# RSGround-R1: Rethinking Remote Sensing Visual Grounding through Spatial Reasoning

arXiv: https://arxiv.org/abs/2601.21634 · HTML: https://mlsanity.com/paper/2601.21634/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 27 of its 61 references that are in the map.
- First posted on arXiv 29 Jan 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 27 of 27)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://mlsanity.com/paper/2402.03300/) (2024)
- [DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://mlsanity.com/paper/2501.12948/) (2025)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling](https://mlsanity.com/paper/2412.05271/) (2024)
- [Vision-R1: Incentivizing Reasoning Capability in Multimodal Large Language Models](https://mlsanity.com/paper/2503.06749/) (2025)
- [MiniGPT-v2: large language model as a unified interface for vision-language multi-task learning](https://mlsanity.com/paper/2310.09478/) (2023)
- [VLM-R1: A Stable and Generalizable R1-style Large Vision-Language Model](https://mlsanity.com/paper/2504.07615/) (2025)
- [R1-Onevision: Advancing Generalized Multimodal Reasoning through Cross-Modal Formalization](https://mlsanity.com/paper/2503.10615/) (2025)
- [Cosmos-Reason1: From Physical Common Sense To Embodied Reasoning](https://mlsanity.com/paper/2503.15558/) (2025)
- [SkySenseGPT: A Fine-Grained Instruction Tuning Dataset and Model for Remote Sensing Vision-Language Understanding](https://mlsanity.com/paper/2406.10100/) (2024)
- [UniVG-R1: Reasoning Guided Universal Visual Grounding with Reinforcement Learning](https://mlsanity.com/paper/2505.14231/) (2025)
- [V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization](https://mlsanity.com/paper/2411.02712/) (2024)
- [RRSIS: Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2306.08625/) (2023)
- [GeoPixel: Pixel Grounding Large Multimodal Model in Remote Sensing](https://mlsanity.com/paper/2501.13925/) (2025)
- [SegEarth-R1: Geospatial Pixel Reasoning via Large Language Model](https://mlsanity.com/paper/2504.09644/) (2025)
- [CounterCurate: Enhancing Physical and Semantic Visio-Linguistic Compositional Reasoning via Counterfactual Examples](https://mlsanity.com/paper/2402.13254/) (2024)
- [NuScenes-SpatialQA: A Spatial Understanding and Reasoning Benchmark for Vision-Language Models in Autonomous Driving](https://mlsanity.com/paper/2504.03164/) (2025)
- [RemoteReasoner: Towards Unifying Geospatial Reasoning Workflow](https://mlsanity.com/paper/2507.19280/) (2025)
- [Geo-R1: Improving Few-Shot Geospatial Referring Expression Understanding with Reinforcement Fine-Tuning](https://mlsanity.com/paper/2509.21976/) (2025)
- [Towards Faithful Reasoning in Remote Sensing: A Perceptually-Grounded GeoSpatial Chain-of-Thought for Vision-Language Models](https://mlsanity.com/paper/2509.22221/) (2025)
- [GeoVLM-R1: Reinforcement Fine-Tuning for Improved Remote Sensing Reasoning](https://mlsanity.com/paper/2509.25026/) (2025)
- [Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models](https://mlsanity.com/paper/2503.00743/) (2025)
- [SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation](https://mlsanity.com/paper/2507.12857/) (2025)
- [TinyRS-R1: Compact Multimodal Language Model for Remote Sensing](https://mlsanity.com/paper/2505.12099/) (2025)
- [GRASP: Geospatial pixel Reasoning viA Structured Policy learning](https://mlsanity.com/paper/2508.17102/) (2025)

## Built on by (most cited first, 8 of 8)

- [RemoteZero: Geospatial Reasoning with Zero Labels](https://mlsanity.com/paper/2605.04451/) (2026)
- [SCA: Spatial Credit Assignment for Reinforcement Learning of GUI Agents](https://mlsanity.com/paper/2609.36939/) (2026)
- [GeoSearcher: Anchor-Guided Progressive Reasoning for Remote Sensing Visual Grounding with Process Supervision](https://mlsanity.com/paper/2607.01050/) (2026)
- [Vector Map as Language: Toward Unified Remote Sensing Vector Mapping](https://mlsanity.com/paper/2606.10701/) (2026)
- [GeoSelect: Spatial-Program Execution for Training-Free Referring Remote Sensing Image Segmentation](https://mlsanity.com/paper/2607.03869/) (2026)
- [FUSAR-R1: A Large-Scale Reasoning Model for Intelligent Interpretation of SAR Images](https://mlsanity.com/paper/2607.16819/) (2026)
- [GrabVG: Graph-Attentive Binding for Visual Grounding in UAV Imagery](https://mlsanity.com/paper/2608.18996/) (2026)
- [CoEvolve: Construct-to-Edit Visual Grounding with Bidirectional State Refinement](https://mlsanity.com/paper/2610.01710/) (2026)

## Abstract (arXiv)

Remote Sensing Visual Grounding (RSVG) aims to localize target objects in large-scale aerial imagery based on natural language descriptions. Owing to the vast spatial scale and high semantic ambiguity of remote sensing scenes, these descriptions often rely heavily on positional cues, posing unique challenges for Multimodal Large Language Models (MLLMs) in spatial reasoning. To leverage this unique feature, we propose a reasoning-guided, position-aware post-training framework, dubbed RSGround-R1, to progressively enhance spatial understanding. Specifically, we first introduce Chain-of-Thought Supervised Fine-Tuning (CoT-SFT) using synthetically generated RSVG reasoning data to establish explicit position awareness. Reinforcement Fine-Tuning (RFT) is then applied, augmented by our newly designed positional reward that provides continuous and distance-aware guidance toward accurate localization. Moreover, to mitigate incoherent localization behaviors across rollouts, we introduce a spatial consistency guided optimization scheme that dynamically adjusts policy updates based on their spatial coherence, ensuring stable and robust convergence. Extensive experiments on RSVG benchmarks demonstrate superior performance and generalization of our model.