Real-time 3D Spatial Mapping and Change Detection using UAV-Based Geoinformatics for Coastal Erosion Monitoring

Project Abstract

This study presents a real-time 3D spatial mapping and change detection framework employing UAV-based geoinformatics to monitor coastal erosion with unprecedented temporal and spatial resolution. The research integrates lightweight multispectral, RGB, and LiDAR-inspired depth sensing with high-precision GNSS/IMU navigation to generate dense 3D point clouds and textured meshes of coastal interfaces. A robust workflow is developed to automate data acquisition, preprocessing, co-registration, and real-time processing on edge devices, enabling near-instantaneous visualization and decision support. Key innovations include a multi-sensor fusion strategy that compensates for variable illumination, atmospheric conditions, and wave-driven surface dynamics, as well as a scalable change detection pipeline that distinguishes geomorphological changes from transient disturbances such as tidal cycles and seasonal vegetation. The methodology leverages Structure from Motion (SfM) and Multi-View Stereo (MVS) techniques optimized for coastal environments, combined with LiDAR-like sketched depth cues to enhance terrain delineation in steep or eroding cliffs where traditional photogrammetry struggles. A temporal stack of calibrated 3D models is analyzed using both geometry-based metrics (morphometric volume changes, shoreline retreat, cliff retreat rates, and shoreline-normalized erosion indicators) and texture-change metrics (diffuse reflectance and albedo shifts) to capture complex processes including cliff undercutting, dune migration, toe scour, and shoreline rollback. Real-time capabilities are achieved through a hybrid cloud-edge architecture edge devices perform immediate data processing and feature extraction, while a secure cloud platform conducts long-term storage, cross-epoch change detection, and advanced analytics such as machine learning-based pattern recognition and probabilistic risk assessment. The study introduces a Coastal Erosion Change Index (CECI) that aggregates multiple indicators into a single, interpretable metric for stakeholders, with uncertainty quantification derived from sensor noise models, GNSS drift, and meteorological variability. Validation is conducted across multiple coastal settings with diverse lithologies and hydrodynamic regimes, including sandy beaches, rocky cliffs, and tidal inlets, comparing UAV-derived results against terrestrial lidar surveys and in-situ shoreline measurements to establish accuracy bounds. Applications target coastal management, hazard zoning, and informing soft engineering interventions such as dune nourishment and shoreline stabilization. The framework demonstrates resilience to operational constraints like limited onboard compute and intermittent connectivity by employing adaptive sampling, dynamic flight planning, and data compression techniques that preserve critical morphodynamic information. Outcomes indicate high-fidelity 3D reconstructions with centimeter-level vertical accuracy under favorable conditions and robust performance in challenging environments. The research advances open-source tooling for replication and transferability across regions, contributing a scalable, repeatable, and cost-effective solution for real-time coastal monitoring, disaster risk reduction, and long-term climate adaptation strategies.

Project Overview

What This Project Is About

---

A plain-language overview of how drones (unmanned aerial vehicles) and simple mapping tools can create 3D models of coastlines and track changes over time, focusing on erosion and shoreline shifts. The project combines practical field work with straightforward data processing to show how the coast is changing and why it matters for safety and planning. It avoids heavy math and uses clear visuals to explain results.

---

---

The Problem It Addresses

---

Coastlines change due to waves, storms, and human activity, which can threaten homes, habitats, and infrastructure. Traditional maps may not show fine details or recent changes quickly. The project fills this gap by using affordable drone data to capture up-to-date 3D representations and detect where erosion or deposition is happening.

---

---

Objectives of the Project

---

1. Learn to plan and conduct small-scale drone surveys of a coastal area.
2. Create simple 3D models of the shoreline from collected images.
3. Compare models over time to identify areas of erosion or gain.
4. Present clear visuals and a basic report highlighting key changes.
5. Discuss factors that influence coastal change and potential mitigation ideas.

---

---

What You Will Do Step by Step

---

1. Choose a safe, reachable coastal site and obtain any necessary permissions.
2. Plan drone flights and capture overlapping pictures along the coast.
3. Process photos into a 3D model using simple software (photogrammetry).
4. Extract measurements of shoreline position at different times.
5. Create side-by-side visuals and a basic change map to show differences.
6. Interpret results and discuss possible causes and limitations.
7. Prepare a short written and visual report suitable for non-specialists.
8. Reflect on data quality and ideas for future improvements.

---

---

Expected Outcome

A clear 3D representation of a coastal area at one or more times and a simple change analysis showing where erosion or accretion occurred, plus a student-friendly explanation of what the changes mean for safety and planning.