We summarize the first major effort to use analytics for preemptive maintenance and repair of an electrical distribution network. This is a large-scale multi-year effort between scientists and students at Columbia and MIT and engineers from Con Edison, which operates the world's oldest and largest underground electrical system. Con Edison's preemptive maintenance programs are less than a decade old, and are made more effective with the use of analytics developing alongside the maintenance programs themselves. Some of the data used for our projects are historical records dating as far back as the 1880's, and some of the data are free text documents typed by dispatchers. The operational goals of this work are to assist with Con Edison's preemptive inspection and repair program, and its vented cover replacement program. This has a continuing impact on public safety, operating costs, and reliability of electrical service in New York City.

Satellite radar imaging from SAR (Synthetic Aperture Radar) is a remote sensing technology that captures ground surface level changes at a relatively high resolution. This technology has been used in many applications, one of which is the estimation of damages after natural disasters, such as wildfire, earthquake, and hurricane events. An efficient and accurate assessment of damages after natural catastrophe events allows public and private sectors to quickly respond in order to mitigate losses and to better prepare for disaster relief. Advances in machine learning and image processing techniques can be applied to this dataset to survey large areas and estimate property damages. In this paper, we introduce a machine learning-based approach for taking satellite radar images and geographical data as inputs to classify the damage status of individual buildings after a major wildfire event. We believe the demonstration of this damage estimation methodology and its application to real world natural disaster events will have a high potential to improve social resilience.

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