TY - JOUR
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, Anthony
AU - Guerini, Cosmo J.
AU - Valenti, Andrew
N1 - Funding Information:
The authors would like to thank the SPIE 2022 Geospatial Informatics XII conference organizers for inviting us to submit our conference proceedings (SPIE paper #12099-2) as a paper to the Journal of Applied Remote Sensing (JARS), and we thank the anonymous JARS referees for their valuable comments. Map data are copyrighted by OSM contributors and available from Ref. . This research was supported by the Defense Advanced Research Projects Agency (DARPA) (Award No. 140D0420C0004) and 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 United States Government.
Publisher Copyright:
© 2023 Society of Photo-Optical Instrumentation Engineers (SPIE).
PY - 2023/1/1
Y1 - 2023/1/1
N2 - 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.
AB - 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.
KW - built environment
KW - classifier
KW - community resilience
KW - convolutional neural network
KW - open data
KW - remote sensing
KW - social capital
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U2 - 10.1117/1.JRS.17.018504
DO - 10.1117/1.JRS.17.018504
M3 - Article
AN - SCOPUS:85151619144
SN - 1931-3195
VL - 17
SP - 18504
JO - Journal of Applied Remote Sensing
JF - Journal of Applied Remote Sensing
IS - 1
ER -