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Found 7 papers, 0 papers with code
Disaster Anomaly Detector via Deeper FCDDs for Explainable Initial Responses
This study presents a new solution that offers a disaster anomaly detection application for initial responses with higher accuracy and devastation explainability, providing a novel contribution to the prompt disaster recovery problem in the research area of anomaly scene understanding.
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs
The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters.
One-class Damage Detector Using Deeper Fully-Convolutional Data Descriptions for Civil Application
Furthermore, we propose a valuable solution of deeper FCDDs focusing on other powerful backbones to improve the performance of damage detection and implement ablation studies on disaster datasets.
MN-Pair Contrastive Damage Representation and Clustering for Prognostic Explanation
For infrastructure inspections, damage representation does not constantly match the predefined classes of damage grade, resulting in detailed clusters of unseen damages or more complex clusters from overlapped space between two grades.
VAE-iForest: Auto-encoding Reconstruction and Isolation-based Anomalies Detecting Fallen Objects on Road Surface
We prototype a method that combines auto-encoding reconstruction and isolation-based anomaly detector in application for road surface monitoring.
Flood Inflow Forecast Using L2-norm Ensemble Weighting Sea Surface Feature
It is important to forecast dam inflow for flood damage mitigation.
Natural Disaster Classification using Aerial Photography Explainable for Typhoon Damaged Feature
This paper proposes a practical method to visualize the damaged areas focused on the typhoon disaster features using aerial photography.
