Serverless Hyperspectral Image Processing: A Cloud-Native Machine Learning Architecture for UAV-Assisted Agricultural and Disaster Remote Sensing

The integration of Unmanned Aerial Vehicles equipped with hyperspectral and multispectral sensors has revolutionized remote sensing in agriculture, forestry, and disaster management. However, hyperspectral imaging generates extraordinarily dense, high-dimensional datasets that overwhelm traditional, localized processing software. This computational bottleneck delays critical land-use classifications and disaster response assessments. This paper proposes a cloud-native architectural framework utilizing Amazon Web Services to construct a real-time, serverless machine learning pipeline for hyperspectral data processing. By deploying asynchronous Python middleware integrated with automated dimensionality reduction and eXtreme Gradient Boosting classification algorithms, the proposed system programmatically ingests massive telemetry bursts from UAV flight operations. The system dynamically reduces spectral noise and translates the data into immediate, high-accuracy geographic classifications. Preliminary architectural evaluations demonstrate that decoupling hyperspectral data processing from localized desktop environments to a distributed cloud infrastructure significantly accelerates image classification, providing GIS analysts with a deterministic, highly scalable tool for autonomous environmental monitoring.