Cost-Sensitive Multitask Active Learning for Characterization of Urban Environments With Remote Sensing

Abstract

In this letter, we propose a novel cost-sensitive multi-task active learning (CSMTAL) approach. Cost-sensitive active learning (CSAL) methods were recently introduced to specifically minimize labeling efforts emerging from ground surveys. Here, we build upon a CSAL method but compile a set of unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. To this purpose, a multi-task meta-protocol based on alternating selection is implemented. It comprises a so-called one-sided selection (i.e., single-task AL selection for a reference target variable with simultaneous labeling of the residual target variables) with a changing leading variable in an iterative selection process. Experimental results are obtained for the city of Cologne, Germany. The target variables to be predicted, using features from remote sensing and a Support Vector Machines framework, comprise “building type” and “roof type”. Comparative model accuracy evaluations underline the capability of the CSMTAL method to provide beneficial solutions with respect to a random sampling strategy and non-cost-sensitive multi-task active sampling.