Big Data and Cloud Computing for Climate-Oriented Remote Sensing
Abstract
Improved climate change surveillance arises from rapid remote sensing via satellites, UAVs, and sensor networks. However, real-time data processing faces storage and format challenges due to growing data volumes. Deep learning enhances remote sensing for climate monitoring when integrated with big data tools in cloud environments. Scientists use multispectral, hyperspectral, and radar systems for analyzing surface temperatures, deforestation, sea levels, and glaciers. Research on Google Earth Engine's structure explores tool performance between AWS and Azure for geolocation-based deep learning. Image analysis benefits from cloud clusters and statistical models to detect climate anomalies. Cloud-powered systems monitor air pollution and floods. To ensure effectiveness, large-scale systems must follow protocols for data processing, optimization, and quality control. The section concludes with data privacy, then shifts to hybrid edge-cloud models and affordable real-time climate analytics. These methods inform policy with critical adaptation data.