Detecting Urban Changes with Recurrent Neural Networks from Multitemporal Sentinel-2 Data
This work addresses urban monitoring for environmental and planning applications, presenting an incremental improvement in change detection accuracy.
The paper tackles urban change detection using a deep learning framework combining fully convolutional networks and recurrent networks (LSTMs) on multitemporal Sentinel-2 data, achieving over 95% overall accuracy and a 1.5% boost in F1 rate for the change class with LSTMs and additional temporal information.
\begin{abstract} The advent of multitemporal high resolution data, like the Copernicus Sentinel-2, has enhanced significantly the potential of monitoring the earth's surface and environmental dynamics. In this paper, we present a novel deep learning framework for urban change detection which combines state-of-the-art fully convolutional networks (similar to U-Net) for feature representation and powerful recurrent networks (such as LSTMs) for temporal modeling. We report our results on the recently publicly available bi-temporal Onera Satellite Change Detection (OSCD) Sentinel-2 dataset, enhancing the temporal information with additional images of the same region on different dates. Moreover, we evaluate the performance of the recurrent networks as well as the use of the additional dates on the unseen test-set using an ensemble cross-validation strategy. All the developed models during the validation phase have scored an overall accuracy of more than 95%, while the use of LSTMs and further temporal information, boost the F1 rate of the change class by an additional 1.5%.