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

Deep few-shot learning for bi-temporal building change detection