  Automatic tree density estimation and counting using single aerial and
satellite images is a challenging task in photogrammetry and remote sensing,
yet has an important role in forest management. In this paper, we propose the
first semisupervised transformer-based framework for tree counting which
reduces the expensive tree annotations for remote sensing images. Our method,
termed as TreeFormer, first develops a pyramid tree representation module based
on transformer blocks to extract multi-scale features during the encoding
stage. Contextual attention-based feature fusion and tree density regressor
modules are further designed to utilize the robust features from the encoder to
estimate tree density maps in the decoder. Moreover, we propose a pyramid
learning strategy that includes local tree density consistency and local tree
count ranking losses to utilize unlabeled images into the training process.
Finally, the tree counter token is introduced to regulate the network by
computing the global tree counts for both labeled and unlabeled images. Our
model was evaluated on two benchmark tree counting datasets, Jiangsu, and
Yosemite, as well as a new dataset, KCL-London, created by ourselves. Our
TreeFormer outperforms the state of the art semi-supervised methods under the
same setting and exceeds the fully-supervised methods using the same number of
labeled images. The codes and datasets are available at
https://github.com/HAAClassic/TreeFormer.
