An open-source research repository for artificial intelligence, machine learning, and geospatial approaches to flood risk assessment and flood forecasting.
This repository focuses on developing reproducible, data-driven, and trustworthy methods for understanding, predicting, and mitigating flood risks using remote sensing, hydrological data, climate information, and next-generation AI systems.
- Flood Risk Assessment
- Flood Susceptibility Mapping
- Flood Hazard Mapping
- Flood Forecasting
- Flash Flood Prediction
- Urban Flood Analysis
- Climate Change Impact on Floods
- Hydrological and Hydraulic Modeling
- Disaster Risk Reduction
- Environmental Risk Assessment
- Machine Learning and Deep Learning
- Explainable AI (XAI)
- Trustworthy AI
- Multimodal AI
- Agentic AI & Multi-Agent Systems
- Large Language Models (LLMs)
- Vision-Language Models (VLMs)
- Remote Sensing
- GIS and Spatial Analysis
- Time-Series Forecasting
- Satellite Image Analysis
- Earth Observation
- Multi-source Data Fusion
- Foundation Models
- Physics-Informed AI
- Uncertainty Quantification
- MLOps for Geospatial AI
- Research implementations
- Reproducible experiments
- Paper reproductions
- Literature reviews
- Benchmark datasets
- Data preprocessing pipelines
- Model evaluation frameworks
- Practical tutorials
- Open-source utilities and reusable tools
Our vision is to advance flood risk assessment through trustworthy, multimodal, and agent-based AI systems by integrating satellite imagery, geospatial information, climate data, hydrological observations, and foundation models into intelligent decision-support frameworks for disaster resilience.