  Understanding and quantifying ecosystem services are crucial for sustainable
environmental management, conservation efforts, and policy-making. The
advancement of remote sensing technology and machine learning techniques has
greatly facilitated this process. Yet, ground truth labels, such as
biodiversity, are very difficult and expensive to measure. In addition, more
easily obtainable proxy labels, such as land use, often fail to capture the
complex heterogeneity of the ecosystem. In this paper, we demonstrate how land
use proxy labels can be implemented with a soft, multi-label classifier to
predict ecosystem services with complex heterogeneity.
