A practically feasible transfer learning method for deep-temporal urban change monitoring

Abstract

Neural networks have shown their potential to monitor urban changes with deep-temporal remote sensing data, which simultaneously considers a large number of observations within a given window. However, training these networks with supervision is a challenge due to the low availability of third-party sources with sufficient spatio-temporal resolution to label each window individually. To remedy this problem, we developed a novel approach utilizing transfer learning (TL) on a set of deep-temporal windows. We demonstrate that labelling of multiple windows simultaneously can be practically viable, even with a low amount of high spatial resolution third-party data. The overall process provides a trade-off between labour resources and the ability to train a network on existing systems, despite its intensive memory requirements. As a demonstration, an existing previously trained (pre-trained) network was used to transfer knowledge to a new target location. We demonstrate our method with combined Sentinel 1 and 2 observations for the area of Liège (Belgium) for the time period spanning 2017–2020. This is underpinned by our use of common metrics in machine learning and remote sensing, and in our discussion of selected examples. Three independent transfers of the same pre-trained model and their combination were carried out, all of which showed an improvement in terms of these metrics.