Situation:
Floods are among the most devastating natural disasters, affecting millions globally. Emergency response teams need faster, more reliable tools to detect flood events and issue warnings in real-time. Traditional methods of monitoring were slow and inaccurate, relying on manual surveys and limited ground sensors

Task:
The goal was to design a data-driven flood detection system that leverages satellite imagery and geospatial analysis to automatically identify and classify flood-prone regions, improving disaster preparedness and government response times.

Action:

• Processed 18 global flood event datasets using Sentinel-1 & Sentinel-2 satellite imagery.
• Built a machine learning classification pipeline using Python + Google Earth Engine.
• Applied Hadoop & HDFS for handling large geospatial datasets.
• Created TF-IDF inspired classification layers for flood pattern detection.
• Developed GIS-enabled dashboards to visualize flood zones and guide evacuation planning.

Result:

• Achieved 94% accuracy in flood classification, a significant improvement over traditional monitoring approaches.
• Reduced time-to-detection, enabling faster government intervention and optimized evacuation routes.
• Delivered a scalable warning system that can be applied globally, supporting disaster relief and risk management initiatives.

Download the Research Paper here: