics

Land Management and Addressing

Government

Buildings

ArcGIS Indoors

Land

Records

ArcGIS

3D

Parcels

Planning

Utilities

Indoors

Imagery

Urban

Planning

ArcGIS Urban

Public

Safety

ArcGIS Online

Citizen

Engagement

ArcGIS Hub Construction

AutoCAD

Models

Costs

Materials

Schedules

Engineering

AutoCAD Civil 3D

Detailed

Design

Revit

Conceptual

Design

InfraWorks

Smart Cities with GIS and BIM

Machine

Learning

Deep

Learning

Artificial Intelligence

CNTK TensorFlowTheano

Natural

Language

Processing

Video game

behavioral AI

Robotic

s

Keras

IBM

Watsonscikit-learn

Computer

Vision

ArcGIS has Machine Learning Tools

ArcGIS

Classification

Clustering

Prediction

Machine Learning Tools in ArcGIS

• Maximum Likelihood

Classification

• Random Trees

• Support Vector Machine

Clustering

• Empirical Bayesian Kriging

• Areal Interpolation

• EBK Regression Prediction

• Ordinary Least Squares

Regression and

Exploratory Regression

• Geographically Weighted

Regression

• Spatially Constrained

Multivariate Clustering

• Multivariate Clustering

• Density-based Clustering

• Image Segmentation

• Hot Spot Analysis

• Cluster and Outlier Analysis

• Space Time Pattern Mining

Classification Prediction

Using the known to estimate the unknown

Accurately predict impacts of climate change on local temperature using global climate

model data

Prediction

In ArcGIS: Empirical Bayesian Kriging, Areal Interpolation, EBK Regression Prediction, Ordinary Least Squares

Regression and Exploratory Regression, Geographically Weighted Regression

The grouping of observations based on similarities of values or locations

Given the nearly 50,000 reports of traffic between 5pm and 6pm in Los Angeles (from

Traffic Alerts by Waze), where are traffic zones that can be used to elicit feedback from

current drivers in the area?

Clustering

In ArcGIS: Spatially Constrained Multivariate Clustering, Multivariate Clustering, Density-based Clustering,

Image Segmentation, Hot Spot Analysis, Cluster and Outlier Analysis, Space Time Pattern Mining

The process of deciding to which category an object should be assigned based on a training dataset

Classify impervious surfaces to help effectively prepare for storm and flood events based

on the latest high-resolution imagery

Classification

In ArcGIS: Maximum Likelihood Classification, Random Trees, Support Vector Machine

GeoAI Examples

ETA Prediction Accidents Probability Prediction Smart Road Digitization

High Resolution Land CoverObject Detection from Imagery Object Detection from Videos

AI can help withPrediction

Clustering Anomaly DetectionObject & Feature Detection Land Classification

Vision

Is Enabling a

Sustainable World

GIS

Applied Example: Federated System for the SDGs

SDG Open Data Hubs

Level of

Difficulty

Narrow / Small Broad / Big

Hard

Ea

sy

Organizational Impact

Level of Difficulty

Data Processing &

Management

Story Maps

Analytics

Dashboards

Open Data

Configure vs Customize

The Key is Open: ArcGIS Is Open and Interoperable

Certifications

. . . Successfully Integrated Into Thousands of Systems

AutoCAD

SAP HANA

Adobe Creative Cloud AWS

AzureMS Office

Oracle

SQL Server

Teradata

NetezzaSharePoint

Jupyter Notebook

Power BI

R Python

Revit PostgreSQL

Altibase

Dameng SQLite

Extensible ArchitectureOpen-Source Contributions

Open APIs & SDKsOpen Data Access Open-Source Integration

REST

OPeNDAPWMS

WCS CSW

GML

WFS

KMLWMTSSLD

GeoPackage

SOAP

IFC

LAS

netCDF

SQL

JSON WaterML CityGML

OneGeologyLERC

Shapefiles

Web Scene (I3S)

INSPIREXLSForm

WPSIMDF

Open Standards and Formats

Direct Product Integration

Open Software Architecture

Embeddable

. . . Many

Hadoop

Driven By Participating Member States. . .

. . . Country Owned and Country Led

Evaluate,

Enhance &

Scale

Operate &

Expand

Initial

Operating

Capability

Assess &

Plan

Commitment: A Federated System for the SDGs

Strengthening the monitoring and

Reporting on the 2030 Agenda

Creating a Open SDG Data Hubs

UN SDGHub Network

Web GIS is Enabling a network of SDG Hubs worldwide

Central Statistics Office &

Ordnance Survey Ireland

Ireland

Philippine

Statistics Authority

Philippines

Instituto Nacional de

Estadística y Geografía

(INEGI)

Mexico

A Global SDG Hub … … And a Network of National SDG Hubs

Palestine Central

Bureau of

Statistics

Palestine

What are the technology components?

ArcGIS

. . . Improved Administration, Quality, and Performance

Users

Apps

Enterprise Web GIS in Your InfrastructureArcGIS Server

Online

Improved

• Portal Integration

• Administration Tools

• Vector Tiles

• Smart Mapping

• Spatial Analysis

• Azure Support

• More OCG Support(KML 2.2, WCS 2.0.1, CWS-3.0)

New

• Distributed Portals

• Big Data Extensions

- Vector

- Raster

- Real-TimePortal

Server

Users

Apps

Integrates with On-Premise Services and Systems

Improved

• Smart Mapping

• Formal Metadata

• Administration

New

• Improved Search

• Advanced Analytics

• WFS / WMTS Support

Online Mapping, Analytics, Apps, and Content

Provides Cloud Based GISArcGIS Online

Data, Services, & Apps

Conceptual & Technical Architecture

Desktop

Data Publishers &

Administrators

ArcGIS Hub

ArcGIS Online

Web

Community

APIs

Developers

Hosted Deployment

Mobile

Field Data

Collection

Conceptual & Technical Architecture

ArcGIS Hub

ArcGIS Online

Hybrid Deployment

Portal

ArcGIS Enterprise

Desktop

Data Publishers &

Administrators

Web

Community

APIs

Developers

Mobile

Field Data

Collection

Desktop

Data Publishers &

Administrators

Charles Brigham

cbrigham@esri.com