Tech Talk: AI-Assisted Vegetation Risk Forecasting for Railway Corridor Asset Protection

The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership required):
Speaker: Ram Sekhar Bodala ((https://www.google.com/url?q=https://www.linkedin.com/in/ramsekhar/&sa=D&source=calendar&ust=1779483011086337&usg=AOvVaw064oUhFK6Sw5L3ulpF45qm))
Title: AI-Assisted Vegetation Risk Forecasting for Railway Corridor Asset Protection
Abstract: Vegetation-related hazards such as fallen-tree obstructions, signal interference, blocked drainage, and wildfire-driven asset damage increasingly affect railway safety and reliability under changing climate conditions. Conventional vegetation programs are still dominated by periodic inspection cycles and manual patrols, which are poorly aligned with dynamic environmental risk. This paper develops a conference-style framework for AI-assisted vegetation risk forecasting in railway corridors by integrating satellite-derived vegetation indicators, meteorological observations, wildfire exposure factors, and Enterprise Asset Management (EAM) automation. The study is grounded in published literature on railway vegetation risk, Earth observation, wildfire exposure, predictive maintenance, and digital railway asset management. Landsat 8/9, Sentinel-2, vegetation and fuels layers, and meteorological observation families are used as the principal data streams described in the literature, while the proposed method fuses them within a spatiotemporal scoring model for obstruction risk, wildfire susceptibility, and work-order prioritization. Because this paper intentionally avoids uncited portal-derived data and fabricated benchmarks, the Results section synthesizes evidence from published studies and presents analytical figure outputs generated from the proposed equations rather than claiming a new field deployment. The resulting architecture shows how vegetation condition, climate stress, and asset criticality can be combined to support prioritized intervention and automated EAM work-order generation. The paper contributes a reproducible, literature-grounded structure for predictive vegetation management in railways
Bio: I am an Enterprise Asset Management leader with 16+ years of experience in modernizing infrastructure systems across rail, automotive, and renewable energy sectors. Skilled in IBM Maximo, predictive maintenance, and ISO 55000 strategies, I have delivered impactful programs for Amtrak, Ford, and GE. A published researcher and active member of IEEE, BCS, SCRS, and IETE, I am also committed to mentoring the next generation of engineering professionals.
Speaker(s): Ram Sekhar Bodala
Virtual: https://events.vtools.ieee.org/m/560178