Agricultural parcel and boundary delineation (APBD) remains challenging in fragmented agricultural landscapes, where small, densely adjacent fields increase merge and split errors even when overlap-based scores appear acceptable. This study addresses the problem as a zero-shot, cross-source, and structure-aware segmentation task for operational use without site-specific retraining or annotated training data. We combine vegetation-enhancement (VE) preprocessing with foundation-model-based instance segmentation and evaluate the pipeline across Sentinel-2 (S2, 10 m), PlanetScope (PS, 3 m), and Sentinel-2 Deep Resolution 3.0 (S2DR3, 1 m). Evaluation uses a structure-aware protocol that complements standard detection metrics with explicit merge and split quantification, enabling assessment of geographic information system (GIS)-ready parcel outputs beyond overlap alone. On a manually annotated benchmark of 424 parcels in Trentino (Italy), the best configuration (Vegetation-Enhancement + Segment Anything Model (VE+SAM) with S2DR3) achieves F1 0.79 and recall 0.78, with the lowest Global Under-Segmentation Error (GUSE 0.21). At source level, moving from S2 to S2DR3 nearly triples recall and halves GUSE, confirming that input resolution is a dominant factor in highly fragmented small-parcel settings. External validation on three Dutch tiles (300 km²), where parcels are larger and fragmentation is lower, shows a weaker resolution effect: S2 is no longer fundamentally inadequate and gains from finer imagery are smaller. In this setting, VE+SAM still maintains a consistent structural advantage over the domain-trained baseline, with systematically lower split and merge error rates, indicating that structural gains from the proposed pipeline persist when resolution is less limiting.
Zero-shot parcel instance delineation in fragmented agricultural landscapes: Cross-source evaluation and structure-aware validation
De Mello E Castro Arnaud Calisto, João Pedro;Vecchio, Massimo;Pincheira, Miguel;Antonelli, Fabio
2026-01-01
Abstract
Agricultural parcel and boundary delineation (APBD) remains challenging in fragmented agricultural landscapes, where small, densely adjacent fields increase merge and split errors even when overlap-based scores appear acceptable. This study addresses the problem as a zero-shot, cross-source, and structure-aware segmentation task for operational use without site-specific retraining or annotated training data. We combine vegetation-enhancement (VE) preprocessing with foundation-model-based instance segmentation and evaluate the pipeline across Sentinel-2 (S2, 10 m), PlanetScope (PS, 3 m), and Sentinel-2 Deep Resolution 3.0 (S2DR3, 1 m). Evaluation uses a structure-aware protocol that complements standard detection metrics with explicit merge and split quantification, enabling assessment of geographic information system (GIS)-ready parcel outputs beyond overlap alone. On a manually annotated benchmark of 424 parcels in Trentino (Italy), the best configuration (Vegetation-Enhancement + Segment Anything Model (VE+SAM) with S2DR3) achieves F1 0.79 and recall 0.78, with the lowest Global Under-Segmentation Error (GUSE 0.21). At source level, moving from S2 to S2DR3 nearly triples recall and halves GUSE, confirming that input resolution is a dominant factor in highly fragmented small-parcel settings. External validation on three Dutch tiles (300 km²), where parcels are larger and fragmentation is lower, shows a weaker resolution effect: S2 is no longer fundamentally inadequate and gains from finer imagery are smaller. In this setting, VE+SAM still maintains a consistent structural advantage over the domain-trained baseline, with systematically lower split and merge error rates, indicating that structural gains from the proposed pipeline persist when resolution is less limiting.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
