arXiv Machine Learning June 9, 2026 By Mattia Forlesi, Alfonso Esposito, Ivan Zyrianoff, Alessandro Marzani, Marco Di Felice

Balancing Real and Synthetic Data for CNN-based Masonry Crack Detection

Read the original on arXiv Machine Learning →

arXiv:2606. 08033v1 Announce Type: cross Abstract: Cracks are a critical indicator of building health, and early stage identification is fundamental to prevent harmful damages.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 18

Investigation of Neural Network Methods for Reconstruction and Classification of Texture Images Under Conditions of Incomplete Information

arXiv:2204. 14224v3 Announce Type: replace-cross Abstract: The automated analysis of heterogeneous natural textures is frequently hindered by physical damage and data loss, presenting a significant challenge to computer vision.

By Galymzhan Abdimanap, Kairat Bostanbekov, Abdelrahman Abdallah, Anel Alimova, Darkhan Kurmangaliyev, Daniyar Nurseitov, Tatyana Dedova, Larissa Balakay, Serik Nurakynov

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arXiv AI

Jun 17

Bridging Spatial And Frequency Views For Disaster Assessment: Benefits And Limitations

arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.

By Shikha V. Chandel, Yadav Raj Ghimire, Timothy Agboada, Leila Hashemi-Beni

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arXiv AI

Jun 16

Multi-Modal Attention for Automated Disaster Damage Assessment Using Remote Sensing Imagery and Deep Learning

arXiv:2606. 14963v1 Announce Type: cross Abstract: Timely and accurate disaster damage assessment is crucial for effective emergency response, resource allocation, and recovery.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

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arXiv AI

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Predicting Steel Fatigue Life from Micrographs Using Physics-Informed Deep Learning

arXiv:2607. 28695v1 Announce Type: cross Abstract: Here is the plain text version optimized for arXiv's submission form.

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arXiv AI

Jun 16

Clay-CNN Hybrids: Leveraging Geospatial Foundation Models as Auxiliary Context for Landslide Detection

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By Huong Binh Vu

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arXiv AI

Jun 15

Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

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