This paper describes our system submission for the Nakba Archive Image Classification task, which requires predicting the presence of structural destruction in historical archival photographs. We framed this as a binary computer vision classification problem (destruction vs. not_destruction). Our system utilizes a pre-trained ResNet-50 convolutional neural network, adapted for binary output, combined with strategic prediction threshold tuning. Evaluated on the unseen final test set, our model achieved a macro F1-score of 0.450 and a balanced accuracy of 0.527, serving as an exploratory baseline that highlights the unique challenges of processing degraded historical imagery.