Interactive Dual-Branch Transformer for Precise Agricultural Parcel Delineation from Remote Sensing Imagery

Abstract

Highlights: What are the main findings? A novel model (IDBT) that integrates semantic and boundary information for precise agricultural parcel delineation. IDBT achieves state-of-the-art accuracy and boundary precision on two large-scale datasets. What is the implication of the main finding? Provides a practical, high-resolution mapping tool for vegetation analysis and agricultural monitoring. Robust transferability across diverse agricultural landscapes and unseen regions. Accurate agricultural parcel (AP) delineation from remote sensing imagery is critical for precision agriculture and effective land management. However, parcel boundaries are typically ambiguous, irregular, and challenging to segment precisely. Existing methods often struggle to adequately integrate high-level semantic information with fine-grained boundary details, leading to incomplete or blurred parcel outlines. To address these limitations, we introduce the Interactive Dual-Branch Transformer (IDBT), a novel segmentation framework designed to significantly enhance boundary localization. Our approach incorporates three key innovations: (1) a dual-branch architecture enabling bidirectional interactive information flow between semantic segmentation and boundary detection branches; (2) attention-enhanced Transformer backbones that facilitate optimized multi-scale feature fusion; and (3) a tailored supervision strategy that combines boundary-aware losses with point-wise refinement to sharpen ambiguous parcel edges. Extensive evaluations conducted on two public datasets demonstrate that IDBT outperforms current state-of-the-art methods, achieving high segmentation accuracy and boundary precision. Additionally, our approach exhibits robust transferability in cross-area experiments. Code will be made publicly available at the repository listed in the Data Availability Statement.