Abstract

Accurate land-use polygon mapping for forest-oriented natural resource monitoring requires both image-based class prediction and the reconciliation of heterogeneous geospatial semantics. In operational mapping, land survey, forestry survey, and natural resource monitoring datasets often differ in classification systems, management objectives, boundary rules, and mapping scales, causing semantic conflicts when overlaid or forced into one-to-one categories. To address this issue, this study proposes a topology-aware land-use polygon-mapping framework that integrates multi-source semantic representation learning, semantically guided relation learning, and topology-constrained polygon optimization. In the experiments, remote sensing imagery provides visual evidence, the Third National Land Survey (TNLS) and forestry survey (FS) datasets are encoded as source-specific auxiliary semantic priors, and the natural resource integrated monitoring (NRIM) data serve only as reference labels for training and evaluation. Rather than resolving cross-source conflicts using predefined rules, the framework learns a unified land-use representation, transforms semantic boundary cues into a vertex-edge topology graph, and reconstructs GIS-compatible polygons through topology-constrained polygon optimization. In a representative forest–agricultural landscape, the method achieved an mIoU of 79.86%, an APLS of 56.82%, and a TOPO-F1 of 54.56%. These results suggest that learnable semantic harmonization and topology-aware polygon generation can improve the semantic consistency and vector reliability of land-use products for forest and natural resource monitoring.

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