As humans increasingly settle in dense urban areas, localized natural and anthropogenic shocks become more likely to impact larger numbers of individuals. Research suggests that resilience to shocks is a function of physical fortifications and social processes including critical infrastructure, social networks, and trust. Although physical fortifications are relatively easy to identify and catalog, social processes elude simple measurement due to data limitations and geographic constraints. Recent work has shown that certain types of infrastructure may correlate with social processes that enhance community resilience; however, the ability to assess where and to what extent that infrastructure exists depends on a complete representation of the built environment. OpenStreetMap (OSM) and Google Places are two sources of data commonly used to locate and characterize infrastructure, but they are often incomplete. We address this limitation by applying a convolutional neural network (CNN) to remote sensing data from Sentinel-2 to estimate the density and type of infrastructure. We compare the classification results to known infrastructure locations from OSM data. Our results show that the CNN classifier performs well and may be used to augment incomplete datasets for a deeper understanding of the prevalence of infrastructure associated with social processes that enhance community resilience.
As more humans settle in dense urban areas, the effect of natural or anthropogenically induced shocks at these locations has an increased potential to impact larger numbers of individuals. In particular, a disruption to the delivery of goods and services can leave large portions of the population in a vulnerable state. Research suggests that resilience to shocks is a function of physical fortifications and social processes, such as levees and critical infrastructure, the strength of social networks, or community efficacy, and trust. While physical fortifications are relatively easy to identify and catalog, the measurement of social processes is more difficult due to data limitations and geographic constraints. Recent work has shown that certain types of infrastructure may correlate with social processes that enhance community resilience; however, the ability to assess where and to what extent that infrastructure exists depends on a complete representation of the built environment. OpenStreetMap (OSM) and Google Places are two sources of data commonly used to identify the location and type of infrastructure but can display varying degrees of completeness depending on geographic location. We address this limitation by applying a Convolution Neural Network (CNN) to remotely sensed data from Sentinel-2 to estimate the density and type of infrastructure. We compare the classification results to known infrastructure locations from OSM data. Our results show that the CNN classifier performs well and may be used to augment incomplete data sets for a deeper understanding of the prevalence of infrastructure associated with social processes that enhance community resilience.
Diverse sociocultural influences in rapidly growing dense urban areas may induce strain on civil services and reduce the resilience of those areas to exogenous and endogenous shocks. We present a novel approach with foundations in computer and social sciences, to estimate the resilience of dense urban areas at finer spatiotemporal scales compared to the state-ofthe-art. We fuse multi-modal data sources to estimate resilience indicators from social science theory and leverage a structured ontology for factor combinations to enhance explainability. Estimates of destabilizing areas can improve the decision-making capabilities of civil governments by identifying critical areas needing increased social services.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.