N-dimensional geospatial data and analytics for critical infrastructure risk assessment

Abstract

The assessment of the vegetation growth rate given remote sensing data is a challenging task in the Earth Observation sciences. LiDAR data acquisition is commonly used to extract height information at a given moment in time, however, the associated cost and complexity restrict continuous acquisitions. Frequently captured aerial imagery can be used to identify and separate vegetation from bare land, water, impervious surface, or built infrastructure. A combination of LiDAR data with aerial and radar imagery allows to track dynamic seasonal growth of vegetation around critical infrastructure such as power lines. We present a general framework that integrates tree identification and growth assessment around power lines with the goal to identify locations of high risk where trees potentially cause power outages.