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This paper presents an innovative Artificial Intelligence-enabled methodology to predict COVID-19 hotspots within cities based on satellite imagery, building heights information, population density distribution and the geospatial location of public water and sanitation infrastructure to assess whether available floor space enables social distancing...
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... In Kinshasa, the capital of the Democratic Republic of the Congo and one of Africa's largest cities, some 12 million people (84% of the population) live in areas that are predicted by the World Bank to be hotspots for contagion risk, given overcrowding and a lack of basic services. 100 The pandemic also disproportionately affects certain informal occupations. For example, lockdowns prevented waste pickers from working, which translated into lost income. ...
The COVID-19 pandemic has provided opportunities for facial recognition technology and other forms of biometric monitoring to expand into new markets. One anticipated result is the wholesale reconfiguration of shared and public space enabled by the automated identification and tracking of individuals in real time. Drawing on data from several industry trade shows, this article considers the forms of ‘environmental’ governance envisioned by those developing and deploying the technology for the purposes of security, risk management, and profit. We argue that the ‘contactless culture’ that emerged during the COVID-19 pandemic anticipates the normalization of a form of mass-customized biopolitics: the ability to operate on the population and the individual simultaneously through automated forms of passive identification. This form of governance relies not just on machinic recognition, but on the real-time reconfiguration of physical space through automated access controls and the channelling of both people and information.
This chapter outlines the vision for a sustainable, resilient, and livable city in the wake of mega risks. It explores ways and means for making cities more resilient, greener, and smarter to face future unanticipated crises. It is pointed out that cities have survived all kinds of shocks in the past and there is every likelihood that they would sail through the storm intact. However, it does not rule out the possibility of transformation in functions or forms of cities, which is already happening and needs to be guided in the right direction. Important lessons learned include curbing or eradicating the driving forces that caused the mega risks; enhancing cities resilience to avoid their far-reaching impacts in the form of economic recession, enhanced poverty, and inequality by adopting a sustainable development path; following a ‘proactive approach’ rather than a reactive response in handling risks to reduce cities vulnerability; and monitoring the development process in ‘building forward better.’ The chapter expresses concern that all tracking initiatives used so far show that the world has missed the initial opportunity to invest in green recoveries and setting a new course for economic growth and prosperity. It is referred as a warning bell and a wakeup signal to governments and other stakeholders for correcting the course in recovery and future development. The chapter stresses on improving sustainability of cities through the selection of right economic, social, and environmental tradeoffs in the development process; adopting appropriate technologies to make them smart; and designing cities, their buildings, neighborhoods, and public spaces in a way to enhance their resilience and livability. It is pointed out that these are not isolated ideas and that they all fall within an overarching framework of sustainable development goals of 2030 Agenda for Sustainable Development.
Settlements, and in particular cities, are at the center of key future challenges related to global change and sustainable development. Widely used indicators to assess the efficiency and sustainability of settlement development are the compactness and density of the built-up area. However, at global scale, a temporally consistent and spatially detailed survey of the distribution and concentration of the building stock – meaning the total area and volume of buildings within a defined spatial unit or settlement, commonly referred to as building density – does not yet exist. To fill this data and knowledge gap, an approach was developed to map key characteristics of the world's building stock in a so far unprecedented level of spatial detail for every single settlement on our planet. The resulting World Settlement Footprint 3D dataset quantifies the fraction, total area, average height, and total volume of buildings for a measuring grid with 90 m cell size. The World Settlement Footprint 3D is generated using a modified version of the World Settlement Footprint human settlements mask derived from Sentinel-1 and Sentinel-2 satellite imagery at 10 m spatial resolution, in combination with 12 m digital elevation data and radar imagery collected by the TanDEM-X mission. The underlying, automated processing framework includes three basic workflows: one estimating the mean building height based on an analysis of height differences along potential building edges, a second module determining the building fraction and total building area within each 90 m cell, and a third part combining the height information and building area in order to determine the average height and total built-up volume at 90 m gridding. Optionally, a simple 3D building model (level of detail 1) can be generated for regions where data on the building footprints is available. A comprehensive validation campaign based on 3D building models obtained for 19 regions (~86,000 km²) and street-view samples indicating the number of floors for >130,000 individual buildings in 15 additional cities documents that the novel World Settlement Footprint 3D data provides valuable and, for the first time, globally consistent information on key characteristics of the building stock in both, large urban agglomerations as well as small-scale rural settlements. Thus, the new dataset represents a promising baseline dataset for a wide range of previously impossible environmental, socioeconomic, and climatological studies worldwide.
