  Deep learning has shown promising performance in submeter-level mapping
tasks; however, the annotation cost of submeter-level imagery remains a
challenge, especially when applied on a large scale. In this paper, we present
the first submeter-level land cover mapping of Japan with eight classes, at a
relatively low annotation cost. We introduce a human-in-the-loop deep learning
framework leveraging OpenEarthMap, a recently introduced benchmark dataset for
global submeter-level land cover mapping, with a U-Net model that achieves
national-scale mapping with a small amount of additional labeled data. By
adding a small amount of labeled data of areas or regions where a U-Net model
trained on OpenEarthMap clearly failed and retraining the model, an overall
accuracy of 80\% was achieved, which is a nearly 16 percentage point
improvement after retraining. Using aerial imagery provided by the Geospatial
Information Authority of Japan, we create land cover classification maps of
eight classes for the entire country of Japan. Our framework, with its low
annotation cost and high-accuracy mapping results, demonstrates the potential
to contribute to the automatic updating of national-scale land cover mapping
using submeter-level optical remote sensing data. The mapping results will be
made publicly available.
