RemoteZero：零人工标注的地理空间推理系统

摘要

地理空间推理要求模型将复杂的空间语义与用户意图解析为精确的对地观测目标位置。近期研究进展已使推理路径摆脱了人工干预，允许模型自主生成推断链条。然而最终依赖依然存在：它们仍受限于人工标注的真实坐标监督。这使得推理过程实现自主化，但其空间终点仍未独立，阻碍了模型在丰富无标注遥感数据上实现真正的自我进化。为突破此瓶颈，我们提出无需边界框监督的地理空间推理框架RemoteZero。该框架的构建基于一个简单的不对称性：多模态大语言模型在验证某区域是否满足查询要求方面，通常优于直接生成精确坐标的能力。通过利用这种更强的判别能力，RemoteZero以内在语义验证取代几何监督，实现了无需边界框标注的GRPO训练。该框架进一步支持迭代式自我进化，使模型能够通过自身验证信号从无标注遥感影像中持续提升。实验表明，RemoteZero在定位任务上达到了与强监督方法相竞争的性能，印证了自验证训练模式在地理空间推理定位领域的潜力。

English

Geospatial reasoning requires models to resolve complex spatial semantics and user intent into precise target locations for Earth observation. Recent progress has liberated the reasoning path from manual curation, allowing models to generate their own inference chains. Yet a final dependency remains: they are still supervised by human-annotated ground-truth coordinates. This leaves the reasoning process autonomous, but not its spatial endpoint, and prevents true self-evolution on abundant unlabeled remote sensing data. To break this bottleneck, we introduce RemoteZero, a box-supervision-free framework for geospatial reasoning. RemoteZero is motivated by a simple asymmetry: an MLLM is typically better at verifying whether a region satisfies a query than at directly generating precise coordinates. Leveraging this stronger discriminative ability, RemoteZero replaces geometric supervision with intrinsic semantic verification and enables GRPO training without box annotations. The resulting framework further supports iterative self-evolution, allowing the model to improve from unlabeled remote sensing imagery through its own verification signal. Experiments show that RemoteZero achieves competitive performance against strong supervised methods, demonstrating the potential of self-verifying training for geospatial reasoning localization.

RemoteZero：零人工标注的地理空间推理系统

RemoteZero: Geospatial Reasoning with Zero Human Annotations

摘要

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