Damage Identification in Social Media Posts using Multimodal Deep Learning: code and dataset

This project aims at predicting natural disasters using Machine Learning. This project was submitted as a part of Code.Fun.Do++ hackathon organised by Microsoft in 2018.

NLPrescue is an advanced Natural Language Processing system designed to detect and classify disaster-related tweets in real-time. Built with PyTorch and modern NLP techniques, it helps emergency responders quickly identify genuine disaster situations on social media platforms.

Climate Disaster Warning System is a deep learning-based project for detecting wildfires, floods, and sea-level rise using satellite and ground data. It leverages ResNet, Vision Transformer (ViT), and GRACE datasets to support early warning systems and climate research.

Alert Text Detector is an NLP-based model that detects alert messages from social media posts. It is built using BERTweet Base and trained on a dataset of 23,000 tweets (alert & non-alert). The model flags emergency-related messages and classifies tweets based on textual content.

Implementation of a Deep Neural Architecture to perform real-time semantic segmentation of forest fires in aerial imagery captured by drones.

Detect and analyze climate disasters like fires, floods, and earthquakes with our deep learning models. Contribute to climate research and early warning systems. 🌍🚀

Alert Text Detector is an NLP-based model that detects alert messages from social media posts. It is built using BERTweet Base and trained on a dataset of 23,000 tweets (alert & non-alert). The model flags emergency-related messages and classifies tweets based on textual content.