Open-vocabulary semantic segmentation (OVSS) of remote sensing faces severe performance degradation when encountering unseen scene distributions caused by geographic, sensor, and resolution variations. Existing vision–language approaches provide strong semantic priors but lack scene-invariant structural representations required for dense prediction. In this work, we focus on open-vocabulary scene generalization semantic segmentation (OVSGSS), achieving dense inference guided by joint modeling of semantic alignment and structural consistency. To this end, we construct USGMS-100K, a large-scale multisensor dataset for self-supervised pretraining, and develop a structure-aware remote sensing image encoder (RSIE) via masked reconstruction to learn scene-invariant representations. Building upon this encoder, we propose a semantic–structural collaborative framework (namely RS-OVSGSeg) that integrates language-derived semantic priors with structural priors through a semantic–structural cost map enhancement (SSCME) module and a dual-prior guided decoder (DPGD). Extensive cross-scene evaluations on five public datasets demonstrate that proposed method achieves state-of-the-art performance in open-vocabulary cross-scene segmentation, while maintaining a favorable balance between accuracy and computational efficiency. The results highlight the importance of explicitly modeling structural invariance for robust open-vocabulary scene generalization in remote sensing. The USGMS-100K dataset, RSIE weight and code are publicly available at https://github.com/HuangWBill/RS-OVSGSeg.
Wu-Biao Huang, Hu-Chen Li, Shuai Zhang et al.· IEEE Transactions on Geoscie...· 0 citations
Abstract. Zero-shot semantic segmentation (ZSSS) is a crucial task in remote sensing image understanding, yet existing methods still suffer from limited generalization to unseen classes. To address this issue, we propose a Knowledge Graph (KG) enhanced ZSSS framework, which introduces explicit hierarchical and relational information into class embeddings to achieve more structured and semantically consistent representations. Specifically, a KG class encoder is designed, consisting of the class enhanced query (CEQ) and class enhanced embedding (CEE) modules, which extract class-relevant subgraphs from a self-constructing Remote Sensing Semantic Class Knowledge Graph (RSSCKG) and generate knowledge-enriched embeddings through a text encoder. Experiments on three public remote sensing datasets demonstrate that the proposed method consistently improves performance across seven state-of-the-art ZSSS frameworks. The integration of KG-based embeddings yields significant gains in the evaluation metrics, with particularly strong improvements on unseen classes, while maintaining accuracy on seen classes. Compared with enhancement strategies based on large language model (LLM) generated descriptions, the proposed KG class encoder exhibit superior semantic separability and stability. These results validate the effectiveness, generalization, and scalability of the proposed framework for ZSSS in remote sensing imagery.
Wu-da Huang, Huchen Li, Shuai Zhang et al.· ISPRS Annals of the Photogra...· 0 citations
Multimodal fusion unlocks the potential of point cloud semantic segmentation, thereby driving advancements in surface observation and visual perception tasks. Although light detection and ranging (LiDAR) systems capture precise 3D structural geometry and optical images provide rich semantic and textural information, existing fusion methods struggle with limited cross-modal perception and insufficient information complementarity. To address these limitations, we propose a multi-stage LiDAR-image collaborative perception fusion network (MCPFNet) for point cloud semantic segmentation of urban scenes. At the middle fusion stage, the network incorporates an elevation-guided geometric-aware fusion module and a semantic-aware cross-attention fusion module to enable bidirectional feature injection between LiDAR and image modalities. In the late fusion stage, a bidirectional adaptive fusion module further refines semantic representations through gated weighting and bidirectional cross-attention mechanisms. Extensive experiments on three multimodal datasets with different resolutions, i.e., ISPRS Vaihingen, N3C-California, and UAVScenes, demonstrate that MCPFNet outperforms existing fusion methods, achieving mIoUs of 74.51%, 95.15%, and 62.76%, respectively. Hence, our multi-stage fusion and bidirectional interaction strategy is more reliable and accurate than existing methods in performing segmentation across diverse and complex urban scenes.
Huchen Li, Wu-da Huang, Xiangda Lei et al.· Remote Sensing· 1 citation
Experiments show that CoMVS-GS remains competitive on object-level reconstruction and improves geometric accuracy and mesh compactness in outdoor scenes while maintaining high rendering quality.
Shihan Chen, Junjing Zhang, Q. Yan et al.· 0 citations
Abstract. The proliferation of continuous Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) has shifted the paradigm of 3D aerial reconstruction from relying solely on geometric stereo matching to inverse rendering optimization. However, while these emerging rendering-based frameworks excel in synthesizing photo-realistic novel views, their capability to extract accurate surfaces in complex aerial scenarios remains ambiguous compared to traditional methods. To establish a clearer understanding, this study presents a comprehensive evaluation of five representative frameworks spanning traditional Structure from Motion (SfM), purely Signed Distance Field (SDF) representations, unstructured 3D Gaussians, hybrid voxel-Gaussians, and strictly explicit sparse voxels. By systematically standardizing identical computational environments, inputs, and unified mesh-extraction pipelines on both real-world airborne LiDAR datasets and synthetic cityscapes, we assess their performance regarding visual fidelity, geometric accuracy, and resource efficiency. The experimental results reveal that while traditional MVS produces the highest overall geometric precision by strictly enforcing multi-view rigid geometry, it is prone to failures in texture-less regions. Among rendering-based representations, a fundamental trade-off exists: highly flexible, unstructured 3DGS achieve highest visual scores but degrade the underlying geometric surfaces; conversely, explicitly structured techniques demonstrate distinct superiority in regularizing topological coherence and floating artifact suppression. Furthermore, we observe that integrating structured voxels avoids the severe memory bottlenecks associated with extracting geometries from chaotic unorganized primitives. These empirical findings emphasize that for large-scale aerial photogrammetry, integrating explicit spatial structuralization into differentiable rendering pipelines is imperative for achieving scalable operations and bridging the geometric accuracy gap with traditional methods.
Shihan Chen, Zhaojin Li, Q. Yan et al.· The International Archives o...· 0 citations
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