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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.
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]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: &lt;searchLink fieldCode=&quot;JN&quot; term=&quot;%22Remote+Sensing%22&quot;&gt;Remote Sensing&lt;/searchLink&gt;. Sep2026, Vol. 18 Issue 18, p3136. 33p.
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  Data: 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 &#215; 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 &lt; 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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  Data: &lt;i&gt;Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder&#39;s express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.&lt;/i&gt; (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18183136
    Languages:
      – Code: eng
        Text: English
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        PageCount: 33
        StartPage: 3136
    Subjects:
      – SubjectFull: Radar interferometry
        Type: general
      – SubjectFull: Reliability in engineering
        Type: general
      – SubjectFull: Geographic spatial analysis
        Type: general
      – SubjectFull: Similarity (Psychology)
        Type: general
      – SubjectFull: Transfer of training
        Type: general
      – SubjectFull: Knowledge transfer
        Type: general
      – SubjectFull: East of England
        Type: general
    Titles:
      – TitleFull: Evidence-Based Reliability Assessment of Spatial Transfer Learning for Satellite-Derived Ground Deformation Monitoring.
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            NameFull: Tshireletso, Thalosang
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            – D: 15
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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            – TitleFull: Remote Sensing
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