Project Abstract

In this capstone project we propose to use imaging data collected from CUSP’s “Urban Observatory” (UO) facility to build a data processing pipeline and application that can detect and geolocate power outages and restoration in near real-time. The UO’s historical imaging data set includes visible wavelength images of Manhattan at a cadence of roughly 10 seconds per image. An analysis of the lighting variability patterns in these images will be used to identify clusters of lights synchronously turning “off” in the images and with photogrammetric techniques, the geospatial location of those lights will be determined.

Project Description & Overview

The “Urban Observatory” (UO) was first created at CUSP as a facility for studying cities as complex systems using proximal remote sensing (Dobler et al., 2021, Remote Sensing, 13, 8, p.1426). Operationally, the UO consists of imaging devices atop tall buildings located at a distance of 1-5 miles from a city that operate in “survey mode”, continuously acquiring images of the city skyline and transferring those images back to a central server for analysis. Typically, the cadence for image acquisition at visible wavelengths is one image per 10 seconds. Previously, we showed that an analysis of these images at night using signal processing, computer vision, and machine learning techniques yields diurnal patterns of lighting variability for city lights (Dobler et al., 2015, Information Systems, 54, pp.115-126). In this proposal, we seek to leverage this capability to develop a method for automatically detecting power outages in near real time by searching the historical UO imaging data set for collections of “off” transitions of individual light sources that are spatially clustered in the scene and that occur simultaneously, indicating a likely power outage. We will then monitor those sources for the return (via “on” transitions which may or may not be simultaneous) to power restoration. Further, we will use the analysis coupled with simulated outages due to extreme conditions, e.g., hurricanes, to build outage and restoration models that take environmental and infrastructure conditions into account as key input variables for a probabilistic classification of likely outages (Ceferino et al. 2021: https://engrxiv.org/pu5da/).

Datasets

The primary data sets that will be used are the historical CUSP visible wavelength imaging data set (consisting of millions of images at 10s cadence over months and years) that is available on the CUSP servers, publicly available topographic LiDAR for photogrammetric geolocation of outages, and publicly available weather information (winds and precipitation for rain and snow).

Competencies

Students should be familiar with Python and statistical analysis on numerical datasets. Expertise with NumPy and array-based operations is a plus, particularly as it might relate to signal and image processing. Familiarity with geospatial data and operations using GeoPandas is also a plus, as is experience with interactive visualizations (Plotly, Bokeh, etc.) or dashboard design (e.g., JupyterDash).

Learning Outcomes & Deliverables

• Technical learning outcomes:
  • Image processing and computer vision
  • Dashboard design and construction
  • Large scale data fusion methodologies
  • Probabilistic modeling and risk analysis
• Qualitative learning outcomes:
  • Estimates of power distribution continuity
  • Rapid alert systems for outages, even in the absence of monitoring
  • Situational awareness and emergency response assessment

Students

Ruoru Feng, Hongming Liu, Hao Shi