A large-scale VHR parcel dataset and a novel hierarchical semantic boundary-guided network for agricultural parcel delineation

Abstract

Current agricultural parcels (AP) extraction faces two main limitations: (1) existing AP delineation methods fail to fully utilize low-level information (e.g., parcel boundary information), leading to unsatisfactory performance under certain circumstances; (2) the lack of large-scale, high-resolution AP benchmark datasets in China hinders comprehensive model evaluation and improvement. To address the first limitation, we develop a hierarchical semantic boundary-guided network (HBGNet) to fully leverage boundary semantics, thereby improving AP delineation. It integrates two branches, a core branch of AP feature extraction and an auxiliary branch related to boundary feature mining. Specifically, the boundary extract branch employes a module based on Laplace convolution operator to enhance the model’s awareness of parcel boundary. For AP feature extraction, a local–global context aggregation module is designed to enhance the semantic representation of AP, improving the adaptability across different AP scenarios. Meanwhile, a boundary-guided module is developed to enhance boundary details of high-level AP semantic information. Ultimately, a multi-grained feature fusion module is designed to enhance the capacity of HBGNet to extract APs with various sizes and shapes. Regarding the second limitation, we construct the first large-scale very high-resolution (VHR) agricultural parcel dataset (FHAPD) across seven different areas, covering more than 10,000 km², using data from GaoFen-1 (2-meter) and GaoFen-2 (1-meter). Detailed experiments are conducted on the FHAPD, a publicly European dataset (i.e., Al4boundaries), and medium-resolution Sentinel-2 images from the Netherlands and HBGNet is compared with other eight AP delineation methods. Results show that HBGNet outperforms the other eight methods in attribute and geometry accuracy. The Intersection over Union (IOU), F1-score of the boundary (F_bdy), and global total-classification (GTC) exceed other methods by 0.61 %-7.52 %, 0.8 %-36.3 %, and 1.7 %-31.8 %, respectively. It also effectively transfers to unseen regions. We conclude that the proposed HBGNet is an effective, advanced, and transferable method for diverse agricultural scenarios and remote sensing images.