  The process of estimating and counting tree density using only a single
aerial or satellite image is a difficult task in the fields of photogrammetry
and remote sensing. However, it plays a crucial role in the management of
forests. The huge variety of trees in varied topography severely hinders tree
counting models to perform well. The purpose of this paper is to propose a
framework that is learnt from the source domain with sufficient labeled trees
and is adapted to the target domain with only a limited number of labeled
trees. Our method, termed as AdaTreeFormer, contains one shared encoder with a
hierarchical feature extraction scheme to extract robust features from the
source and target domains. It also consists of three subnets: two for
extracting self-domain attention maps from source and target domains
respectively and one for extracting cross-domain attention maps. For the
latter, an attention-to-adapt mechanism is introduced to distill relevant
information from different domains while generating tree density maps; a
hierarchical cross-domain feature alignment scheme is proposed that
progressively aligns the features from the source and target domains. We also
adopt adversarial learning into the framework to further reduce the gap between
source and target domains. Our AdaTreeFormer is evaluated on six designed
domain adaptation tasks using three tree counting datasets, \ie Jiangsu,
Yosemite, and London. Experimental results show that AdaTreeFormer
significantly surpasses the state of the art, \eg in the cross domain from the
Yosemite to Jiangsu dataset, it achieves a reduction of 15.9 points in terms of
the absolute counting errors and an increase of 10.8\% in the accuracy of the
detected trees' locations. The codes and datasets are available at
https://github.com/HAAClassic/AdaTreeFormer.
