Abstract
We describe an “Urban Observatory” facility designed for the study of complex urban systems via persistent, synoptic, and granular imaging of dynamical processes in cities. An initial deployment of the facility has been demonstrated in New York City and consists of a suite of imaging systems—both broadband and hyperspectral—sensitive to wavelengths from the visible (∼400 nm) to the infrared (∼13 micron) operating at cadences of ∼0.01–30 Hz (characteristically ∼0.1 Hz). Much like an astronomical survey, the facility generates a large imaging catalog from which we have extracted observables (e.g., time-dependent brightnesses, spectra, temperatures, chemical species, etc.), collecting them in a parallel source catalog. We have demonstrated that, in addition to the urban science of cities as systems, these data are applicable to a myriad of domain-specific scientific inquiries related to urban functioning including energy consumption and end use, environmental impacts of cities, and patterns of life and public health. We show that an Urban Observatory facility of this type has the potential to improve both a city’s operations and the quality of life of its inhabitants.
Highlights
With millions of interacting people and hundreds of governing agencies, urban environments are the largest, most dynamic, and most complex human systems on earth
In [76,94] we showed that the visible and near-infrared (VNIR) HSI data obtained with the UO can be used to determine the lighting type of individual sources via comparison to spectra measured in the lab and developed Template Activated Partition (TAP) clustering to identify lighting types that were not previously cataloged
We have recently found that applications of regional convolutional neural networks (R-CNNs) [151,152,153] can be tuned to detect such plumes [97] and we have developed end-to-end tracking systems for application to these types of data [154]
Summary
With millions of interacting people and hundreds of governing agencies, urban environments are the largest, most dynamic, and most complex human systems on earth. This advent of large scale urban data collection and availability, and the application of modern statistical methods to that data, has led to the emerging field of “urban science” [24,25,26,27], in which approaches from the natural, computational, and engineering sciences are blended with those from the social and earth sciences to describe emergent phenomena and interactions between the the human, built, and natural environment components of urban systems.
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