AI Analysis of City Changes AI Analysi s of City Changes.pdf3.Learning/Big Data Analytics 4.Planning 5.Natural Language Processing 6.Perception –Pattern Recognition –Multimodal

AI Analysis of City Changes

同济大学智能规划协同创新中心

China Intelligent Urbanization Co-creation Center

iCity Group

奥泰因.赫尔佐格Prof. Dr. Otthein HERZOG

德国国家工程院院士Academician, acatech

United Nations World Geospatial Information Congress

Deqing, November 19, 2018

1. City Development Challenges and Artificial Intelligence

2. Analyzing and Classifying City Images with Deep Learning Methods(Work done at CIUC by iCity Group)

3. Examples

Outline

Challenges for Intelligent Cities: Activity Spheres

Energy and resource

infrastructure

Service and

attendance

Politics and

administration

Health

Education

Communication/IT

Human and

lifestyle

Urban Production

Nutrition

Environment

and climate

Transport and Logistics

Buildings and

living

Mobility

and traffic

City structure

Convergence of city

systems

Safety

AI system capabilities are:

1.Reasoning, Problem Solving, Decision support

2.Knowledge Representation/Engineering

3.Learning/Big Data Analytics

4.Planning

5.Natural Language Processing

6.Perception – Pattern Recognition – Multimodal Interfaces

7.Motion and Manipulation/Robotics

8.Social Intelligence

9.Creativity

Artificial Intelligence Areas

Source: Wikipedia, “Artificial Intelligence”

The overall research goal of Artificial Intelligence is to create technology that allows computers and machines to function in an intelligent way.

AI system capabilities for Urban Planning are:

1.Reasoning, Problem Solving, Decision support

2.Knowledge Representation/Engineering

3.Learning/Big Data Analytics

4.Planning

5.Natural Language Processing

6.Perception – Pattern Recognition – Multimodal Interfaces

7.Motion and Manipulation/Robotics

8.Social Intelligence

9.Creativity

Artificial Intelligence Areas

Source: Wikipedia, “Artificial Intelligence”

The overall research goal of Artificial Intelligence is to create technology that allows computers and machines to function in an intelligent way.

7

“Traditional” Software Systems

1 Sense• Audio (Speech)• Video (Photo, Movie)• Digital (Log Data)• IoT (Sensor Data)• Touch (Fingerprint)• Smell (Gas Detection)

2 Comprehend• Specific interpretations of

input data

3 Act• Command Appliances• Trigger follow-up processes• Display meaningful texts• Play translated speech• Draw printings• Compose music

After Purdy, M., & Daugherty, P. (2016). Why Artificial Intelligence is the Future of Growth. Abgerufen am 8. Juni 2017 von https://www.accenture.com/lv-en/_acnmedia/PDF-33/Accenture Why-AI-is-the-Future-of-Growth.pdf

https://www.accenture.com/lv-en/_acnmedia/PDF-33/Accenture

8

Knowledge-based Artificial Intelligence Systems


Explicit Application Domain Knowledge acquired through• Deep Learning• Machine Learning• Statistics/Analytics• Rule Sets• Ontology development

2 Comprehend• Deep Learning• Machine Learning• Statistics/Analytics• Rule Sets• Decision Making




9

Learning Artificial Intelligence Systems


5 Application Domain Knowledge: Learn /Train• From coding to training

2 Comprehend• Deep Learning• Machine Learning• Statistics/Analytics• Rule Sets• Decision Making


4 Feedback• Cause/effect• Right/wrong



Technological Innovation:

World Largest City Big Data Base constantly updated技术创新：建立世界城市大数据，并持续升级追踪超越原最大数据库

1988 1997 2016

4 Generations of City Big

Data Base

Quantitative Analysis

of World Cities

2007

Media-Scale Cities

Simulation• 12 Billion data records in 8 Categories• 集聚了8类120亿条有效数据

美国UIC城市诊断

数据库

团队建构的WUBDB

数据库

120亿

有效

数据

2亿

有效

数据



3. Examples

Outline

City Tree“城市树”

Satellite Images of Cities through 40 years跨度40年城市卫片

Super Speed: Intelligent Identification速度超越:智能识别

Super Precision:30mX30m精度超越:30X30m

EU 1X1km2

欧盟 1X1km2

New representation: City Tree首创“城市树”

2015

2005

2000

1995

1990

1985

1980

1975

S W E N

2010

Spatial Law空间规律

Ningbo


纽约

北京


937个美国 200个

2036个50km2以上的城市

100km2以上的城市




9516Cities over 9km2





通过机器识别技术超越美国最大城市数据库原有的200个城市人工识别记录

Cities over1km2 13810

City Trees for 13810 world cities have been diagrammed by 2018 Jan 18至2018年已绘制13810个城市的城市树

全样本全球范围内识别独立建成区13810个

Full coverage of 13810 independent built areas in the world

year

Area km2

2

2 2 2

1

2y a x b x c= + +

2

1 1 1

1

2y a x b x c= + +

Area2015

Area1975

Classifying Cities by Urban Edge Growth按城市边界增长分类城市的方法

Sprouting萌芽型

Rickets佝偻型

Steady Growth成长型

Matured成熟型

Rapid Growth发育型

Regional区域型

Declining衰退型

Illustrating the Development courses of 13810 world’s cities by City Trees绘制“城市树” 完成了全球13810个城市发展历程的识别

Findings: 7 Categories of Urban Development Modes and the related laws of growth. 发现并定义为7类城市发展类型及其特性，以及城市成长规律。

China

Brazil

Argentina

UK

Germany

France

Matured成熟型

1900

Declining衰退型

201

Spouting萌芽型

435

Regional区域型

143

Rickets佝偻型

3601

Steady 成长型

2365

Rapid 发育型

831

7 Categories of cities by the measure of “City Tree”

7种类型的城市分布

Yangtze River Delta region

1975

1990

2000

2015

◼ 苏州、上海、无锡、常州、南通城镇群集聚历程Urban Agglomeration Process



3. Examples

Outline

长三角城市群京津冀城市群Beijing, Tianjin and Hebei city clusterYangtze River Delta city cluster

长江中游城市群粤港澳湾区Middle Yangtze River City Cluster Guangdong, Hong Kong and Macao Bay Area

美国西海岸美国东海岸U.S. West Coast U.S. East Coast

东京湾五大湖U.S. The Great Lakes region Tokyo Bay Area

Thank you very much for your attention!

Otthein [email protected]

mailto:[email protected]

Documents

AI Analysis of City Changes AI Analysi s of City Changes.pdf3.Learning/Big Data Analytics 4.Planning 5.Natural Language Processing 6.Perception –Pattern Recognition –Multimodal