Popular GitHub Repositories for Disaster

multimodal social media content (text, image) classification

This repository focuses on training semantic segmentation models to predict the presence of floodwater for disaster prevention. Models were trained using SageMaker and Colab.

Exploration of BERT-BiLSTM models with Layer Aggregation (attention-based and capsule-routing-based) and Hidden-State Aggregation (attention-based and capsule-routing-based).

An Autonomous UAV Assisted Communication in Remote Disaster Area using Imitation Learning (Behavioral Cloning)

Text based Named Entity Recognition data pipeline to annotate, and geo locate disaster related data points.

:rotating_light: Finds the best location for an Emergency Response Unit using Genetic Algorithm. A homework in Computational Intelligence course. :fire_engine: :oncoming_police_car: :ambulance:

This Project is a part of Data Science Nanodegree Program by Udacity in collaboration with Figure Eight. The initial dataset contains pre-labelled tweet and messages from real-life disasters. The aim of this project is to build a Natural Language Processing tool that categorize messages.

We design a multi-task model for joint disaster classification and victim detection. We train the model using both the Centralized Learning (CL) and Federated Learning (FL) methods. We also tried Active Learning (AL) to see how it could help in reducing the labeling workload for disaster dataset. Lastly, we optimized the model using OpenVINO.

The Satellite-Dispatch is an CrewAI-driven platform designed to facilitate disaster response and recovery efforts using satellite technology.

Classification of tweets pertinent to disaster events. NLP basics with a focus on text embedding, interpretability and the use of LIME, and Keras to build a 1D CNN

fine-tuned BERT and scikit-learn models for real-time classification of disaster-related tweets, using TensorFlow, Keras, and Transformers. .

Imagine boarding the Titanic in 2021, and you have provided all your details as a passenger to the captain. There is are three people involved, the data scientist, captain and the passenger. Imagine the company who has built Titanic has created a machine ML model to predict the rate of survival of the passengers, in case of a disaster. The job of the data scientist is to make a model that is explainable to the passengers who are not technical, and that they get the answer about the reasons why they may not survive.

Centralized Disaster Response and Inventory Management System that leverages AI and Google Cloud Technologies to predict disasters, optimize resource management, and provide real-time coordination.

[IEEE Access'22] A Deep Attentive Multimodal Learning Approach for Disaster Identification

End-to-end machine learning pipeline to classify disaster messages into 36 categories and a web app to deploy the trained model.

Scalable Network-Based RL Environment for High-Level EV Natural Disaster Response Simulation

ResqAI is an AI-driven emergency companion that provides real-time weather forecasts, earthquake data, disaster analysis, and emergency response plans to help you stay prepared and informed. Stay safe with the latest news and AI-generated insights.

Analyze a dataset of conversations from the Chatbot Arena, where various LLMs provide responses to user prompts. The goal is to develop a model that enhances chatbot interactions, ensuring they align more closely with human preferences.

This project uses NLP to classify disaster-related messages.

Flood-Image-Detection is a vision-language encoder model fine-tuned from google/siglip2-base-patch16-512 for binary image classification. It is trained to detect whether an image contains a flooded scene or non-flooded environment. The model uses the SiglipForImageClassification architecture.