Uncertainty-Aware Label-Efficient Landslide Segmentation in Open-Pit Mines via Transformer Transfer Learning and Active Learning.
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| Title: | Uncertainty-Aware Label-Efficient Landslide Segmentation in Open-Pit Mines via Transformer Transfer Learning and Active Learning. |
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| Authors: | Li, Haiying1 (AUTHOR) lihaiying@wust.edu.cn, Hu, Xin1,2 (AUTHOR), Ren, Fengyu2,3 (AUTHOR), Lan, Zhou1,3 (AUTHOR), Cai, Sheng1,2 (AUTHOR) |
| Source: | Remote Sensing. Jun2026, Vol. 18 Issue 11, p1774. 35p. |
| Subjects: | Active learning, Epistemic uncertainty, Landslide hazard analysis, Remote sensing, Environmental monitoring, Strip mining |
| Abstract: | Highlights: What is the main finding? A label efficient Probabilistic SegFormer framework is developed for landslide segmentation in active open pit mines under severe source to target domain shift and limited local annotation. What is the implication of the main finding? The proposed uncertainty guided active learning strategy prioritizes ambiguous mine scenes for expert annotation, supporting trustworthy geohazard monitoring with a small fraction of local labels. Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What is the main finding? A label efficient Probabilistic SegFormer framework is developed for landslide segmentation in active open pit mines under severe source to target domain shift and limited local annotation. What is the implication of the main finding? The proposed uncertainty guided active learning strategy prioritizes ambiguous mine scenes for expert annotation, supporting trustworthy geohazard monitoring with a small fraction of local labels. Reliable landslide mapping in active mining regions is constrained by two coupled issues: severe domain shift from public datasets and extremely limited local annotations. In line with Transformer-centric intelligent interpretation of complex remote-sensing scenes, this study proposes a label-efficient transfer segmentation framework from a public source corpus to target open-pit mines built on SegFormer with a lightweight hybrid adapter that couples global context modeling with mining-specific directional cues. The pipeline combines source-domain Transformer pre-training, class-conditional feature alignment, Bayesian uncertainty estimation, and human-guided active learning. First, the backbone is pre-trained on the GDCLD source domain to learn transferable landslide morphology priors. Second, a joint optimization stage with class-conditional alignment reduces source and target embedding discrepancy during adaptation. Third, Monte Carlo dropout is enabled at inference to estimate predictive distributions, and sample acquisition is driven by mutual-information-based querying to prioritize epistemically informative target patches, addressing the small-sample supervision challenge emphasized in remote-sensing deep learning. This design turns uncertainty into an operational annotation policy rather than a passive diagnostic output. Experimental results show that the framework consistently outperforms deterministic counterparts and strong active-learning baselines in spectrally complex mine scenes, while approaching the fully supervised upper bound with only a small fraction of local labels. The approach is especially effective in shadowed benches and fault-adjacent slopes, supporting trustworthy deployment for geohazard monitoring and disaster-relevant slope safety workflows; extension to multi-modal constraints (e.g., SAR or elevation) is discussed as future work. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18111774 |
