Transfer Learning in Earth Observation Data Analysis: A review
Artur Nowakowski, Maria Pia Del Rosso, Paulina Zachar, Dario Spiller, Grzegorz Gabara, Domenico Barretta, Kamila Barbara Kalinowska, Kamil Choromański, Artur Wilkowski, Alessandro Sebastianelli, Przemysław Kupidura, Katarzyna Osińska-Skotak, Silvia Liberata Ullo
Abstract
The significant increase in the amount of satellite data in recent years along with the increase in computing resources has opened up new possibilities for Earth observation (EO) data analysis. However, the main obstacle to obtaining high-quality products is the limited amount of labeled data. Among the methods that can help solve this type of problem, transfer learning (TL) has gained enormous interest in recent years. It involves using a model trained on one problem to solve another problem. This article reviews TL methods as applied to EO data analyses. It provides a structured overview of methods in relation to applications, presents publication statistics, and summarizes the advantages and disadvantages noted for previous research.