Deep Learning for Instance Segmentation of Agricultural Fields - Master thesis

Abstract
This thesis aims to delineate agricultural field parcels from satellite images via deep learning instance segmentation. Manual delineation is accurate but time consuming, and many automated approaches with traditional image segmentation techniques struggle to capture the variety of possible field appearances. Deep learning has proven to be successful in various computer vision tasks, and might be a good candidate to enable accurate, performant and generalizable delineation of agricultural fields. Here, a fully convolutional instance segmentation architecture (adapted from Li et al., 2016), was trained on Sentinel-2 image data and corresponding agricultural field polygons from Denmark. In contrast to many other approaches, the model operates on raw RGB images without significant pre- and post-processing. After training, the model proved successful in predicting field boundaries on held-out image chips. The results generalize across different field sizes, shapes and other properties, but show characteristic problems in some cases. In a second experiment, the model was trained to simultaneously predict the crop type of the field instance. Performance in this setting was significantly worse. Many fields were correctly delineated, but the wrong crop class was predicted. Overall, the results are promising and prove the validity of the deep learning approach. Also, the methodology offers many directions for future improvement.