# RSGPNet: Geometric Prompting for Remote Sensing Open-Vocabulary Semantic Segmentation

arXiv: https://arxiv.org/abs/2606.28410 · HTML: https://mlsanity.com/paper/2606.28410/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 32 of its 58 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 30 of 32)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [SAM 3: Segment Anything with Concepts](https://mlsanity.com/paper/2511.16719/) (2025)
- [Extract Free Dense Labels from CLIP](https://mlsanity.com/paper/2112.01071/) (2021)
- [Open-Vocabulary Semantic Segmentation with Mask-adapted CLIP](https://mlsanity.com/paper/2210.04150/) (2022)
- [Side Adapter Network for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2302.12242/) (2023)
- [LoveDA: A Remote Sensing Land-Cover Dataset for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2110.08733/) (2021)
- [A Simple Framework for Open-Vocabulary Segmentation and Detection](https://mlsanity.com/paper/2303.08131/) (2023)
- [MaskCLIP: Masked Self-Distillation Advances Contrastive Language-Image Pretraining](https://mlsanity.com/paper/2208.12262/) (2022)
- [RSPrompter: Learning to Prompt for Remote Sensing Instance Segmentation based on Visual Foundation Model](https://mlsanity.com/paper/2306.16269/) (2023)
- [SCLIP: Rethinking Self-Attention for Dense Vision-Language Inference](https://mlsanity.com/paper/2312.01597/) (2023)
- [SegCLIP: Patch Aggregation with Learnable Centers for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2211.14813/) (2022)
- [ClearCLIP: Decomposing CLIP Representations for Dense Vision-Language Inference](https://mlsanity.com/paper/2407.12442/) (2024)
- [Grounding Everything: Emerging Localization Properties in Vision-Language Transformers](https://mlsanity.com/paper/2312.00878/) (2023)
- [Pay Attention to Your Neighbours: Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2404.08181/) (2024)
- [CLIP as RNN: Segment Countless Visual Concepts without Training Endeavor](https://mlsanity.com/paper/2312.07661/) (2023)
- [Explore the Potential of CLIP for Training-Free Open Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2407.08268/) (2024)
- [RemoteSAM: Towards Segment Anything for Earth Observation](https://mlsanity.com/paper/2505.18022/) (2025)
- [Exploring Efficient Open-Vocabulary Segmentation in the Remote Sensing](https://mlsanity.com/paper/2509.12040/) (2025)
- [SegEarth-OV: Towards Training-Free Open-Vocabulary Segmentation for Remote Sensing Images](https://mlsanity.com/paper/2410.01768/) (2024)
- [Towards Open-Vocabulary Remote Sensing Image Semantic Segmentation](https://mlsanity.com/paper/2412.19492/) (2024)
- [Effective SAM Combination for Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2411.14723/) (2024)
- [Semi-supervised Semantic Segmentation for Remote Sensing Images via Multi-scale Uncertainty Consistency and Cross-Teacher-Student Attention](https://mlsanity.com/paper/2501.10736/) (2025)
- [Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos](https://mlsanity.com/paper/2507.22052/) (2025)
- [Exploring Simple Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2401.12217/) (2024)
- [Towards Open-Vocabulary Video Semantic Segmentation](https://mlsanity.com/paper/2412.09329/) (2024)
- [Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation](https://mlsanity.com/paper/2501.07806/) (2025)
- [Open-Vocabulary Domain Generalization in Urban-Scene Segmentation](https://mlsanity.com/paper/2602.18853/) (2026)
- [Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection](https://mlsanity.com/paper/2604.11231/) (2026)
- [InfoCLIP: Bridging Vision-Language Pretraining and Open-Vocabulary Semantic Segmentation via Information-Theoretic Alignment Transfer](https://mlsanity.com/paper/2511.15967/) (2025)
- [Rethinking the Global Knowledge of CLIP in Training-Free Open-Vocabulary Semantic Segmentation](https://mlsanity.com/paper/2502.06818/) (2025)

## Abstract (arXiv)

Open-vocabulary semantic segmentation (OVSS) enables text-guided segmentation of unseen objects, breaking fixed-class limitations to achieve open-world understanding. However, existing OVSS methods primarily focus on modifying the CLIP attention mechanism, which still suffers from unstable local segmentation for remote sensing (RS) domain. To address these limitations, we propose RSGPNet, a training-free geometric prompting framework for RS OVSS that refines segmentation by leveraging object geometric areas and consistency constraints. Specifically, RSGPNet comprises three core modules: a Text-guided Coarse Mask module (TCM), a Geometric Re-prompting Module (GRP), and a Coarse-to-fine Consistency Verification Mechanism (CVM). TCM utilizes text prompts and the input image to construct initial coarse segmentation masks. GRP then converts these coarse masks into geometric box prompts, feeding them back into the segmentation model to generate refined masks. Finally, CVM employs consistency computation to prevent prompting from reinforcing erroneous regions. They allow the model to improve segmentation accuracy in complex areas, such as category boundaries. Extensive experiments on RS datasets demonstrate that RSGPNet significantly outperforms state-of-the-art methods across both quantitative and qualitative metrics while exhibiting excellent interpretability. The code is released at hrefhttps://github.com/wangshanwen001/RSGPNethttps://github.com/wangshanwen001/RSGPNet.