Cross-Resolution Fusion of AlphaEarth Foundations Embeddings and GF-1 Imagery via Semantic Anchor-Graph Enhancement for Litchi Orchard Extraction

Abstract

Litchi is an important cash crop in China. Accurate litchi orchard extraction is crucial for agricultural management and economic development. However, litchi orchards are spectrally very similar to evergreen broad-leaved forests and are poorly resolved in medium- and low-resolution satellite imagery, so large-scale extraction remains challenging. A cross-resolution fusion network via semantic anchor-graph enhancement (SAGE-Net) is therefore proposed, using a dual-branch encoder–decoder to aggregate complementary features of AlphaEarth Foundations (AEF) embeddings and GF-1 imagery. To enable semantic interaction across resolutions, the SAGE module is designed to gather the AEF embeddings into a few semantic anchors, reason over the anchor graph, and scatter them back under GF-1 guidance, so that semantic content and spatial extent are supplied by the source better suited to each. To refine the coarse prediction to full resolution without discarding what is already reliable, the reliability-adaptive correction (RAC) head is designed to gate a detail-guided correction by the confidence and local consistency of that prediction, concentrating refinement where it is uncertain. In Shenzhen, SAGE-Net attains the highest IoU (0.5660) and F1-score (0.7229) among eight comparison methods, with a Precision of 0.7162 and a Recall of 0.7297. Under stricter protocols that separate the training and the evaluation patches by a 256 m buffer or by whole spatial blocks, the IoU falls to 0.5224 and 0.5000 and SAGE-Net remains the most accurate of the networks retested, by a smaller margin. The results demonstrate the value of role-aware enhancement and reliability-adaptive correction for fine-grained orchard mapping, and confirm AEF embeddings as an effective supplementary data source for large-scale mapping.