Active-Learning Approaches for Landslide Mapping Using Support Vector Machines
Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-07-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/13/2588 |
| _version_ | 1827688182301327360 |
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| author | Zhihao Wang Alexander Brenning |
| author_facet | Zhihao Wang Alexander Brenning |
| author_sort | Zhihao Wang |
| collection | DOAJ |
| description | Ex post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling. |
| first_indexed | 2024-03-10T09:50:16Z |
| format | Article |
| id | doaj.art-dc3bb8b5b0b34578ad98dbcc0b0592d1 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T09:50:16Z |
| publishDate | 2021-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-dc3bb8b5b0b34578ad98dbcc0b0592d12023-11-22T02:49:27ZengMDPI AGRemote Sensing2072-42922021-07-011313258810.3390/rs13132588Active-Learning Approaches for Landslide Mapping Using Support Vector MachinesZhihao Wang0Alexander Brenning1Department of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, GermanyDepartment of Geography, Friedrich Schiller University Jena, Loebdergraben 32, 07743 Jena, GermanyEx post landslide mapping for emergency response and ex ante landslide susceptibility modelling for hazard mitigation are two important application scenarios that require the development of accurate, yet cost-effective spatial landslide models. However, the manual labelling of instances for training machine learning models is time-consuming given the data requirements of flexible data-driven algorithms and the small percentage of area covered by landslides. Active learning aims to reduce labelling costs by selecting more informative instances. In this study, two common active-learning strategies, uncertainty sampling and query by committee, are combined with the support vector machine (SVM), a state-of-the-art machine-learning technique, in a landslide mapping case study in order to assess their possible benefits compared to simple random sampling of training locations. By selecting more “informative” instances, the SVMs with active learning based on uncertainty sampling outperformed both random sampling and query-by-committee strategies when considering mean AUROC (area under the receiver operating characteristic curve) as performance measure. Uncertainty sampling also produced more stable performances with a smaller AUROC standard deviation across repetitions. In conclusion, under limited data conditions, uncertainty sampling reduces the amount of expert time needed by selecting more informative instances for SVM training. We therefore recommend incorporating active learning with uncertainty sampling into interactive landslide modelling workflows, especially in emergency response settings, but also in landslide susceptibility modelling.https://www.mdpi.com/2072-4292/13/13/2588active learninglandslide modellingsupport vector machinemachine learning |
| spellingShingle | Zhihao Wang Alexander Brenning Active-Learning Approaches for Landslide Mapping Using Support Vector Machines Remote Sensing active learning landslide modelling support vector machine machine learning |
| title | Active-Learning Approaches for Landslide Mapping Using Support Vector Machines |
| title_full | Active-Learning Approaches for Landslide Mapping Using Support Vector Machines |
| title_fullStr | Active-Learning Approaches for Landslide Mapping Using Support Vector Machines |
| title_full_unstemmed | Active-Learning Approaches for Landslide Mapping Using Support Vector Machines |
| title_short | Active-Learning Approaches for Landslide Mapping Using Support Vector Machines |
| title_sort | active learning approaches for landslide mapping using support vector machines |
| topic | active learning landslide modelling support vector machine machine learning |
| url | https://www.mdpi.com/2072-4292/13/13/2588 |
| work_keys_str_mv | AT zhihaowang activelearningapproachesforlandslidemappingusingsupportvectormachines AT alexanderbrenning activelearningapproachesforlandslidemappingusingsupportvectormachines |