A JavaScript code developed in Google Earth Engine (GEE) Platform to Detect Flooded Area along with Affected Population

This is a Semester Project which aim is to implement a Deep Learning model in order to detect Flood Events from Satellite Images

Flood detection from images using deep learning. Deep learning library KERAS was employed and MobileNet architecture was fine-tuned for image classification task.

Web App for automated change detection in multi temporal satellite images for natural hazard classification.

Generates a merged raster mosaic for the entire AMD0 boundary, overcoming DEA sandbox disk and memory limitations.

2024 IEEE GRSS Data Fusion Contest Track 1

Homeworks for the course Earth Observation Data Analysis, 2020, Sapienza University of Rome

Flood detector using effientnet as semantic segmentation model

This the hardware project made by me and my group members as our semester project.

Flood Detection using U-Net with Attention Mechanism

This repository contains models trained for various purposes during my summer internship at Optical Networks and Technologies Lab.

Homeworks for the course Earth Observation Data Analysis, 2020, Sapienza University of Rome

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

This Shell module allows to filter the sending email of snmp traps to avoid flooding. It works with snmptt among others.

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.

Flood Detection using U-Net with Attention Mechanism