RBP-MTL: Agricultural Parcel Vectorization via Region-Boundary-Parcel Decoupled Multitask Learning

Agricultural parcel vectorization is important for precision agriculture when analyzing crops at the parcel scale. However, it is a challenging task to vectorize parcels from satellite imagery, where under-segmentation and the discontinuous boundaries of parcels are common problems, due to the varied shapes and sizes of parcels caused by the terrain and the farming mode, and the blurred boundaries caused by the limited resolution and the shadows. In this paper, a region-boundary-parcel decoupled multi-task learning (RBP-MTL) framework is proposed for agricultural parcel vectorization, where the local spatial constraints between the regions, boundaries, and the objects of each parcel are jointly modeled via multi-task learning, to promote the object separability of agricultural parcels and the boundary connectivity. In addition, simple and efficient boundary-object interaction vectorization is introduced to further ensure that each parcel is independent with a complete and separable boundary. In the experiments, the proposed framework was verified using three datasets with different spatial resolutions, i.e., the public parcel extraction dataset from the iFLYTEK Challenge 2021 (~1 m), in addition to a single-temporal Gaofen-1 dataset from Hubei province in China (2 m) and a multi-temporal Sentinel-2 dataset from the Netherlands (10 m), which were annotated and built by the authors. The proposed RBP-MTL framework achieved a state-of-the-art accuracy, with effective extraction of complete parcel boundaries in the different scenes.