Satsense
SA
A Python library for land use classification based in satellite images.
Updated 29 months ago
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Image: Alex Graves (CC License)
Catastrophes on the scale of the 2011 earthquake, tsunami and nuclear emergency in Japan are thankfully rare. Over the past few years, however, we have seen various emergencies of one sort or another that still have a significant impact on our ability to go about our daily lives, most notably (urban) flooding and aggressive epidemics. What if an infectious disease would threaten to spread throughout the Netherlands (population 17 million) or the dense city of Bangalore (population 10 million)? What would be the effect of various strategies for containment? The Indian Ocean tsunami in 2004 and the disastrous floods in Uttarakhand in 2013 have provided us with examples that require better management of resources. In contrast, as was evident in the case of cyclone Phailin in 2013, planning, preparation and evacuation mitigated the effects of the disaster. How would we evacuate slum dwellers in the city of Bangalore in case of emergency?
It is crucial to have access to the right data and to have methods and tools to assess various scenarios of the possible development and extent of these crisis situations. The good news is that recent advances in experimental techniques have opened up new vistas into physical, biological, and socio-economic processes on many levels of detail.
SIM-CITY will study and develop an agent-based complex network system to interactively explore socio-economic scenarios that can support decision makers of large urban areas, notably greater Amsterdam in the Netherlands and Bangalore in India. The research will provide a decision support infrastructure for three case studies – the Relationship between the Urban Poor and the City in an Increasingly Urbanizing World; Dense City Infectious Disease Pressure; and Evacuation Strategies in Case of Emergencies. In the last case study for example, geographical information system data about the road network in the city will be collected, along with spatial data from the different emergency services, and the capabilities of the emergency services (for example for hospitals: specialty of the hospital, number of beds, number of ambulances, etc.). This will then have to be overlaid with the traffic data we wish to collect through the traffic authorities in the city.
The ultimate goal of this project is to develop theory, data-driven methods, and tools to simulate, understand, and manage complex, urban, socially interactive systems, with a focus on crisis management. A prototype distributed decision support system will be build that will allow for interactively exploring various scenarios of intervention.
In order to achieve the real-time requirements of such living simulations it will be necessary to execute large numbers of complex simulations to evaluate, with statistical confidence, the various possible control scenarios. This will necessitate the use of high performance computing techniques and new methods for rapid parameter sweeping. SIM-CITY will take data from for instance urban census data, demographic data, and traffic and logistics data to distil model parameters and provide support for ‘what if’ simulations to assist the city council in assessing intervening strategies.