Analysing Earth Observation Data for Land Management

Analysing large volumes of earth observation data from satellite missions for tasks like land-use classification, crop monitoring, and phenotyping demands high-performance computational power.

Quantum Convolutional Neural Networks (QCNNs) and hybrid deep learning models are applied to classify multispectral and hyperspectral satellite imagery. This offers better run time and learning efficiency.

This enhances computer vision algorithms for land-use analysis, potentially offering improved prediction accuracy and learning efficiency. Combined with AI/IoT, it aids farmers in monitoring crop health, but also support governing agencies in monitoring land usage.