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language: |
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- en |
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--- |
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Source: CrisisMMD dataset (Alam et al., 2017) |
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✅Original Labels (8 classes from annotations): |
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Infrastructure and utility damage |
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Vehicle damage |
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Rescue, volunteering, or donation efforts |
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Affected individuals |
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Injured or dead people |
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Missing or found people |
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Other relevant information |
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Not humanitarian |
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✅Label Preprocessing (Class Merging): |
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Vehicle damage merged into Infrastructure and utility damage |
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Missing or found people merged into Affected individuals |
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Not humanitarian retained as a separate class |
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Removed very low-frequency categories (e.g., "Missing or found people" as a separate class) |
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✅Final Label Set (5 classes total): |
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Infrastructure and utility damage |
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Rescue, volunteering, or donation efforts |
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Affected individuals |
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Injured or dead people |
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Not humanitarian |
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✅Multimodal Consistency: |
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Selected only those posts where text and image annotations matched |
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Resulted in a total of 8,219 consistent samples: |
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Train set: 6,574 posts |
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Test set: 1,644 posts |
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✅ Preprocessing Done Text: |
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Tokenized using BERT tokenizer (bert-base-uncased) |
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Extracted input_ids and attention_mask |
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Image: |
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Processed using ResNet-50 |
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Extracted 2048-dimensional image features |
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The preprocessed data was saved in PyTorch .pt format: |
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train_human.pt and test_human.pt |
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Each contains: input_ids, attention_mask, image_vector, and label |
