An Active Learning Tool for the Generation of Earth Observation Image Benchmarks

This paper describes an active learning tool for the generation of Earth Observation (EO) benchmark datasets. This tool is able to generate training datasets, based on its active learning strategy with a classification accuracy of around 90%. Afterwards, a data cleaning tool is needed, in order to correct noisy data and provide a clean dataset to be stored in the benchmark database, and for subsequent benchmark verification. The data cleaning procedure is supported by unsupervised learning, using clustering algorithms to group similar patterns, and dimension reduction algorithms to embed them in lower dimension with annotated labels. Moreover, interactive visualizations are implemented in most modules to help better manipulate datasets and get better understandings.