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Multitask Active Learning for Characterization of Built Environments with Multisensor Earth Observation Data

Geiß, Christian and Thoma, Matthias and Pittore, Massimiliano and Wieland, Marc and Dech, Stefan and Taubenböck, Hannes (2017) Multitask Active Learning for Characterization of Built Environments with Multisensor Earth Observation Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10 (12), pp. 5583-5597. IEEE - Institute of Electrical and Electronics Engineers. DOI: 10.1109/JSTARS.2017.2748339 ISSN 1939-1404

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Official URL: http://ieeexplore.ieee.org/document/8058421/

Abstract

In this paper, we propose a multitask active learning (AL) framework for an efficient characterization of buildings using features from multisensor earth observation data. Conventional AL methods establish query functions based on a preliminary trained learning machine to guide the selection of additional prior knowledge (i.e., labeled samples) for model improvement with respect to a single target variable. In contrast to that, here, we follow three multitask AL metaprotocols to select unlabeled samples from a learning set which can be considered relevant with respect to multiple target variables. In particular, multitask AL methods based on multivariable criterion, alternating selection, rank combination, as well as hybrid approaches, which internalize multiple principles from the different metaprotocols, are introduced. Thereby, the alternating selection strategies implement 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. The multivariable criterion-based methods and rank combination approaches aim to select unlabeled samples based on combined single-task selection decisions. Experimental results are obtained from two application scenarios for the city of Cologne, Germany. Thereby, the target variables to be predicted comprise building material type, building occupancy, urban typology, building type, and roof type. Comparative model accuracy evaluations underline the capability of the introduced methods to provide superior solutions with respect to one-sided selection and random sampling strategies.

Item URL in elib:https://elib.dlr.de/115169/
Document Type:Article
Title:Multitask Active Learning for Characterization of Built Environments with Multisensor Earth Observation Data
Authors:
AuthorsInstitution or Email of AuthorsAuthors ORCID iD
Geiß, Christianchristian.geiss (at) dlr.deUNSPECIFIED
Thoma, Matthiasmatthias.thoma (at) hswt.deUNSPECIFIED
Pittore, Massimilianopittore (at) gfz-potsdam.deUNSPECIFIED
Wieland, Marcmarc.wieland (at) zoo.ox.ac.ukUNSPECIFIED
Dech, Stefanstefan.dech (at) dlr.deUNSPECIFIED
Taubenböck, Hanneshannes.taubenboeck (at) dlr.deUNSPECIFIED
Date:2017
Journal or Publication Title:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Refereed publication:Yes
Open Access:No
Gold Open Access:No
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:10
DOI :10.1109/JSTARS.2017.2748339
Page Range:pp. 5583-5597
Publisher:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:Published
Keywords:Building material type, building occupancy, building type, LiDAR, multitask active learning (AL), roof type, support vector machines (SVM), urban typology, very high resolution imagery
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Erdbeobachtung
DLR - Research theme (Project):R - Vorhaben Sicherheitsrelevante Erdbeobachtung, R - Remote sensing and geoscience
Location: Oberpfaffenhofen
Institutes and Institutions:German Remote Sensing Data Center > Geo Risks and Civil Security
Deposited By: Geiß, Christian
Deposited On:09 Nov 2017 09:05
Last Modified:10 Dec 2018 09:44

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