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

Deterministic real-time environmental regulatory escalation engine with Pathway streaming, satellite verification, and policy-grounded enforcement

GreenGPT is an AI-powered platform designed to address India's and the world's most pressing environmental challenges through advanced document analysis, real-time chat assistance, and actionable insights generation.

Ecosystem health analysis through sound. Computes ACI, ADI, BIO and NDSI acoustic indices from .wav recordings or live microphone input, with optional BirdNET bird species identification and an auto-generated dashboard.

Predicting alkalinity, conductance and phosphorus in unseen river stations using satellite imagery, climate data and spatial generalization — EY AI & Data Challenge 2026.

Carbon-aware machine learning benchmarking framework evaluating predictive performance alongside energy consumption and CO₂ emissions.

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.

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.

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 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.

The Maltese Domestic Dataset (MDD) is an open-source collection of annotated images of domestic waste bags captured in Maltese urban environments. It supports computer vision research by providing labelled data for training and evaluating object detection models that identify and categorise different types of waste bags.

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

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.

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

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.

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

🔥 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.