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