Evidence-Based Reliability Assessment of Spatial Transfer Learning for Satellite-Derived Ground Deformation Monitoring.
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| Title: | Evidence-Based Reliability Assessment of Spatial Transfer Learning for Satellite-Derived Ground Deformation Monitoring. |
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| Authors: | Tshireletso, Thalosang1 (AUTHOR), Bagheri, Meghdad1 (AUTHOR) m.bagheri@uel.ac.uk, Ghorashi, Seyed Ali1 (AUTHOR) |
| Source: | Remote Sensing. Sep2026, Vol. 18 Issue 18, p3136. 33p. |
| Subjects: | Radar interferometry, Reliability in engineering, Geographic spatial analysis, Similarity (Psychology), Transfer of training, Knowledge transfer |
| Geographic Terms: | East of England |
| Abstract: | Highlights: What are the main findings? A transferability-aware framework was developed to predict satellite-derived ground deformation by combining local observations with knowledge transferred from environmentally similar monitoring tasks. An evidence-based reliability framework accompanies every prediction with independent evidence reliability, transfer reliability, and validation-calibrated expected prediction error, extending transfer learning beyond prediction accuracy. What are the implications of the main findings? Within the investigated study area, environmental similarity emerged as a stronger indicator of successful spatial knowledge transfer than structural similarity, supporting more reliable source-task selection. Deployment-oriented reliability profiles enable infrastructure managers to prioritise monitoring and maintenance by jointly considering deformation hazard, confidence in available evidence, and expected prediction uncertainty. Satellite-derived ground deformation monitoring has become an essential tool for infrastructure management; however, the spatial heterogeneity of Interferometric Synthetic Aperture Radar (InSAR) observations limits reliable assessment in regions with sparse measurement coverage. While spatial transfer learning offers a practical means of extending deformation predictions beyond well-observed areas, existing approaches primarily emphasise predictive accuracy and provide little indication of whether transferred predictions can be trusted during operational deployment. This study presents an evidence-based reliability assessment framework for spatial transfer learning using European Ground Motion Service (EGMS) observations. The framework accompanies every prediction with complementary evidence reliability, transfer reliability, and validation-calibrated expected prediction error. The methodology was evaluated within a single 100 km × 100 km EGMS tile in Eastern England, characterised by gradual, subsidence-type ground motion, using 815 EGMS observations and deployed to assess deformation risk for 140 road corridors. The proposed local–transfer fusion achieved a mean RMSE of 1.720 mm yr−1, outperforming multi-source transfer strategies. Feature–space divergence exhibited a significant positive relationship with transfer prediction error ( ρ = 0.515 , p < 0.001 ), demonstrating that environmental similarity is a stronger indicator of transfer success than structural similarity within the investigated study area. The proposed framework extends conventional transfer learning beyond prediction accuracy and provides a practical foundation for risk-informed infrastructure monitoring using satellite-derived ground deformation observations. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A transferability-aware framework was developed to predict satellite-derived ground deformation by combining local observations with knowledge transferred from environmentally similar monitoring tasks. An evidence-based reliability framework accompanies every prediction with independent evidence reliability, transfer reliability, and validation-calibrated expected prediction error, extending transfer learning beyond prediction accuracy. What are the implications of the main findings? Within the investigated study area, environmental similarity emerged as a stronger indicator of successful spatial knowledge transfer than structural similarity, supporting more reliable source-task selection. Deployment-oriented reliability profiles enable infrastructure managers to prioritise monitoring and maintenance by jointly considering deformation hazard, confidence in available evidence, and expected prediction uncertainty. Satellite-derived ground deformation monitoring has become an essential tool for infrastructure management; however, the spatial heterogeneity of Interferometric Synthetic Aperture Radar (InSAR) observations limits reliable assessment in regions with sparse measurement coverage. While spatial transfer learning offers a practical means of extending deformation predictions beyond well-observed areas, existing approaches primarily emphasise predictive accuracy and provide little indication of whether transferred predictions can be trusted during operational deployment. This study presents an evidence-based reliability assessment framework for spatial transfer learning using European Ground Motion Service (EGMS) observations. The framework accompanies every prediction with complementary evidence reliability, transfer reliability, and validation-calibrated expected prediction error. The methodology was evaluated within a single 100 km × 100 km EGMS tile in Eastern England, characterised by gradual, subsidence-type ground motion, using 815 EGMS observations and deployed to assess deformation risk for 140 road corridors. The proposed local–transfer fusion achieved a mean RMSE of 1.720 mm yr−1, outperforming multi-source transfer strategies. Feature–space divergence exhibited a significant positive relationship with transfer prediction error ( ρ = 0.515 , p < 0.001 ), demonstrating that environmental similarity is a stronger indicator of transfer success than structural similarity within the investigated study area. The proposed framework extends conventional transfer learning beyond prediction accuracy and provides a practical foundation for risk-informed infrastructure monitoring using satellite-derived ground deformation observations. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18183136 |
