BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//NOAA Center for Earth System Sciences and Remote Sensing Technologies - ECPv6.18.0//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:NOAA Center for Earth System Sciences and Remote Sensing Technologies
X-ORIGINAL-URL:https://www.cessrst.org
X-WR-CALDESC:Events for NOAA Center for Earth System Sciences and Remote Sensing Technologies
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20250309T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20251102T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20260308T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20261101T060000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:EDT
DTSTART:20270314T070000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:EST
DTSTART:20271107T060000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=America/New_York:20261016T143000
DTEND;TZID=America/New_York:20261016T150000
DTSTAMP:20261009T134632Z
CREATED:20261009T134632Z
LAST-MODIFIED:20261009T134632Z
UID:6188-1792161000-1792162800@www.cessrst.org
SUMMARY:NOAA Seminar Series: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts
DESCRIPTION:[vc_row][vc_column][vc_column_text css=””]\n\nTitle: Leveraging Machine Learning and Artificial Intelligence Approaches to Improve Harmful Algal Bloom Monitoring and Forecasts\n \nPresenter(s): Haniel Cordero Nieves\, CESSRST II Graduate Fellow  \nRemote Access: Video call link: https://meet.google.com/out-xrvc-baz\n\nAbstract: \nHarmful algal blooms (HABs) in the Chesapeake Bay are challenging to characterize from satellite observations because of optically complex waters\, mixed phytoplankton communities\, variable environmental conditions\, and limited coincident field observations. This NERTO project developed a reproducible framework integrating historical phytoplankton observations\, NOAA satellite-derived algal bloom products\, geospatial analysis\, ecological information\, and machine-learning methods to support future AI-assisted discrimination of diatom- and dinoflagellate-dominated events. Field observations were matched with georeferenced satellite imagery and evaluated through a structured quality-control workflow addressing mixed pixels\, land contamination\, clouds\, missing data\, non-detect conditions\, saturation\, and coordinate inconsistencies. Of 31\,531 field-to-satellite records evaluated\, 860were initially classified as valid\, while neighboring-pixel analysis identified additional potentially recoverable observations. A preliminary Mini U-Net segmentation model demonstrated the feasibility of the approach but also revealed limitations associated with automatically generated labels\, motivating development of an interactive expert-review tool for event validation\, masking\, and labeling. Ecological analyses additionally explored seasonal phenology and environmental variables as potential features for future multimodal models \n\n\n[/vc_column_text][/vc_column][/vc_row]
URL:https://www.cessrst.org/event/noaa-seminar-series-leveraging-machine-learning-and-artificial-intelligence-approaches-to-improve-harmful-algal-bloom-monitoring-and-forecasts/
CATEGORIES:NOAA Seminar Series,Seminar Series
ORGANIZER;CN="Center for Earth System Sciences and Remote Sensing Technologies (CESSRST)":MAILTO:cessrst@ccny.cuny.edu
END:VEVENT
END:VCALENDAR