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Spatial process-based transfer learning for prediction problems

Daisuke Murakami et al.

Journal of Geographical Systems2025https://doi.org/10.1007/s10109-024-00455-yarticle
AJG 1
Weight
0.48

What the paper says

Although spatial prediction is a versatile tool for urban and environmental monitoring, the predictive accuracy is often unsatisfactory when limited samples are available from the study area. The present study was conducted to improve the accuracy in such cases through transfer learning, which uses larger datasets from external areas. Specifically, we proposed the SpTrans method, which pre-trains map patterns for each area using spatial process models. These patterns are then used in transfer learning to distinguish between unique patterns in the study area and common patterns across areas. The performance of the proposed SpTrans method was examined using land price prediction, with empirical results suggesting that the model achieves higher prediction accuracy than conventional learning, which does not explicitly consider local spatial dependence.

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https://doi.org/https://doi.org/10.1007/s10109-024-00455-y

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@article{daisuke2025,
  title        = {{Spatial process-based transfer learning for prediction problems}},
  author       = {Daisuke Murakami et al.},
  journal      = {Journal of Geographical Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1007/s10109-024-00455-y},
}

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Evidence weight

0.48

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.41 × 0.4 = 0.16
M · momentum0.63 × 0.15 = 0.09
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.