LC4-DViT: Land-cover Creation for Land-cover Classification with Deformable Vision Transformer

• URL: http://arxiv.org/abs/2511.22812v1
• Date: Thu, 27 Nov 2025 23:56:35 GMT
• Title: LC4-DViT: Land-cover Creation for Land-cover Classification with Deformable Vision Transformer
• Authors: Kai Wang, Siyi Chen, Weicong Pang, Chenchen Zhang, Renjun Gao, Ziru Chen, Cheng Li, Dasa Gu, Rui Huang, Alexis Kai Hon Lau,
• Abstract summary: LC4-DViT is a framework that combines generative data creation with a deformation-aware Vision Transformer.<n>A text-guided diffusion pipeline uses GPT-4o-generated scene descriptions to synthesize high-fidelity training images.<n>DViT couples a DCNv4 deformable convolutional backbone with a Vision Transformer encoder to jointly capture fine-scale geometry and global context.
• Score: 14.684808109822386
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Land-cover underpins ecosystem services, hydrologic regulation, disaster-risk reduction, and evidence-based land planning; timely, accurate land-cover maps are therefore critical for environmental stewardship. Remote sensing-based land-cover classification offers a scalable route to such maps but is hindered by scarce and imbalanced annotations and by geometric distortions in high-resolution scenes. We propose LC4-DViT (Land-cover Creation for Land-cover Classification with Deformable Vision Transformer), a framework that combines generative data creation with a deformation-aware Vision Transformer. A text-guided diffusion pipeline uses GPT-4o-generated scene descriptions and super-resolved exemplars to synthesize class-balanced, high-fidelity training images, while DViT couples a DCNv4 deformable convolutional backbone with a Vision Transformer encoder to jointly capture fine-scale geometry and global context. On eight classes from the Aerial Image Dataset (AID)-Beach, Bridge, Desert, Forest, Mountain, Pond, Port, and River-DViT achieves 0.9572 overall accuracy, 0.9576 macro F1-score, and 0.9510 Cohen' s Kappa, improving over a vanilla ViT baseline (0.9274 OA, 0.9300 macro F1, 0.9169 Kappa) and outperforming ResNet50, MobileNetV2, and FlashInternImage. Cross-dataset experiments on a three-class SIRI-WHU subset (Harbor, Pond, River) yield 0.9333 overall accuracy, 0.9316 macro F1, and 0.8989 Kappa, indicating good transferability. An LLM-based judge using GPT-4o to score Grad-CAM heatmaps further shows that DViT' s attention aligns best with hydrologically meaningful structures. These results suggest that description-driven generative augmentation combined with deformation-aware transformers is a promising approach for high-resolution land-cover mapping.

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