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Building‐damage detection method based on machine learning utilizing aerial photographs of the Kumamoto earthquake
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Analyzing the Performance of Multilayer Neural Networks for Object Recognition
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Building damage annotation on post-hurricane satellite imagery based on convolutional neural networks
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Disaster damage detection through synergistic use of deep learning and 3D point cloud features derived from very high resolution oblique aerial images, and multiple-kernel-learning
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Automatic Detection of Damaged Buildings after Earthquake Hazard by Using Remote Sensing and Information Technologies
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Generative Adversarial Networks
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Nazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment
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Detection of Damaged Rooftop Areas From High-Resolution Aerial Images Based on Visual Bag-of-Words Model
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Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification
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Backpropagation Applied to Handwritten Zip Code Recognition
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Use of Satellite SAR Intensity Imagery for Detecting Building Areas Damaged Due to Earthquakes
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Detection of Building Damage Areas of the 2006 Central Java, Indonesia, Earthquake through Digital Analysis of Optical Satellite Images
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Automatic Structural Seismic Damage Assessment with Airborne Oblique Pictometry© Imagery
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Prototype of a Real-Time System for Earthquake Damage Estimation in Japan
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Development of a Real-Time Damage Estimation System
inferredcites →https://doi.org/10.23919/mva.2017.7986759openalex-snapshot
Damage detection from aerial images via convolutional neural networks
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CLASSIFICATIONS OF STRUCTURAL TYPES AND DAMAGE PATTERNS OF BUILDINGS FOR EARTHQUAKE FIELD INVESTIGATION
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RECOGNITION OF DAMAGED BUILDING USING DEEP LEARNING BASED ON AERIAL AND LOCAL PHOTOS TAKEN AFTER THE 1995 KOBE EARTHQUAKE
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Building Damage Detection from Post-Event Aerial Imagery Using Single Shot Multibox Detector
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Towards Operational Satellite-Based Damage-Mapping Using U-Net Convolutional Network: A Case Study of 2011 Tohoku Earthquake-Tsunami
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Multi-Resolution Feature Fusion for Image Classification of Building Damages with Convolutional Neural Networks
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Building Damage Estimation by Integration of Seismic Intensity Information and Satellite L-band SAR Imagery
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Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery
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Identification of Structurally Damaged Areas in Airborne Oblique Images Using a Visual-Bag-of-Words Approach
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Detection of Urban Damage Using Remote Sensing and Machine Learning Algorithms: Revisiting the 2010 Haiti Earthquake
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Very Deep Convolutional Networks for Large-Scale Image Recognition
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Land Use Classification in Remote Sensing Images by Convolutional Neural Networks
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Bayesian Updating of Damaged Building Distribution in Post-Earthquake Assessment
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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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