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

arXiv: https://arxiv.org/abs/2108.11262 · HTML: https://mlsanity.com/paper/2108.11262/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 41 references that are in the map.
- First posted on arXiv 25 Aug 2021.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics](https://mlsanity.com/paper/1705.07115/) (2017)
- [Aleatoric and Epistemic Uncertainty in Machine Learning: An Introduction to Concepts and Methods](https://mlsanity.com/paper/1910.09457/) (2019)
- [PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment](https://mlsanity.com/paper/1908.06391/) (2019)
- [Fully Convolutional Siamese Networks for Change Detection](https://mlsanity.com/paper/1810.08462/) (2018)
- [Efficient Uncertainty Estimation for Semantic Segmentation in Videos](https://mlsanity.com/paper/1807.11037/) (2018)
- [From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning Techniques](https://mlsanity.com/paper/2003.06583/) (2020)
- [On uncertainty estimation in active learning for image segmentation](https://mlsanity.com/paper/2007.06364/) (2020)
- [SAMA-VTOL: A new unmanned aircraft system for remotely sensed data collection](https://mlsanity.com/paper/2011.11007/) (2020)
- [Robust building footprint extraction from big multi-sensor data using deep competition network](https://mlsanity.com/paper/2011.02879/) (2020)

## Abstract (arXiv)

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.