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Artificial Intelligence for Smart Cities

Objective

It is estimated that by 2050, 68% of the world population will live in urban areas, according to the United Nations. With the accelerated rhythm of population growth, the expected number of urban citizens in 2050 is close to 6.5 billion (compared to 4.2 billion nowadays). Uncontrolled urbanization raises a significant problem, the topic of the United Nations Development Programme Goal 11: ensuring sustainable and safe growth of urban areas. This project therefore addresses a global problem. To enable smart urban planning and scalable methods for example to predict the risk of structural degradation or damage to city buildings, it is essential to create efficient tools that can analyse large amounts of data, over time (4D), to create comprehensive global urban maps. The continuous expansion of data sets poses a significant problem for data analytics for global smart urban planning: we currently lack solutions that can generate useful insights from the data.

In this PoC project, I aim to extend the AI algorithms and the big Earth observation data management features developed in the ERC Starting grant to very high resolution data for Smart City applications and offer our software as a commercial, integrated service. Within the PoC, a comprehensive business case that will assist us in designing an exploitation strategy will be developed. Achieving these objectives will advance our AI solution for Smart City from a technology readiness level (TRL) of 4 to 6.

Our value proposition in AI4SmartCities is a set of professional solutions for very high resolution geo-spatial and social-economic indicators for Smart City planning and management by retrieving them from big EO data using AI. For example, high resolution building footprint map, urban building change map, and traffic flow map. Supporting the abovementioned solutions is an easy-to-use, interactive big EO data analysis platform.

Field of science

  • /natural sciences/computer and information sciences/artificial intelligence
  • /engineering and technology/civil engineering/architecture engineering/smart city
  • /natural sciences/computer and information sciences/data science/data analysis

Call for proposal

ERC-2020-PoC
See other projects for this call

Funding Scheme

ERC-POC-LS - ERC Proof of Concept Lump Sum Pilot

Host institution

TECHNISCHE UNIVERSITAET MUENCHEN
Address
Arcisstrasse 21
80333 Muenchen
Germany
Activity type
Higher or Secondary Education Establishments
EU contribution
€ 150 000

Beneficiaries (1)

TECHNISCHE UNIVERSITAET MUENCHEN
Germany
EU contribution
€ 150 000
Address
Arcisstrasse 21
80333 Muenchen
Activity type
Higher or Secondary Education Establishments