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Delivering the Value of Geospatial Analytics into Your Industry 4.0 Initiatives

Dr. A. IstamarCEOEsri Indonesia

W a t e r R e s o u r c e s

T r a n s p a r e n t G o v e r n m e n t

Making a Difference

Our Users

Data Fraud Failure of National Governance

Spread ofInfectious Diseases

Failure of Climate-Change Adaption

Natural Catastrophes

Critical Infrastructure Breakdown

Communicating Effectively

Unemployment

Access to Clean Water

Theft

State Conflict

Biodiversity Loss and Ecosystem Collapse

Cyber Attacks

Communities

Creating Many Challenges

The Pace of Change Is Accelerating

Drought

Mass Migration

Loss of Biodiversity

Ocean Acidification

Deforestation

Population

Climate Change

Pollution

Food

Water

Political Polarization

Urbanization

Energy

Evidence

Threatening Our Natural World

and Our Future as Humans

We Are Now at a Place Where No Humans Have Been . . .

We Need More Understanding

. . . Collaboration and

. . . Action

Social Conflict

Creating a SUSTAINABLE competitive advantage

Design with Nature

Create Sustainable Development

Make CitiesSmarter

IntegrateEnvironmental

Thinking

EngageCitizens

ImproveProductivity

and Efficiency

Leverage Our Best Technology, Geographic Science, and Holistic Design Thinking

ProtectBiodiversity

Keep Moving, Learning, and Understanding . . .

What Should We Do?Apply the Power of Digital Geography

Leveraging the Power of Geography . . .to Better Manage Our World

A Frameworkand Process

Action

Decision-Making

Analysis & Modeling

Planning & Design

Visualization & Mapping

Data Management & Integration

GeospatialInfrastructur

e

What Is Geospatial Infrastructure?

. . . and SolvingProblems Holistically

IntegrationLeveraging Location . . .

. . . and Technology

Foundational . . .. . . Directly Usable

and Services-Based

Content

Geographic Sciences . . .. . . and Visualization

Mapping & Analytics

A Platform for Sharing . . . . . . and Collaboration

. . . GIS at Scale

WebGIS

ArcGISThe Science of Where Is Advancing Rapidly

Integrating and Leveraging Many Innovations

Easier, Open,

and Accessible

Data

Computing GIS Innovation

. . . Enabling Geospatial Infrastructure

Creating Smart, Dynamic Organizations

Using the Power of Location to Integrate Everything

Interconnected Information, Processes, and Workflows . . .

. . . All Happening at the Same Time

Geo-enable the 4th Industrial Revolution

Real-Time Monitoring

Autonomous Vehicles

Smart DevicesIoT

Data-Driven Analytics

Predictive Analytics

Advanced Modeling

Automation

Cloud Computing

Efficiency and Collaboration

Geospatial Solutions

Internet

Location Intelligence

Open Data Access

and Transparency

Geospatial AI

Distributed Architecture

Machine Learning

Big Data

SaaS

ArcGIS itself is part of the Esri Geospatial Cloud

Geo-Enabled

Systems

Indoors

Insights

[ Spatial BI ]

Hub

Urban

Planning

Business

Analytics

GIS

Mapping &

Location

Esri

Field Operations

Spatial Analysis

& Data Science

Imagery

Real Time

3D

Available as SaaS or as Software

and includes distinct product offerings that work well together

Geospatial Capabilities & Geo-Enabled Systems

Developers

Data Management

Creating

a Common Language

Imagery

Lidar

3DVector

Tabular

Real-Time

(IoT)

Big Data

Maps,

Scenes,

Layers

Unstructured

ArcGISIntegrates All Types of Data

BIM

Advanced ModelingEfficiency and Collaboration

Real-Time Monitoring

ImmersiveExperiences

Automation

Artificial Intelligence

Geodesign

PervasiveMappingCloud Computing

Predictive Analytics

Data-Driven Analytics

Autonomous Vehicles

Remote Sensing

Geospatial Solutions

Smart Devices

IoT

Internet

PersistentSurveillance

Location Intelligence

Synthetic Biology

Open Data Accessand Transparency

PervasiveGeographic

Understanding

Digital Transformation Is Just Beginning . . .

Geoplanning

Geoaccounting

Web GIS

Web GIS Is Already Playing a Fundamental Role . . .. . . Integrating Geography Into Everything We Do

Robotics

We Are Living in an Era of Exponential Technological Advancement

New Challenge for National Government : Intelligence Challenges

…Multi-Jurisdiction, Multi-Agency Missions Supporting Complex Operations

Immigration Control Cyber Warfare

Domestic TerrorismBorder Security

Organized CrimeEnergy Security

Natural Disasters

Political Crisis

Adversaries

We Need Integrated Approaches . . .. . . Enabling Better Understanding

Sharing dynamic data across organizations for shared situational awareness

Integrating and analyzing data into actionable information

Deploying mission based applications to support decisions

Disseminating information to multiple devices when and where needed

The Underlying Challenges :

It’s applied everywhere..

Autonomous Cars

Predictive Maintenance

Chatbots

Cancer Detection

Crime Prediction

Personalized Marketing

FacialRecognition

Advanced Video Analytics

Stock Market Prediction

Advanced Satellite Intelligence

WarRobots

Sentiment Analysis

1. AI > ML > DL

Reasoning Knowledge Representation

Perception

RoboticsNLP Machine Learning

features Labels1. Training

2. Predicting

Supervised Learning

Unsupervised LearningReinforcement Learning

Deep Supervised Learning

Artificial Intelligence Machine Learning Deep Learning

Dog

Machine Learning

Deep Learning

Artificial Intelligence

CNTK TensorFlowTheano

Natural Language Processing

Video game behavioral AI

Robotics

Keras

IBM Watson

scikit-learn

Computer Vision

ArcGIS has Machine Learning Tools

ArcGIS

Classification

Clustering

Prediction

Combining the AI power of Microsoft with the geospatial analytics of Esri.

Pre-Configured environments in the cloud for GeoSpatialData Science & AI Modelling, Development & Deployment.

https://azure.microsoft.com/en-us/blog/microsoft-and-esri-launch-geospatial-ai-on-azure/

GeoAI Virtual Machine

ArcGIS Pro

GeoAI Patterns

PredictionObject Detection Pattern Detection

Buildings, Road Segments,

Swimming Pools, Blight,

Graffiti, Overgrowth, Road

Signs, Vehicles from CCTVs,

and more

Water Pipe Breaks, Diseases,

Crimes, Crashes, Incidents,

Fires, Congestion, 911 Calls,

Top Risky Segments, Emerging

Hotspots of 911 Calls, Disease

Clusters, and more

Machine Learning for Oil Well Optimization

Location-based analytics using clustering tool in ArcGIS Pro and scatter plot in Insights for ArcGIS

Build data pipeline

Collect and prepare data

Train ML and DL models to

derive insights

Operationalize models and

distribute insights at scale

Serving business users and end users with intelligent and dynamic

applications

And most aren’t satisfied with their current solutions

“What it Takes to be Data-Driven: Technologies and Best Practices for Becoming a Smarter Organization”, TDWI, 2017.

Are satisfied with ease of

use of analytics software46% 21%Are satisfied with access to semi-

structured and unstructured data 28%Are satisfied with ability to scale to

handle unexpected requirements

Complexity of solutions

Many options in the marketplace

Data silos

Incongruent data types

Difficult to scale effectively

Performance constraints

General purpose machine learning

Azure availability:D, F, L, M, H Series

CPUs

Optimized for flexibility Optimized for performance

GPUs FPGAs

Deep learning

N Series

Specialized hardware accelerated deep learning

Project Brainwave

And most aren’t satisfied with their current solutions

“What it Takes to be Data-Driven: Technologies and Best Practices for Becoming a Smarter Organization”, TDWI, 2017.

Are satisfied with ease of

use of analytics software46% 21%Are satisfied with access to semi-

structured and unstructured data 28%Are satisfied with ability to scale to

handle unexpected requirements

Pre-Configured environments in the cloud for Data Science and AI Development

And most aren’t satisfied with their current solutions

“What it Takes to be Data-Driven: Technologies and Best Practices for Becoming a Smarter Organization”, TDWI, 2017.

Are satisfied with ease of

use of analytics software46% 21%Are satisfied with access to semi-

structured and unstructured data 28%Are satisfied with ability to scale to

handle unexpected requirements

Identifying Broken Insulators via Deep Learning Partial Discharge Detection using Scikit Learn & ArcGIS Pro

Machine Learning for Electricity

Voice Recognition in Map

Detect Objects Using Deep Learning

Detected Objects

Model

Imagery

Detected Objects GP Tool

Advanced Object DetectionDetecting complex Objects from Satellite Imagery using a trained deep learning CNN TensorFlow Model. The model is called on-the-fly via a Raster Function from Pro. Detected Sites are then converted to vector points to apply further spatial analytics

4. ArcGIS Field Apps like Workforce + Collector and Survery123 could be used to plan inspections for detected sites. Operations Dashboard could be used to monitor execution of assigned tasks in Real-TimeInspections Results could then be analyzed in ArcGIS

1. ArcGIS “Export Training Data For Deep Learning” GP tool used to Prepare the a labelled training data set from feature class

2. CNTK or TensorFlow used to train a CNN to detect objects of interest using the labelled training data set

3. ArcGIS Imagery tools used for imagery management and analytics. A Raster Function is used to call the trained CNN and generate the results directly at Pro, allowing for further vector and raster analytics

Palm Detection with Deep Learning

Real-Time Detection of Cracks

GeoAI Sample Use-Cases (Videos)

ETA Prediction Accidents Probability Prediction Smart Road Digitization

High Resolution Land Cover Object Detection from VideosObject Detection from Imagery

How Do We Take the Next Step?

. . . Create a Geoscience-Based Foundation for Our Future

LearnContinuously

Understand the Possibilities

Envision a Better Future

Create Solutions

Inform & Educate

Participate & Take Action

Embrace Technology

Take the Initiative

Work Together

Innovate

Embrace Digital Transformation . . .

. . . and Leverage The Science of Where