1. Overcoming the Smart City
Challenges:
Promoting Environmental &
Social Sustainability
Valérie Issarny
Inria Paris-Rocquencourt
June 18, 2013

2. Leverage ICT to make our cities better place to live
• Promote Environmental sustainability
• And Social sustainability
The Promise of Digital Cities
Leverage ICT to make our cities better place to live
• Promote Environmental sustainability
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3. The Central Role of
Sensing and Actuation
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Both Physical and Social

4. The CityLabs @ Inria Paris Program
• From urban-scale sensing & actuation
• Leveraging the Internet of Things, while
• Addressing the specifics of the target network (scale,
heterogeneity, mobility, …), and
• Enforcing privacy
• … to understanding
• Data assimilation combining simulation models and observations
• Large-scale quantitative visual analysis of urban environments
• … and enabling our cities to evolve
• Next generation transportation systems for our cities
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5. Making Cities Smart:
Leveraging the Internet of Things
 Challenges
• Scale: Number of things starting in
the millions
• Heterogeneity: Wide diversity of
things wrt types and instances
• Participatory sensing: Key role
of mobile phones in sustaining
social and physical sensing
• Unknown topology and data
availability
• Privacy, trust & security
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6. Making Cities Smart:
From Sensing to Understanding
 Challenges
• Combining simulation models
and observation through data
assimilation
• Visual analytics
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powerful models for spatio-temporal, distributed and dynamic visual data. For example, while natural text
vocabulary and grammar are rather well defined, there is no accepted visual equivalent that captures
subtle but important visual differences in architectural styles, or that differentiates fine changes in human
behavior leading to vastly different scene interpretations.
Methodology: This project will build on the considerable progress in visual object, scene and human
action recognition achieved in the last ten years, as well as the recent advances in large-scale scale
machine learning that enable optimizing complex structured models using massive sets of data. The
project will develop a general framework for finding, describing and quantifying dynamic visual patterns,
such as architectural styles or human behaviors, distributed across many dynamic scenes from urban
environments. The models will be automatically learnt from visual data with different forms of readily-
available but noisy and incomplete metadata such as text, geotags, or publicly available map-based
information (e.g. the type or use of buildings). Our initial results in this direction on static Street-view
images have been published in [Doersch12] and are illustrated in figure 1.
Figure 1: Quantitative visual analysis of urban environments from street-view imagery.
a: Examples of architectural visual elements characteristic for Paris, Prague and London automatically
learnt by analyzing thousands of Street-view images. b: An example of a geographic pattern (shown as
red dots on the map of Paris) for one visual element. Here balconies with cast-iron railings are
concentrated on the main boulevards. Figure from [Doersch12].

7. Making Cities Smart:
From Understanding to Evolution
 Challenges
• Next generation transportation
systems
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8. The CityLabs Programme @ Large
• Bring together research at Inria & CITRIS
• Paris and San Francisco partnership
• Great opportunity for challenging projects
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9. THANK YOU
email: valerie.issarny@inria.fr
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CityLabs@Inria brings together
Inria Teams at Paris-Rocquencourt:
ARLES, CLIME, HIPERCOM2, IMARA, SMIS, WILLOW