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 complexity and diversity of military assets increase, ensuring that military forces are well-trained becomes complex and costly when relying on traditional classroom instruction. Human instructors bear the burden of manually creating training datasets with tools that are often not geared to their missions, tasks, and objectives. Further, analyzing how well students learned their tasks often requires collecting and managing student performance over time, which may not be feasible in time-critical situations, and may consume instructor time and attention that could be spent facilitating learning. In addition, while one-to-one human tutoring has proven to be effective, it is costly and impractical to provide in every task domain. We present Multi-task Adaptive Learning Tutor (MALT), a concept for an intelligent tutoring system (ITS) for the psychomotor domain that flexibly responds to a user’s current tasking, information needs, and cognitive ability to interpret information. As the user performs a series of complex psychomotor sub-tasks drawn from flight procedures implemented in a simulator, MALT will learn to predict which features contributed most to their performance. In a proof-of-concept study, we trained MALT using data collected from pilots, ranging from new student pilots to Certified Flight Instructors, while performing different flight procedures. This paper presents the MALT concept, and methods and results associated with the proof-of-concept study focused on MALT’s diagnostic capability. We believe MALT to be among the first to expand the ITS beyond traditional cognitive tasks such as problem solving to include complex psychomotor tasks.
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.
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