Leveraging Bayesian Neural Networks for multimodal AUV data fusion, enabling precise and uncertainty-aware mapping of underwater environments.

Waste classification system using MobileNetV2 transfer learning. Flask web app with upload, camera capture, and batch processing for 7 waste categories

🔥 Asia-Pacific Fire Anomaly Detection using ESA Fire_cci v5.1 & Isolation Forest ML.

Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for EU case study in v1.4.2 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support EU-specific satellite data formats.

This MVP demonstrates a multi-indicator, high-reliability wildfire detection framework that surpasses conventional approaches. By combining Earth observation with intelligent vector analytics, it opens pathways to operational-scale environmental monitoring.

Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for North America case study in v1.4.3 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support North America-specific satellite data formats.

Deep learning system for climate change analysis using satellite imagery and weather data. Predicts natural disasters, monitors deforestation, tracks glacier melting, and analyzes urban heat islands.

Global Fire Monitoring System v3.2 is an advanced satellite-based fire analysis platform that leverages ESA CEDA Fire_cci data for large-scale global fire pattern detection and clustering analysis. The system processes 12,500+ grid cells simultaneously and provides comprehensive insights into fire behavior patterns across 6 continents.

A deep learning project implementing YOLOv8 for multi-class waste detection and classification using the TACO dataset.

Large-scale fire detection analysis using NASA FIRMS data feat: Add dynamic region support for Africa case study in v1.4.4 - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to support Africa-specific satellite data formats.

This notebook implements a deep learning-based image classification system for identifying different types of garbage (e.g., plastic, paper, metal). It includes custom image preprocessing functions and prepares the dataset for model training.

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.

Large-scale fire detection analysis using NASA FIRMS data. feat: Add dynamic region support for South America case study in v1-4_area - Enabled flexible geospatial parameterization for wildfire analysis - Updated preprocessing pipeline to accommodate South American satellite data formats.

AI-powered environmental sustainability analysis using satellite imagery and deep learning

Deep learning models for automated waste classification using computer vision techniques for environmental sustainability

The MVP provides automated fire risk assessment by extracting wildfire indicators—such as smoke, flame patterns, and thermal anomalies—from imagery, and presenting them in structured natural language analysis.

Large-scale fire detection analysis using NASA FIRMS data

🔥 Advanced satellite-based fire anomaly detection & reasoning system for Africa using ESA Fire_cci v5.1 data with Isolation Forest ML and LLM-based explanations.