Date of Award

Spring 6-4-2025

Document Type

Thesis (Undergraduate)

Department

Computer Science

First Advisor

Alberto Quattrini Li

Abstract

This paper presents a comprehensive implementation

study of an informative path planning (IPP) algorithm

for autonomous water body detection and mapping using

unmanned aerial vehicles (UAVs). We propose a hybrid IPP

framework that seamlessly integrates Bayesian probabilistic

classification and real-time uncertainty quantification to achieve

superior flight efficiency and mapping accuracy compared

to conventional systematic coverage methods. Our approach

employs the state-of-the-art SegFormer deep learning segmentation

model in conjunction with log-odds-based orthomosaic

generation to produce high-fidelity water body maps under

diverse environmental conditions. Through random sampling of

the FloodNet dataset, we demonstrate that our IPP algorithm

maintains flight distance while achieving 14.7% higher coverage

and comparable water-pixel ratios, with mean uncertainty

reduced by approximately 8%. We also sampled a Google

Earth Pro satellite image without a segmentation mask to

qualitatively assess the performance of our IPP algorithm.

Overall, our findings highlight the effectiveness of the proposed

IPP algorithm for efficient and accurate water body detection

and mapping, suggesting promising applications in real-world

environmental monitoring and disaster response.

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