  Estimating accurate number of interested objects from a given image is a
challenging yet important task. Significant efforts have been made to address
this problem and achieve great progress, yet counting number of ground objects
from remote sensing images is barely studied. In this paper, we are interested
in counting dense objects from remote sensing images. Compared with object
counting in natural scene, this task is challenging in following factors: large
scale variation, complex cluttered background and orientation arbitrariness.
More importantly, the scarcity of data severely limits the development of
research in this field. To address these issues, we first construct a
large-scale object counting dataset based on remote sensing images, which
contains four kinds of objects: buildings, crowded ships in harbor,
large-vehicles and small-vehicles in parking lot. We then benchmark the dataset
by designing a novel neural network which can generate density map of an input
image. The proposed network consists of three parts namely convolution block
attention module (CBAM), scale pyramid module (SPM) and deformable convolution
module (DCM). Experiments on the proposed dataset and comparisons with state of
the art methods demonstrate the challenging of the proposed dataset, and
superiority and effectiveness of our method.
