A Semi-Automatic Framework for Dry Beach Extraction Using Photogrammetry 🌍🏆 #WorldResearchAwards


Introduction

The spatial configuration of dry beaches in tailings ponds is a key indicator for assessing tailings dam safety and operational stability. Traditional two-dimensional image-based approaches often fail to capture the true geometric and spatial characteristics required for accurate monitoring. To address these limitations, this research integrates deep learning–based semantic segmentation with three-dimensional reconstruction from UAV imagery, enabling robust, high-precision dry beach extraction and analysis in complex tailings environments.

UAV-based 3d reconstruction for tailings analysis

High-resolution UAV images are used to reconstruct detailed 3D point clouds of tailings ponds, forming the geometric foundation of the proposed method. By deriving the projection matrix for each image, a precise correspondence between 2D image pixels and 3D spatial points is established. This step enables accurate spatial mapping and overcomes the inherent depth ambiguity of conventional 2D dry beach detection methods.

Deep learning-driven image selection and feature extraction

To efficiently identify images containing dry beach boundaries, deep convolutional neural networks, including AlexNet and GoogLeNet, are employed for feature extraction and image classification. This automated image screening step reduces redundancy, improves computational efficiency, and ensures that only informative images contribute to subsequent semantic segmentation and boundary extraction processes.

Semantic segmentation using deeplabv3+

A DeepLabv3+ semantic segmentation network is trained on manually labeled UAV images to accurately distinguish dry beach areas from water, dam structures, and surrounding terrain. The adoption of a lightweight incremental training strategy enhances model adaptability while reducing labeling effort. High segmentation accuracy and Intersection over Union values demonstrate the effectiveness of the network in complex tailings pond scenes.

3D boundary back-projection and spatial consistency

After segmentation, dry beach boundary pixels are detected in 2D images and back-projected into 3D space using the established image–point cloud relationship. This process generates spatially consistent dry beach boundaries directly within the 3D point cloud, enabling precise extraction of dry beach geometry and supporting accurate spatial measurements.

Validation and safety monitoring applications

The proposed framework is validated using multi-phase UAV datasets from a tailings pond in Yunnan Province, China. Results show strong generalization performance across temporal datasets, with stable dam boundary detection and minor degradation in hillside and water regions. The extracted 3D dry beach point clouds support reliable monitoring of dry beach length and deposition morphology, providing valuable insights for tailings dam safety assessment and long-term risk management.

Global Particle Physics Excellence Awards


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Hashtags

#DryBeachExtraction, #TailingsDamSafety, #UAVPhotogrammetry, #3DReconstruction, #DeepLearningResearch, #SemanticSegmentation, #PointCloudAnalysis, #MiningSafety, #GeospatialAnalysis, #EnvironmentalMonitoring, #RemoteSensing, #AIinGeoscience, #DamMonitoring, #InfrastructureSafety, #ComputerVisionResearch, #EarthObservation, #MiningEngineering, #SpatialAnalysis, #ResearchInnovation, #WorldResearchAwards

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