TimeTravel – Automated Image Recognition to Improve Long-term Urban Planning in Response to Pandemics
TimeTravel

TimeTravel – Automated Image Recognition to Improve Long-term Urban Planning in Response to Pandemics

TimeTravel – Automated Image Recognition to Improve Long-term Urban Planning in Response to Pandemics (Expanding on ArchiMediaL)

The corona pandemic endangers lives and livelihoods; it also challenges contemporary European urban planning paradigms that are geared towards high density, public transportation, diversity and compact mixed developments, but current data doesn’t allow yet to fully comprehend and visualize the complex socio-spatial interrelation of long-term urban planning and pandemics. Building on the ArchiMediaL approach (Mager et.al 2019; Khademi et al. 2018), combining research in architectural, urban and planning history with cutting-edge image-recognition tools based on crowd-sourcing and AI technology, Time Travel proposes to:
1. develop a methodology to analyze and visualize the complex socio-spatial urban and regional transformations and the impact on urban planning following pandemics using historical maps and texts. The GIS-based methodology of visualizing socio-spatial, health-related short-and long-term transformations at the regional, urban and architecture scale will be
2. discussed in two workshops (methodological and content-focused) with a group of 40-50 key actors, including public officials, professionals from architectural offices and journalists from the City of Hamburg and the Province of South Holland to inform the ongoing debate on corona-related socio-spatial urban and planning developments and practices and to facilitate future design.

Vacancy

Facts
Applicants:
Prof. Dirk Schubert, HCU HH
Prof. Carola Hein, TU Delft
Ass. Prof. Christoph Lofi, TU Delft

Keywords:
public health, resilient urban planning, crowd-sourcing, AI, stakeholder engagement