TY - GEN
T1 - Correspondence between Spectral Reflectance and Features of the Built Environment for Community Resilience
AU - Tamrakar, Shailesh
AU - Helderop, Edward
AU - Nelson, Jake R.
AU - Palladino, Anthony
AU - Farelo, David Goldsztajn
AU - Bienenstock, Elisa J.
AU - Grubesic, Tony H.
AU - Valenti, Andrew
N1 - Funding Information:
The authors would like to thank the SPIE 2022 Geospatial Informatics XII conference organizers and the anonymous referees for their valuable comments. Map data copyright by OpenStreetMap contributors and available from https://www.openstreetmap.org. This research was supported by the Defense Advanced Research Projects Agency (DARPA) under Contract No. 140D0420C0004, and this paper has been approved with Distribution Statement “A” (Approved for Public Release, Distribution Unlimited). The views, opinions, and/or findings contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of DARPA or the U.S. Government.
Publisher Copyright:
© 2022 SPIE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Dense urban areas
KW - community resilience
KW - fragile cities
KW - multispectral analysis
KW - remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85136116560&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85136116560&partnerID=8YFLogxK
U2 - 10.1117/12.2619008
DO - 10.1117/12.2619008
M3 - Conference contribution
AN - SCOPUS:85136116560
T3 - Proceedings of SPIE - The International Society for Optical Engineering
BT - Geospatial Informatics XII
A2 - Palaniappan, Kannappan
A2 - Seetharaman, Gunasekaran
A2 - Harguess, Joshua D.
PB - SPIE
T2 - Geospatial Informatics XII 2022
Y2 - 6 June 2022 through 12 June 2022
ER -