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

We propose a novel cost-sensitive multitask 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 multitask 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 machine framework, are 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 noncost-sensitive multitask active sampling.