Wang, Zhiyuan und Marconcini, Mattia und Nguyen, Hoang Khanh Linh und Pham, Tung Gia und Bachofer, Felix (2023) Settlement Growth Prediction exploiting EO-based Time Series with the Spatio-Temporal Matrix Approach: a Case Study for the City of Hue, Vietnam. In: 2023 Joint Urban Remote Sensing Event, JURSE 2023, Seiten 1-4. IEEE xplore. 2023 Joint Urban Remote Sensing Event (JURSE), 2023-05-17 - 2023-05-19, Heraklion, Greece. doi: 10.1109/JURSE57346.2023.10144086. ISBN 978-166549373-4. ISSN 2642-9535.
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Kurzfassung
Satellite-based Earth observation (EO) time series data possess enormous potential for analyzing the past and forecasting future trends of urban/settlement development. While historic settlement extent maps with high spatial resolution can be generated from EO data, detailed local information such as intra-urban recreation spaces or restricted areas for specific land use types are hard to acquire. In order to overcome this data gap from which many modelling approaches suffer, the Spatio-Temporal Matrix (STM) was developed. The STM provides spatial and temporal characteristics of a target pixels’ neighborhood to be used for predicting the future urban/settlement growth with a machine learning approach. In this study, a multi-layer perceptron (MLP) was employed to utilize the STM for the settlement growth prediction of the City of Hue, Vietnam. The SLEUTH model was used as a benchmark for the performance evaluation. The results show that the STM-based model achieved a high accuracy in settlement growth modelling. Compared to the SLEUTH model, the STM approach simulated less growth in restricted areas without having to rely on external datasets.
elib-URL des Eintrags: | https://elib.dlr.de/195454/ | ||||||||||||||||||||||||
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Dokumentart: | Konferenzbeitrag (Vortrag) | ||||||||||||||||||||||||
Titel: | Settlement Growth Prediction exploiting EO-based Time Series with the Spatio-Temporal Matrix Approach: a Case Study for the City of Hue, Vietnam | ||||||||||||||||||||||||
Autoren: |
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Datum: | 2023 | ||||||||||||||||||||||||
Erschienen in: | 2023 Joint Urban Remote Sensing Event, JURSE 2023 | ||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||
In SCOPUS: | Ja | ||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||
DOI: | 10.1109/JURSE57346.2023.10144086 | ||||||||||||||||||||||||
Seitenbereich: | Seiten 1-4 | ||||||||||||||||||||||||
Verlag: | IEEE xplore | ||||||||||||||||||||||||
ISSN: | 2642-9535 | ||||||||||||||||||||||||
ISBN: | 978-166549373-4 | ||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||
Stichwörter: | urban, settlement growth, modelling | ||||||||||||||||||||||||
Veranstaltungstitel: | 2023 Joint Urban Remote Sensing Event (JURSE) | ||||||||||||||||||||||||
Veranstaltungsort: | Heraklion, Greece | ||||||||||||||||||||||||
Veranstaltungsart: | internationale Konferenz | ||||||||||||||||||||||||
Veranstaltungsbeginn: | 17 Mai 2023 | ||||||||||||||||||||||||
Veranstaltungsende: | 19 Mai 2023 | ||||||||||||||||||||||||
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 - Fernerkundung u. Geoforschung | ||||||||||||||||||||||||
Standort: | Oberpfaffenhofen | ||||||||||||||||||||||||
Institute & Einrichtungen: | Deutsches Fernerkundungsdatenzentrum > Dynamik der Landoberfläche | ||||||||||||||||||||||||
Hinterlegt von: | Bachofer, Dr. Felix | ||||||||||||||||||||||||
Hinterlegt am: | 31 Jul 2023 12:54 | ||||||||||||||||||||||||
Letzte Änderung: | 24 Apr 2024 20:55 |
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