Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning

• Verfasst von: Vallejo Orti, Miguel [VerfasserIn]
• Verfasst von: Anders, Katharina [VerfasserIn]
• Verfasst von: Ajayi, Oluibukun [VerfasserIn]
• Verfasst von: Bubenzer, Olaf [VerfasserIn]
• Verfasst von: Höfle, Bernhard [VerfasserIn]
• Titel: Integrating multi-user digitising actions for mapping gully outlines using a combined approach of Kalman filtering and machine learning
• Verf.angabe: Miguel Vallejo, Katharina Anders, Oluibukun Ajayi, Olaf Bubenzer, Bernhard Höfle
• E-Jahr: 2024
• Jahr: April 2024
• Umfang: 15 S.
• Fussnoten: Online veröffentlicht: 10. Februar 2024 ; Gesehen am 26.02.2024
• Titel Quelle: Enthalten in: ISPRS open journal of photogrammetry and remote sensing
• Ort Quelle: Amsterdam : Elsevier, 2021
• Jahr Quelle: 2024
• Band/Heft Quelle: 12(2024) vom: Apr., Artikel-ID 100059, Seite 1-15
• ISSN Quelle: 2667-3932
• DOI: doi:10.1016/j.ophoto.2024.100059
• Datenträger: Online-Ressource
• Sprache: eng
• Bibliogr. Hinweis: Forschungsdaten: Vallejo Orti, Miguel, 1983 - : Integrating VGI contributions for gully mapping using Kalman filter and machine learning
• K10plus-PPN: 1880834146

Abstract

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.