Overcoming the Smart City
Challenges:
Promoting Environmental &
Social Sustainability
Valérie Issarny
Inria Paris-Rocquenc...
Leverage ICT to make our cities better place to live
•  Promote Environmental sustainability
•  And Social sustainability
...
The Central Role of
Sensing and Actuation
- 3
3
Both Physical and Social
The CityLabs @ Inria Paris Program
•  From urban-scale sensing & actuation
•  Leveraging the Internet of Things, while
•  ...
Making Cities Smart:
Leveraging the Internet of Things
  Challenges
•  Scale: Number of things starting in
the millions
•...
Making Cities Smart:
From Sensing to Understanding
  Challenges
•  Combining simulation models
and observation through da...
Making Cities Smart:
From Understanding to Evolution
  Challenges
•  Next generation transportation
systems
7
The CityLabs Programme @ Large
•  Bring together research at Inria & CITRIS
•  Paris and San Francisco partnership
•  Grea...
THANK YOU
email: valerie.issarny@inria.fr
9
CityLabs@Inria brings together
Inria Teams at Paris-Rocquencourt:
ARLES, CLIME...
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Innovative city convention 2013 - Workshop 1 Overcoming the smart city challenges - Inria - Valérie Issarny

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Innovative city convention 2013 - Workshop 1 Overcoming the smart city challenges - Inria - Valérie Issarny

  1. 1. Overcoming the Smart City Challenges: Promoting Environmental & Social Sustainability Valérie Issarny Inria Paris-Rocquencourt June 18, 2013
  2. 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 2
  3. 3. The Central Role of Sensing and Actuation - 3 3 Both Physical and Social
  4. 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 4
  5. 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 5
  6. 6. Making Cities Smart: From Sensing to Understanding   Challenges •  Combining simulation models and observation through data assimilation •  Visual analytics 6 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. 7. Making Cities Smart: From Understanding to Evolution   Challenges •  Next generation transportation systems 7
  8. 8. The CityLabs Programme @ Large •  Bring together research at Inria & CITRIS •  Paris and San Francisco partnership •  Great opportunity for challenging projects 8
  9. 9. THANK YOU email: valerie.issarny@inria.fr 9 CityLabs@Inria brings together Inria Teams at Paris-Rocquencourt: ARLES, CLIME, HIPERCOM2, IMARA, SMIS, WILLOW

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