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Boosted machine learning ensemble regression with decision fusing strategy for mapping built-up height and built-up density with openstreetmap data and SENTINEL-2 imagery

Geiß, Christian und Schrade, Henrik und Taubenböck, Hannes (2018) Boosted machine learning ensemble regression with decision fusing strategy for mapping built-up height and built-up density with openstreetmap data and SENTINEL-2 imagery. 5th EARSeL Joint Workshop “Urban Remote Sensing – Challenges & Solutions”, 2018-09-24 - 2018-09-26, Bochum, Germany.

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Kurzfassung

Detailed characterization of urban environments for large areas is a challenging but crucial task for e.g., analyzing urbanization processes and assessing vulnerability and risks with respect to natural hazards, among others. Recent Earth Observation missions feature a notable tradeoff between a fairly high spatial resolution and large area coverage. In particular, ESA’s Sentinel-2 satellites provide superspectral imagery with a spatial resolution of 10 meters for the bands covering visible light and near infrared. Notably, Sentinel-2 data are provided free of charge to the public via a data hub, which is accessible online. In parallel, open-source geoinformation compiled by volunteers such as OpenStreetMap (OSM) can provide valuable information for characterization of urban environments. Numerous cities around the globe were already mapped in detail. Such data sets allow for instance the computation of built-up heights and built-up densities. However, spatially continuous and detailed OSM data are not available everywhere. To allow for truly large-area application, we combine OSM data and Sentinel-2 imagery and render the mapping of built-up heights and built-up densities as a supervised learning problem. Given the rational level of measurement of the two variables to be predicted, we generate a regression model based on Areas where data from both OSM and Sentinel-2 are available in order to estimate the target variables for areas where only Sentinel-2 data is available: The regression estimation problem is regarded as finding the mapping between an incoming vector (i.e., ubiquitously available image features from Sentinel-2) and an observable output from a given set of samples (i.e., automatically derived over spatially limited areas where data from both OSM and Sentinel-2 are available). From a methodological point of view, we introduce a novel regression approach to account for the No-free-Lunch-Theorem, which states that there is no algorithm that induces the most accurate learner in any domain, all the time. As such, we combine the model outputs of advanced machine learning-based Regression algorithms incorporating Support Vector Regression, Regression Trees, Gaussian Process Regression, and Neural Networks within a decision fusion framework. We extend this method within an ensemble learning approach, and learn individual models based on bootstrap Aggregation from the training data to further increase accuracy and robustness of predictions. Experimental results from the city of Cologne, Germany, underline the viability of the approach.

elib-URL des Eintrags:https://elib.dlr.de/123038/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Boosted machine learning ensemble regression with decision fusing strategy for mapping built-up height and built-up density with openstreetmap data and SENTINEL-2 imagery
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Geiß, Christianchristian.geiss (at) dlr.dehttps://orcid.org/0000-0002-7961-8553NICHT SPEZIFIZIERT
Schrade, Henrikhenrik.schrade (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Taubenböck, Hanneshannes.taubenboeck (at) dlr.dehttps://orcid.org/0000-0003-4360-9126NICHT SPEZIFIZIERT
Datum:2018
Referierte Publikation:Nein
Open Access:Nein
Gold Open Access:Nein
In SCOPUS:Nein
In ISI Web of Science:Nein
Status:veröffentlicht
Stichwörter:mapping of built-up height and density
Veranstaltungstitel:5th EARSeL Joint Workshop “Urban Remote Sensing – Challenges & Solutions”
Veranstaltungsort:Bochum, Germany
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:24 September 2018
Veranstaltungsende:26 September 2018
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Sicherheitsrelevante Erdbeobachtung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Deutsches Fernerkundungsdatenzentrum > Georisiken und zivile Sicherheit
Hinterlegt von: Geiß, Christian
Hinterlegt am:12 Nov 2018 12:14
Letzte Änderung:24 Apr 2024 20:27

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