A Weakly Supervised Multimodal Deep Learning Approach for Large-Scale Tree Classification: A Case Study in Cyprus - Arslan Amin; Andreas Kamilaris; Savvas Karatsiolis (Remote Sensing)
Forest ecosystems play an essential role in ecological balance, supporting biodiversity and climate change mitigation. These ecosystems are crucial not only for ecological stability but also for the local economy. Performing a tree census at a country scale via traditional methods is resource-demanding, error-prone, and requires significant effort by a large number of experts. While emerging technologies such as satellite imagery and AI provide the means for achieving promising results in this task with less effort, considerable effort is still required by experts to annotate hundreds or thousands of images. This study introduces a novel methodology for a tree census classification system which leverages historical and partially labeled data, employs probabilistic data imputation and a weakly supervised training technique, and thus achieves state-of-the-art precision in classifying the dominant tree species of Cyprus. A crucial component of our methodology is a ResNet50 model which takes as input high spatial resolution satellite imagery in the visible band and near-infrared band, as well as topographical features. By applying a multimodal training approach, a classification accuracy of 90% among nine targeted tree species is achieved.