Automated Flood Inundation Mapping and Risk Assessment using Remote Sensing and GIS for Urban Watersheds

Project Abstract

This study presents an integrated framework for automated flood inundation mapping and risk assessment in urban watersheds by leveraging remote sensing data and GIS analytics. The research addresses growing flood vulnerability in rapidly urbanizing catchments where traditional hydrological models struggle to scale and adapt to heterogenous urban landscapes. A multi-source data fusion approach combines high-resolution optical and synthetic aperture radar (SAR) imagery, time-series precipitation data, digital elevation models, land use/land cover, and socio-economic indicators to generate near-real-time flood extent maps and probabilistic risk indicators. The methodology advances three core components (i) automated flood detection and delineation using a hybrid classifier that fuses SAR backscatter characteristics with optical spectral indices to robustly identify inundation under varying surface moisture and wind conditions; (ii) a dynamic hydrological modeling pipeline that integrates calibrated rainfall-runoff models with delineated flood extents to simulate flood propagation, depth, and duration at the grid-cell level, and (iii) a risk assessment module that translates physical flood outputs into exposure, vulnerability, and consequence metrics, enabling composite flood risk indices and scenario-based planning outputs. The data assimilation framework incorporates machine learning optimization for feature selection and model parameter tuning, ensuring adaptability to different urban morphologies, drainage configurations, and climate regimes. Validation is conducted across multiple urban watershed cases featuring diverse hydrological responses, building densities, and land cover heterogeneity, using ground truth datasets from field surveys, high-resolution drone imagery, and municipal flood records. The results demonstrate high accuracy in flood extent detection (f1-scores above 0.85 in most sites) and reliable depth estimations within urban flood plains. The risk assessment outputs reveal spatially explicit hotspots where inundation likelihood, population exposure, and critical infrastructure vulnerability converge, providing actionable insights for municipal emergency planning, flood zoning, and green-blue infrastructure interventions. The study also investigates the operational performance of the framework in near-real-time contexts, assessing processing times, data latency, and automation levels, and provides a cost-benefit analysis of deploying such a system within city-scale decision support platforms. Sensitivity analyses identify key drivers of model performance, including temporal resolution of input imagery, SAR calibration parameters, and accuracy of digital elevation representations in densely built environments. The framework supports scenario analysis for rainfall extremes, land-use changes, and drainage upgrades, enabling proactive resilience planning. This work contributes to the field by delivering a scalable, transparent, and transferable methodology that integrates remote sensing techniques with GIS-based spatial analytics to produce timely flood inundation maps and comprehensive risk assessments tailored to urban watershed contexts.

Project Overview

What This Project Is About

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A straightforward, beginner-friendly look at how remote sensing and GIS can be used to map floods in urban areas and assess associated risks. The project combines satellite images, simple data processing, and map-based risk ideas to show when and where floods may happen and who or what is affected.

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The Problem It Addresses

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Urban areas are vulnerable to flooding because of built structures and paved surfaces that change how water flows. Traditional methods can be slow or costly. This project aims to show a practical, accessible way to identify flood-prone zones and prioritize mitigation efforts using available data and easy-to-use tools.

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Objectives of the Project

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1. Explain the basic concepts of flood mapping and risk assessment in simple terms.
2. Show how to collect and prepare lightweight data (like satellite images) for analysis.
3. Demonstrate a basic workflow to identify flood extents and affected areas in a city.
4. Illustrate how to create simple risk indicators (like where people live, how many are affected).
5. Provide an easy-to-follow guide that can be replicated in other urban settings.

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What You Will Do Step by Step

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1. Learn key terms and select a small urban study area.
2. Obtain basic satellite imagery and terrain data.
3. Preprocess data (correct errors, align layers).
4. Identify flood-prone times and map flood extents using simple thresholds.
5. Overlay population or critical infrastructure data to assess impact.
6. Create easy-to-read maps and a simple risk score.
7. Evaluate limitations and discuss practical uses for planners.
8. Document steps so others can repeat the process.

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Expected Outcome

A clear, ready-to-use workflow and a set of basic flood maps and risk indicators for an urban area. The project should produce practical guidance for local authorities and a demonstration that the method is adaptable to other cities with limited data.