Tick Tick Bloom: Harmful Algal Bloom Detection Challenge

This repo comes after author's participation in NASA's machine learning competition for cyanobacterial algal bloom severity classification.

<img src="assets/competition_cyano_banner.jpeg" alt="competition_cyano_banner" />

General

• Problem statement: use satellite imagery to detect and classify the severity of cyanobacteria blooms in small, inland water bodies.
• Type: Ordinal regression
• Host: NASA
• Platform: Drivendata
• Competition link: https://www.drivendata.org/competitions/143/tick-tick-bloom/
• Placement: Top 1% (5/1377)
• User Name: Ouranos

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DownLoad Raw data Notebooks

Clima
Geomorphology
Satellites Earth Engine
Satellites Planetary Computer

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Make Datasets Notebook

Make Datasets

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Training and Inference Notebook

Training and inference pipeline below is a simplified version ranked 6th scoring 0.811 on private LB instead of author's best 5th place.

v42

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Citing This Work

This code has been used in the research paper "AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging Sentinel-2, DEM, and NOAA climate data". If you find this code useful, please consider citing it.

This research was published by Remote Sensing Applications: Society and Environment and It's preprint can be found on arxiv.

BibTeX:

@article{nasios2025ai,
  title = {AI-driven multi-source data fusion for algal bloom severity classification in small inland water bodies: Leveraging   Sentinel-2, DEM, and NOAA climate data},
  journal = {Remote Sensing Applications: Society and Environment},
  volume = {40},
  pages = {101800},
  year = {2025},
  issn = {2352-9385},
  author = {Ioannis Nasios}
}

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