  Geospatial object segmentation, as a particular semantic segmentation task,
always faces with larger-scale variation, larger intra-class variance of
background, and foreground-background imbalance in the high spatial resolution
(HSR) remote sensing imagery. However, general semantic segmentation methods
mainly focus on scale variation in the natural scene, with inadequate
consideration of the other two problems that usually happen in the large area
earth observation scene. In this paper, we argue that the problems lie on the
lack of foreground modeling and propose a foreground-aware relation network
(FarSeg) from the perspectives of relation-based and optimization-based
foreground modeling, to alleviate the above two problems. From perspective of
relation, FarSeg enhances the discrimination of foreground features via
foreground-correlated contexts associated by learning foreground-scene
relation. Meanwhile, from perspective of optimization, a foreground-aware
optimization is proposed to focus on foreground examples and hard examples of
background during training for a balanced optimization. The experimental
results obtained using a large scale dataset suggest that the proposed method
is superior to the state-of-the-art general semantic segmentation methods and
achieves a better trade-off between speed and accuracy. Code has been made
available at: \url{https://github.com/Z-Zheng/FarSeg}.
