Urban trees species classification using Sentinel-2 and Planetscope satellite image time series

Urban trees play an important role in the city’s thermal comfort, carbon storage capacity, rainwater infiltration, capture of some pollutants or to enhance biodiversity. The classification of tree species in urban areas is essential for monitoring their health and distribution, particularly in the context of increasingly frequent heatwaves and global warming. In this study, we present a deep learning-based approach to classify the 20 most representative urban tree species in Strasbourg using Satellite Image Time Series (SITS) from Sentinel-2 and PlanetScope. We implement and compare three state-of-the-art models used for Time Series Classification (TSC) — InceptionTime, H-InceptionTime, and LITE — augmented with sensor fusion (features fusion) to exploit the complementary spectral and temporal information from both sensors. Our results show that the sensor fusion approach significantly improves classification accuracy, with the best scores for H-InceptionTime model with an accuracy of 69%. Also, the error can be explained by the specific spatial organisation of the trees in urban areas, where dense and heterogenous tree distribution inside parks is more prone to errors than aligned ones.