**5. Conclusions**

For an accurate building outline extraction, we design a nearest feature selector (NFS) module to dynamically re-align predictions and slightly misaligned annotations. The proposed module can be easily combined with existing loss functions to manage subpixel or pixel-to-level misalignments of the manually created annotations more effectively. For all loss functions, the addition of the proposed NFS yielded significantly better performances in all the evaluation metrics. For the classic L1 loss, the increments gained by using the additional NFS are 8.8%, 8.9%, and 9.8% for the f1-score, kappa coefficient, and Jaccard index, respectively. We plan to improve the similarity selection mechanism and apply it to other data sources to achieve better generalization capacity for large-scale applications.

**Author Contributions:** Conceptualization, G.W.; Investigation, Y.W.; Project administration, G.W.; Resources, R.S.; Validation, Y.G. and Y.H.; Writing—original draft, Y.W.; Writing—review & editing, G.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** Part of this work was supported by the JST (Japan Science and Technology Agency) aXis Grant Number JPMJAS2019.

**Acknowledgments:** We gratefully acknowledge SAKURA Internet Inc. for the provision of the *koukaryoku* GPU server for our experiments.

**Conflicts of Interest:** The authors declare no conflict of interest.
