Damage Identification in Social Media Posts using Multimodal Deep Learning: code and dataset
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Updated
Sep 7, 2021 - Python
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.
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.
Disaster Tweets API - Production-ready BERT-based FastAPI service for classifying tweets as Disaster or Not Disaster, with Docker, CI, and cloud deployment support.
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.
Dual-architecture landslide detector: RISC-V VisionFive 2 (Python) + 8051 AT89S52 (Assembly) with tiered LED/buzzer alerts via BJT interface. Oscilloscope-verified 55.7 ms timing accuracy. Built at USM.
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.
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.
LLL-based Disaster Detector Agentic AI Application : This project enables the detection and interpretation of environmental threats (e.g., floods, infrastructure risks) by leveraging large language models (LLMs) and multimodal inputs derived from CCTV-based river surveillance feeds.
Implementation of a Deep Neural Architecture to perform real-time semantic segmentation of forest fires in aerial imagery captured by drones.
AI disaster damage detection system using OpenCV, Streamlit, and MySQL with interactive visualization and monitoring dashboard.
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