BEGIN:VCALENDAR VERSION:2.0 PRODID:IEEE vTools.Events//EN CALSCALE:GREGORIAN BEGIN:VTIMEZONE TZID:America/Los_Angeles BEGIN:DAYLIGHT DTSTART:20260308T030000 TZOFFSETFROM:-0800 TZOFFSETTO:-0700 RRULE:FREQ=YEARLY;BYDAY=2SU;BYMONTH=3 TZNAME:PDT END:DAYLIGHT BEGIN:STANDARD DTSTART:20261101T010000 TZOFFSETFROM:-0700 TZOFFSETTO:-0800 RRULE:FREQ=YEARLY;BYDAY=1SU;BYMONTH=11 TZNAME:PST END:STANDARD END:VTIMEZONE BEGIN:VEVENT DTSTAMP:20260610T021219Z UID:E5941913-509C-4CA8-8D60-D768F8CFEAA9 DTSTART;TZID=America/Los_Angeles:20260527T190000 DTEND;TZID=America/Los_Angeles:20260527T200000 DESCRIPTION:The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership requi red):\n\nSpeaker: Ram Sekhar Bodala ([Connect on LinkedIn](https://www.goo gle.com/url?q=https://www.linkedin.com/in/ramsekhar/&sa=D&source=calendar& ust=1779483011086337&usg=AOvVaw064oUhFK6Sw5L3ulpF45qm))\n\nTitle: AI-Assis ted Vegetation Risk Forecasting for Railway Corridor Asset Protection\n\nA bstract: Vegetation-related hazards such as fallen-tree obstructions\, sig nal interference\, blocked drainage\, and wildfire-driven asset damage inc reasingly affect railway safety and reliability under changing climate con ditions. Conventional vegetation programs are still dominated by periodic inspection cycles and manual patrols\, which are poorly aligned with dynam ic environmental risk. This paper develops a conference-style framework fo r AI-assisted vegetation risk forecasting in railway corridors by integrat ing satellite-derived vegetation indicators\, meteorological observations\ , wildfire exposure factors\, and Enterprise Asset Management (EAM) automa tion. The study is grounded in published literature on railway vegetation risk\, Earth observation\, wildfire exposure\, predictive maintenance\, an d 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 m ethod fuses them within a spatiotemporal scoring model for obstruction ris k\, wildfire susceptibility\, and work-order prioritization. Because this paper intentionally avoids uncited portal-derived data and fabricated benc hmarks\, the Results section synthesizes evidence from published studies a nd presents analytical figure outputs generated from the proposed equation s rather than claiming a new field deployment. The resulting architecture shows how vegetation condition\, climate stress\, and asset criticality ca n be combined to support prioritized intervention and automated EAM work-o rder generation. The paper contributes a reproducible\, literature-grounde d structure for predictive vegetation management in railways\n\nBio: I am an Enterprise Asset Management leader with 16+ years of experience in mode rnizing infrastructure systems across rail\, automotive\, and renewable en ergy sectors. Skilled in IBM Maximo\, predictive maintenance\, and ISO 550 00 strategies\, I have delivered impactful programs for Amtrak\, Ford\, an d 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.\n\nSpeaker(s): Ram Sekhar Bodala\n\nVirtual: https://event s.vtools.ieee.org/m/560178 LOCATION:Virtual: https://events.vtools.ieee.org/m/560178 ORGANIZER:ruben.glatt@ieee.org SEQUENCE:14 SUMMARY:Tech Talk: AI-Assisted Vegetation Risk Forecasting for Railway Corr idor Asset Protection URL;VALUE=URI:https://events.vtools.ieee.org/m/560178 X-ALT-DESC:Description: <br /><p>The San Francisco Bay Area chapter of the IEEE Computer Society invites to our free and open Virtual Tech Talks (no IEEE membership required):</p>\n<p><strong>Speaker:</strong><strong> </str ong>Ram Sekhar Bodala (<a title="https://www.google.com/url?q=https://www. linkedin.com/in/ramsekhar/&amp\;sa=D&amp\;source=calendar&amp\;ust=1779483 011086337&amp\;usg=AOvVaw064oUhFK6Sw5L3ulpF45qm" href="https://www.google. com/url?q=https://www.linkedin.com/in/ramsekhar/&amp\;sa=D&amp\;source=cal endar&amp\;ust=1779483011086337&amp\;usg=AOvVaw064oUhFK6Sw5L3ulpF45qm" tar get="_blank" rel="nofollow noopener noreferrer ugc">Connect on LinkedIn</a >)</p>\n<p><strong>Title: </strong>AI-Assisted Vegetation Risk Forecasting for Railway Corridor Asset Protection</p>\n<p><strong>Abstract:</strong> Vegetation-related hazards such as fallen-tree obstructions\, signal inter ference\, 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 inspectio n cycles and manual patrols\, which are poorly aligned with dynamic enviro nmental risk. This paper develops a conference-style framework for AI-assi sted vegetation risk forecasting in railway corridors by integrating satel lite-derived vegetation indicators\, meteorological observations\, wildfir e exposure factors\, and Enterprise Asset Management (EAM) automation. The study is grounded in published literature on railway vegetation risk\, Ea rth 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 principa l data streams described in the literature\, while the proposed method fus es them within a spatiotemporal scoring model for obstruction risk\, wildf ire susceptibility\, and work-order prioritization. Because this paper int entionally avoids uncited portal-derived data and fabricated benchmarks\, the Results section synthesizes evidence from published studies and presen ts 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 comb ined to support prioritized intervention and automated EAM work-order gene ration. The paper contributes a reproducible\, literature-grounded structu re for predictive vegetation management in railways</p>\n<p><strong>Bio:</ strong> I am an Enterprise Asset Management leader with 16+ years of exper ience 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 Amtra k\, Ford\, and GE. A published researcher and active member of IEEE\, BCS\ , SCRS\, and IETE\, I am also committed to mentoring the next generation o f engineering professionals.</p> END:VEVENT END:VCALENDAR