  In real-world applications (e.g., change detection), annotating images is
very expensive. To build effective deep learning models in these applications,
deep few-shot learning methods have been developed and prove to be a robust
approach in small training data. The analysis of building change detection from
high spatial resolution remote sensing observations is important research in
photogrammetry, computer vision, and remote sensing nowadays, which can be
widely used in a variety of real-world applications, such as map updating. As
manual high resolution image interpretation is expensive and time-consuming,
building change detection methods are of high interest. The interest in
developing building change detection approaches from optical remote sensing
images is rapidly increasing due to larger coverages, and lower costs of
optical images. In this study, we focus on building change detection analysis
on a small set of building change from different regions that sit in several
cities. In this paper, a new deep few-shot learning method is proposed for
building change detection using Monte Carlo dropout and remote sensing
observations. The setup is based on a small dataset, including bitemporal
optical images labeled for building change detection.
