As hubs of human activity, urban surfaces consist of a wealth of semantic entities. Segmenting these various entities from satellite imagery is crucial for a range of downstream applications. Current advanced segmentation models can reliably segment entities defined by physical attributes (e.g., buildings, water bodies) but still struggle with socially defined categories (e.g., schools, parks). In this work, we achieve socio-semantic segmentation by vision-language model reasoning. To facilitate this, we introduce the Urban Socio-Semantic Segmentation dataset named SocioSeg, a new resource comprising satellite imagery, digital maps, and pixel-level labels of social semantic entities organized in a hierarchical structure. Additionally, we propose a novel vision-language reasoning framework called SocioReasoner that simulates the human process of identifying and annotating social semantic entities via cross-modal recognition and multi-stage reasoning. We employ reinforcement learning to optimize this non-differentiable process and elicit the reasoning capabilities of the vision-language model. Experiments demonstrate our approach's gains over state-of-the-art models and strong zero-shot generalization. Our dataset and code are available in https://github.com/AMAP-ML/SocioReasoner.

翻译：作为人类活动的枢纽，城市地表蕴含着丰富的语义实体。从卫星影像中分割这些多样化的实体对于一系列下游应用至关重要。当前先进的语义分割模型能够可靠地分割由物理属性定义的实体（例如建筑物、水体），但在处理由社会属性定义的类别（例如学校、公园）时仍面临困难。在本研究中，我们通过视觉语言模型的推理实现了社会语义分割。为此，我们引入了名为SocioSeg的城市社会语义分割数据集，这是一个包含卫星影像、数字地图以及按层级结构组织的社会语义实体像素级标注的新资源。此外，我们提出了一种新颖的视觉语言推理框架SocioReasoner，该框架通过跨模态识别与多阶段推理来模拟人类识别与标注社会语义实体的过程。我们采用强化学习来优化这一不可微分的流程，并激发视觉语言模型的推理能力。实验结果表明，我们的方法相较于现有最先进模型具有显著优势，并展现出强大的零样本泛化能力。我们的数据集与代码已公开于https://github.com/AMAP-ML/SocioReasoner。