@inproceedings{0663f376c34b40ed9dc6d50b43df9806,
title = "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.",
keywords = "geospatial analytics, geospatial information system, LiDAR, remote sensing, satellite imagery",
author = "Klein, \{Levente J.\} and Albrecht, \{Conrad M.\} and Wang Zhou and Carlo Siebenschuh and Sharathchandra Pankanti and Hamann, \{Hendrik F.\} and Siyuan Lu",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE International Conference on Big Data, Big Data 2019 ; Conference date: 09-12-2019 Through 12-12-2019",
year = "2019",
month = dec,
doi = "10.1109/BigData47090.2019.9006600",
language = "English",
series = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5637--5643",
editor = "Chaitanya Baru and Jun Huan and Latifur Khan and Hu, \{Xiaohua Tony\} and Ronay Ak and Yuanyuan Tian and Roger Barga and Carlo Zaniolo and Kisung Lee and Ye, \{Yanfang Fanny\}",
booktitle = "Proceedings - 2019 IEEE International Conference on Big Data, Big Data 2019",
}