More Labels or Better Labels? A Semantic Segmentation Study Case Using Historical Aerial Images for Tree Delineation

Forest monitoring using remotely sensed data is a central task in forestry and ecosystem studies. The long-term assessment of woody vegetation assists, for instance, in change detection and handling environmental hazards. Recently, France has made available its historical aerial images archive, covering the whole country extent. The public availability of such datasets expands the scope of long-term monitoring studies, such as forest monitoring. The main aim of this study is to investigate how the volume and the quality of the reference data influence the semantic segmentation of woody vegetation using historical grayscale aerial images in southwestern France. The two main contributions of this study are the provision of a rare 1942 binary dataset (woody vegetation vs. no woody vegetation) and the investigation of the effects of training data quality and quantity using a deep learning architecture.