AI for carbon emissions monitoring: Computer vision and remote sensing for automated carbon emissions tracking

The exact measurement and scale-up of carbon emissions are essential to fulfill environmental targets and satisfy regulatory standards. The study investigates how AI-powered computer vision and satellite-based remote sensing technologies track industrial sectors' carbon emissions in an automatic and near-real-time manner. The proposed system merges CNNS with spectral analysis and geo-temporal data fusion mechanisms to identify and measure emissions from power plants, manufacturing facilities, and transport centers. The system uses satellite imagery (for example, Sentinel-5p and Landsat) and environmental sensor data to enhance measurement accuracy and spatial resolution. The confidence-weighted emissions estimation model incorporates features to decrease incorrect emissions detection while delivering auditable information streams to ESG auditors and governments. The developed system advances AI-based environmental monitoring technologies while enabling transparent verification and economic analysis, which allows global enforcement of decarbonization strategies.