Advanced AI-Driven Methane Monitoring Dashboard
An interactive visualization platform showcasing our research on methane emission detection, quantification, and localization across Canada. This dashboard leverages multi-source data fusion, satellite observations, climate reanalysis, and geospatial intelligence to provide actionable insights into methane dynamics for environmental monitoring and policy development.
Collected and Fused Data (Download)
This AI system utilizes a multi-source dataset, combining Sentinel-5P satellite observations, ERA5 climate reanalysis data, OpenStreetMap (OSM) geospatial information, and resources from Google Earth Engine (GEE). All datasets are openly accessible for non-commercial use and may be copied, used, and redistributed, provided that proper credit is given to the original data providers and authors.
Download DataHybrid Fusion Framework Overview
Diagram of our multi-source fusion architecture integrating satellite, climate, and industrial datasets.
Hybrid Multi-Source Fusion Framework Visualization
Methane Data Collection Sources
Overview of the diverse data inputs used for methane anomaly detection and prediction.
Sources for Methane Data Collection and Integration
Methane Emission Detection
Detection identifies abnormal methane emission events by analyzing deviations from normal concentration levels.
Abnormal Methane Emission Events Detection
Methane Concentration Distribution by Anomaly Level
Geospatial Multiclass Detection
Hotspot Detection
An interactive map highlighting regions with significantly elevated methane concentrations, helping to identify critical emission hotspots for mitigation efforts.
High-Intensity Methane Hotspot Map
Methane Emission Quantification
In this dashboard, quantification refers to predicting the exact level of methane emissions (measured in parts per billion, ppb) across Canada by using AI models trained on multi-source data, including satellite observations, climate records, and geospatial features.
Monthly Mean Methane Concentration Trends (2020–2024)
Yearly Mean Methane Concentration (2020–2024)
Future Methane Emission Forecast
Building upon the historical trends observed from 2020 to 2024, the dashboard extends its predictive capability to forecast methane emission levels for the next three years (2025–2027). The forecasting model utilizes deep learning architectures (CNN-LSTM and CNN-GRU) trained on multi-source historical datasets to predict future methane concentrations under the assumption of stable meteorological and industrial conditions.
Predicted Monthly Methane Concentration
Forecasted Yearly Mean Methane Concentration
Methane Emission Localization
Localization pinpoints the likely sources of detected methane emissions by analyzing spatial and environmental patterns.