Projekt

Daten zum Projekt

SmartSlum: Smart Water Consumption Landscapes for Urban Slum Areas

Initiative: Künstliche Intelligenz – Ihre Auswirkungen auf die Gesellschaft von morgen
Ausschreibung: Künstliche Intelligenz – Ihre Auswirkungen auf die Gesellschaft von morgen - Planning Grant
Bewilligung: 07.02.2019
Laufzeit: 1 Jahr

Projektinformationen

The continued expansion of slum areas to currently about 30% of the population in developing countries presents an immense challenge to urban planning capacities. It has led to the permanent retreat of the state and its public services, such as water provision, from these areas. Vulnerable populations in slums are especially at risk of experiencing highly deficient water supply and sanitation, threatening the pursuit of the UN's Sustainable Development Goal 6 ("clean water and sanitation for all"). In order to extend essential water supply services into urban slums, the society of the future could rely on AI-based Earth Observation (EO) techniques as a basis for a new planning paradigm that restores the reach of the state's planning capacity into these areas. An abundance of EO satellites employing novel sensor technologies and mission concepts are already in service or will be launched in the near future. The resulting unprecedented volumes of data have nurtured a skyrocketing development of machine learning techniques in EO (AI4EO) as a powerful and comparably inexpensive tool to address ever-more complex societal questions, such as predicting poverty risks in developing countries. Based on an exploratory case study of Pune, India, we propose to prepare an interdisciplinary collaboration that combines sophisticated AI4EO techniques with a ground-truthing approach based on socio-economic surveys into a hydro-economic multi-agent model, providing a new urban water management paradigm that will allow cities around the world to expand clean water access in urban slums.

Projektbeteiligte

  • Prof. Dr.-Ing. Xiaoxiang Zhu

    Technische Universität München
    Department of Civil Geo and Environmental
    Engineering
    Signal Processing in Earth Observation
    München

  • Prof. Dr. Bernd Klauer

    Helmholtz-Zentrum für
    Umweltforschung - UFZ
    Department Ökonomie
    Arbeitsgruppe Sozialwissenschaftliche
    Wasserforschung
    Leipzig