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The NakbaArchiveClassifier Shared Task on Nakba Image Classification

The proliferation of social media platforms has significantly reshaped how conflicts are documented, generating large-scale visual records that must be structured to enable meaningful analysis. In this paper, we present the NakbaArchiveClassifier shared task, which focuses on binary classification of infrastructure damage in images from Gaza. This task formed part of the Nakba-NLP Workshop at LREC 2026 and is grounded in an ongoing initiative focused on humanitarian archiving. It utilizes a carefully curated dataset of 2,001 images sourced from Palestinian journalists and content creators on Instagram, spanning the period from October 7, 2023 to December 15, 2025. The objective for participants was to classify whether an image depicts damaged or destroyed infrastructure versus intact structures. This task poses multiple challenges, such as the complexity of real-world conflict imagery, imbalance between classes, and the inherent ambiguity present in many visual scenes. The NakbaArchiveClassifier shared task introduces a new benchmark for analyzing conflict-related visual data and provides valuable resources for advancing research in humanitarian AI, crisis analytics, and Arabic digital humanities.