建筑年龄是城市基础设施安全、运营和规划的基础，直接影响结构安全和可持续发展。现有方法大多依赖街景、功能或材料等详细建筑信息，而这些数据比卫星影像和数字表面模型（DSM）等地理空间数据更难获取。然而，基于这些地理空间数据的现有方法主要依赖光学影像和局部纹理特征，限制了其捕捉结构信息和区分不同时期建筑的能力。此外，建筑年龄类别之间固有的时序关系常被忽视。为解决这些局限性，我们提出了首个利用广泛可用的多源地理空间数据（包括遥感数据及其衍生产品以及建筑足迹信息）进行多建筑年龄估计的深度学习框架，该统一流程在像素级和实例级同时估计年龄。具体而言，采用带有微调适配器的大型预训练骨干网络提取代表性特征，并通过栅格衍生和属性衍生融合模块支持像素级和实例级预测。像素级估计采用带有余弦引导混合专家（CMoE）的金字塔解码器，跨尺度融合多源栅格数据，以捕捉细粒度的建筑年龄差异；而实例级预测则使用注意力池化和属性条件自适应实例归一化（PCAdaIN）来提取实例级表示并整合属性特征。时序序数约束损失（TOCL）强制预测年龄的顺序排列。我们还构建了香港建筑年龄基准数据集以实现全面评估。源代码和数据集可在 https://github.com/littlebeen/RS-buildingAge.git 获取。

Building age is fundamental for urban infrastructure safety, operation, and planning, as it directly affects structural safety, and sustainable development. Existing methods mostly rely on detailed building information such as street view, function, or materials, which are harder to obtain than geospatial data like satellite imagery and Digital Surface Models (DSMs). However, existing methods based on those geospatial data mainly rely on optical imagery and local texture features, limiting their ability to capture structural information and distinguish buildings from different periods. Moreover, the inherent temporal ordering among building age categories is often overlooked. To address these limitations, we propose the first deep-learning framework for multi-building age estimation using widely available multi-source geospatial data, including remote sensing data and their derived products, as well as building footprint information that estimates age at both pixel and instance levels within a unified pipeline. Specifically, a large pretrained backbone with fine-tuning adapters extracts representative features, and raster-derived and property-derived fusion modules support pixel-wise and instance-wise predictions. Pixel-level estimation employs a pyramid decoder with Cosine-guided Mixture-of-Experts (CMoE) to fuse multi-source raster data across scales, capturing fine-grained building age differences, while instance-level prediction uses attention pooling and Property-Conditional Adaptive Instance Normalization (PCAdaIN) to extract instance-level representations and integrate property attributes. A Temporal Ordinal Constraint Loss (TOCL) enforces the sequential ordering of predicted ages. We also construct a building age benchmark for Hong Kong to enable comprehensive evaluation. The source code and datasets are available at https://github.com/littlebeen/RS-buildingAge.git.