Scalable approach for high-resolution land cover: a case study in the Mediterranean Basin

Abstract The production of land cover maps is an everyday use image classification applications on remote sensing. However, managing Earth observation satellite data for a large region interest challenging in the task creating maps. Since imagery getting more precise and extensive, Big Data techniques are becoming essential to handle rising quantity data. Furthermore, given complexity analysing data, defining methodology that reduces process into different smaller steps vital processing. This paper presents employing artificial intelligence algorithms. Machine Learning algorithms contemplated sensing geodata classification, supported by explainable intelligence. considers aspects related downloading from satellites, Copernicus ASTER, executing pre-processing processing distributed environment, depicting visualisation result. validated test case er map Mediterranean Basin.