2020/08/04 by Alessandro Sebastianelli, Sebastianelli, Alessandro, Maria Pia Del Rosso +3
Computer Science · Engineering · #Advanced Computational Techniques and Applications #Advanced Data Processing Techniques #Computational Physics and Python Applications #FOS: Electrical engineering #Image and Video Processing (eess.IV) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2008.01578
openalex publication_date 2020/08/04 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Nowadays the use of Machine Learning (ML) algorithms is spreading in the\nfield of Remote Sensing, with applications ranging from detection and\nclassification of land use and monitoring to the prediction of many natural or\nanthropic phenomena of interest. One main limit of their employment is related\nto the need for a huge amount of data for training the neural network, chosen\nfor the specific application, and the resulting computational weight and time\nrequired to collect the necessary data. In this letter the architecture of an\ninnovative tool, enabling researchers to create in an automatic way suitable\ndatasets for AI (Artificial Intelligence) applications in the EO (Earth\nObservation) context, is presented. Two versions of the architecture have been\nimplemented and made available on Git-Hub, with a specific Graphical User\nInterface (GUI) for non-expert users.\n