Developing an annual and global high-resolution land cover map is one of the most ambitious tasks in remote
sensing, with increasing importance due to the continual rise in validated data and satellite imagery. The
success of land cover classification models largely hinges on the data quality, coupled with the application of
Big Data techniques and distributed computing. This is essential for efficiently processing the extensive volume
of available satellite data. However, maintaining the lifecycle of several annual Machine Learning models
presents a complex challenge. The rise of Machine Learning Operations offers an opportunity to automate the
maintenance of these models, a feature particularly beneficial in systems that require generating new models
each year alongside the continuous integration of validated data. This article details the development of an
end-to-end MLOps workflow, meticulously integrating land cover classification models that employ Big Data
strategies for processing large-scale, high-resolution spatial data. The workflow is designed within a Kubernetes
environment, achieving on-demand auto-scaling, distributed computing, and load balancing. This integration
demonstrates the practicality and efficiency of managing and deploying models that treat satellite imagery in
an automated, scalable framework, thus marking a significant advancement in remote sensing and MLOps.
