Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning
Abstract: Scalable and transferable methods for generating reliable reference data for automated remote sensing approaches are crucial, especially for mapping complex Earth surface processes such as gully erosion in low-populated and inaccessible areas. As an alternative for the labour-intense in-si...
Gespeichert in:
| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
April 2024
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| In: |
ISPRS open journal of photogrammetry and remote sensing
Year: 2024, Jahrgang: 12, Pages: 1-15 |
| ISSN: | 2667-3932 |
| DOI: | 10.1016/j.ophoto.2024.100059 |
| Online-Zugang: | Resolving-System, kostenfrei, Volltext: https://doi.org/10.1016/j.ophoto.2024.100059 Verlag, kostenfrei, Volltext: https://www.sciencedirect.com/science/article/pii/S2667393224000024?via%3Dihub 
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| Verfasserangaben: | Miguel Vallejo, Katharina Anders, Oluibukun Ajayi, Olaf Bubenzer, Bernhard Höfle |
MARC
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| 245 | 1 | 0 | |a Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning |c Miguel Vallejo, Katharina Anders, Oluibukun Ajayi, Olaf Bubenzer, Bernhard Höfle |
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| 520 | |a Abstract: Scalable and transferable methods for generating reliable reference data for automated remote sensing approaches are crucial, especially for mapping complex Earth surface processes such as gully erosion in low-populated and inaccessible areas. As an alternative for the labour-intense in-situ authoritative mapping, collaborative approaches enable volunteers to generate redundant independent geoinformation by digitising Earth observation imagery. We face the challenge of mapping the complex gully outlines integrating multi-user contributions of the same gully network. Comparing Sentinel 2, Bing Aerial, and unoccupied aerial vehicle orthophoto base maps, we examine the volunteered geographic information process and multi-contribution integration using Kalman filtering and machine learning to segment a gully border in a remote area in northwestern Namibia. The Kalman filtering integrates the different lines finding a smoothed solution, and a Random Forest model is used to identify mapping conditions and terrain features as key predictors for evaluating contributors' digitising quality. Assessing results with expert-based reference data, we identify ten contributions as optimal, yielding root mean square distance values of 19.1 m, 15.9 m and 16.6 m, and variability of 2.0 m, 4.2 m and 3.8 m (root mean square distance standard deviation) for Sentinel 2, Bing Aerial, and unoccupied aerial vehicle orthophoto, respectively. Eliminating the lowest performing contributions for Sentinel 2 using a Random Forest regression-based quality indicator improves the accuracy by up to 35% in the root mean square distance compared to a random selection, and up to 54% compared to a supervised remote sensing classification. Results for Sentinel 2 show that low slope, low terrain ruggedness index, and high normalised difference vegetation index values are correlated to high spatial mapping deviations, with Pearson correlation coefficients of −0.61, −0.5, and 0.18, respectively. Our approach is a powerful alternative for authoritative mapping of morphologically complex environmental phenomena and can provide independent reference data for supervised automatic remote sensing analysis. | ||
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