@inproceedings{kannan-rajalakshmi-2026-dlrg,
title = "{DLRG}@ {N}akba{A}rchive{C}lassifier Shared Task: Deep Transfer Learning for Destruction Detection in Nakba Archive Images Using {E}fficient{N}et-B3",
author = "Kannan, Ramesh R. and
Rajalakshmi, Ratnavel",
editor = "Jarrar, Mustafa and
El-Haj, Mo and
Haddad, Amal and
Atiani, Serin and
Abudalfa, Shadi and
Regier, Terry and
Rayson, Paul and
Sima{'}an, Khalil and
Mansour, Camille",
booktitle = "Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ {LREC} 2026",
month = may,
year = "2026",
address = "Palma, Mallorca (Spain)",
publisher = "ELRA Language Resources Association (ELRA)",
url = "https://aclanthology.org/2026.nakbanlp-1.34/",
doi = "10.63317/4uzdggnax4e2",
pages = "229--233",
abstract = "Automatic identification of destruction in conflict-affected regions is an important task for humanitarian monitoring and historical documentation. Visual analysis of destruction scenes can assist researchers and policy makers in understanding the extent of damage in affected areas. This paper presents a deep learning-based image classification approach for identifying destruction and non-destruction scenes in Nakba-related images. The problem is formulated as binary image classification on Nakba images. A transfer learning approach using EfficientNet-B3 is adopted to learn discriminative visual features from Nakba images. Experimental evaluation shows that the proposed model achieved an Weighted F1-score of 83.87 {\%} and an overall classification accuracy of 85.57 {\%} and secured 10th rank in the competition. The results demonstrate that our proposed pre-trained method can effectively capture structural damage patterns and visual cues associated with destruction scenes. Code: \url{https://github.com/kannanrrk/NakbaImageClassifier}"
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<abstract>Automatic identification of destruction in conflict-affected regions is an important task for humanitarian monitoring and historical documentation. Visual analysis of destruction scenes can assist researchers and policy makers in understanding the extent of damage in affected areas. This paper presents a deep learning-based image classification approach for identifying destruction and non-destruction scenes in Nakba-related images. The problem is formulated as binary image classification on Nakba images. A transfer learning approach using EfficientNet-B3 is adopted to learn discriminative visual features from Nakba images. Experimental evaluation shows that the proposed model achieved an Weighted F1-score of 83.87 % and an overall classification accuracy of 85.57 % and secured 10th rank in the competition. The results demonstrate that our proposed pre-trained method can effectively capture structural damage patterns and visual cues associated with destruction scenes. Code: https://github.com/kannanrrk/NakbaImageClassifier</abstract>
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%0 Conference Proceedings
%T DLRG@ NakbaArchiveClassifier Shared Task: Deep Transfer Learning for Destruction Detection in Nakba Archive Images Using EfficientNet-B3
%A Kannan, Ramesh R.
%A Rajalakshmi, Ratnavel
%Y Jarrar, Mustafa
%Y El-Haj, Mo
%Y Haddad, Amal
%Y Atiani, Serin
%Y Abudalfa, Shadi
%Y Regier, Terry
%Y Rayson, Paul
%Y Sima’an, Khalil
%Y Mansour, Camille
%S Proceedings of the 2nd International Workshop on Nakba Narratives as Language Resources @ LREC 2026
%D 2026
%8 May
%I ELRA Language Resources Association (ELRA)
%C Palma, Mallorca (Spain)
%F kannan-rajalakshmi-2026-dlrg
%X Automatic identification of destruction in conflict-affected regions is an important task for humanitarian monitoring and historical documentation. Visual analysis of destruction scenes can assist researchers and policy makers in understanding the extent of damage in affected areas. This paper presents a deep learning-based image classification approach for identifying destruction and non-destruction scenes in Nakba-related images. The problem is formulated as binary image classification on Nakba images. A transfer learning approach using EfficientNet-B3 is adopted to learn discriminative visual features from Nakba images. Experimental evaluation shows that the proposed model achieved an Weighted F1-score of 83.87 % and an overall classification accuracy of 85.57 % and secured 10th rank in the competition. The results demonstrate that our proposed pre-trained method can effectively capture structural damage patterns and visual cues associated with destruction scenes. Code: https://github.com/kannanrrk/NakbaImageClassifier
%R 10.63317/4uzdggnax4e2
%U https://aclanthology.org/2026.nakbanlp-1.34/
%U https://doi.org/10.63317/4uzdggnax4e2
%P 229-233
Markdown (Informal)
[DLRG@ NakbaArchiveClassifier Shared Task: Deep Transfer Learning for Destruction Detection in Nakba Archive Images Using EfficientNet-B3](https://aclanthology.org/2026.nakbanlp-1.34/) (Kannan & Rajalakshmi, NakbaNLP 2026)
ACL