Automated Delineation of the Agricultural Fields using Multi-Task Deep Learning and Optical Satellite Imagery

Abstract

Agricultural field boundary information is an essential input for precision agriculture. This paper proposes a Multi-scale Multi-task Boundary Detection Deep Learning (DL) Network (MMBDNet) based on spatial attention mechanisms to delineate agricultural fields using high-resolution optical satellite imagery. The designed DL architecture simultaneously learns three tasks - a major task for field prediction and two auxiliary tasks for boundary prediction and distance estimation. We experimented with the agricultural landscape of Île-de-France, France, using the cloud-free time-series images from PlanetScope satellite that capture key phenological stages of crops. The segmentation results from different months are combined and post-processed using hierarchical watershed segmentation to extract field instances. We compared the MMBDNet with the baseline single-task U-Net and multitask BsiNet models at pixel- and object-level. Our results show that the MMBDNet has the highest pixel-level (above 85%) and object-level (above 70%) accuracy compared to U-Net and BsiNet.