用多智能体系统融合数据，精准识别城市建筑年龄分布，助力低碳能源规划。
A Multi-Agent System for Building-Age Cohort Mapping to Support Urban Energy Planning
- 构建三智能体系统融合普查、地图和文物数据，自动清洗整合建筑信息。
- 卫星图像分类模型准确率达90.69%，但跨代际混淆仍存，需人工复核低信度预测。
- 输出带置信度的建筑年龄图谱，适合城市热力规划与低碳能源部署决策者使用。
确定城市建筑存量的年龄分布对可持续城市供热规划和升级优先级至关重要。然而现有方法多依赖传感器或遥感数据，存在数据不一致与缺失问题。本文提出一个包含三个核心智能体（Zensus、OSM、Monument）的多智能体大语言模型系统，融合异构数据源。数据协调器与调和器对建筑痕迹进行地理编码与去重。基于融合后的真值数据，提出BuildingAgeCNN，一种仅依赖卫星影像的分类器，采用ConvNeXt主干网络，结合特征金字塔网络（FPN）、CoordConv空间通道与挤压-激励（SE）模块。在空间交叉验证下，总体准确率达90.69%，但宏平均F1仅为67.25%，反映类别不平衡及相邻历史世代间的持续混淆。为降低规划风险，地址至预测管道引入校准置信度估计，并标记低置信度案例供人工审核。该多智能体系统不仅辅助结构化数据采集，还帮助能源需求规划者优化区域供热网络，推动低碳可持续能源系统建设。
原文摘要 · Abstract (English)
Determining the age distribution of the urban building stock is crucial for sustainable municipal heat planning and upgrade prioritization. However, existing approaches often rely on datasets gathered via sensors or remote sensing techniques, leaving inconsistencies and gaps in data. We present a multi-agent LLM system comprising three key agents, the Zensus agent, the OSM agent, and the Monument agent, that fuse data from heterogeneous sources. A data orchestrator and harmonizer geocodes and deduplicates building imprints. Using this fused ground truth, we introduce BuildingAgeCNN, a satellite-only classifier based on a ConvNeXt backbone augmented with a Feature Pyramid Network (FPN), CoordConv spatial channels, and Squeeze-and-Excitation (SE) blocks. Under spatial cross validation, BuildingAgeCNN attains an overall accuracy of 90.69% but a modest macro-F1 of 67.25%, reflecting strong class imbalance and persistent confusions between adjacent historical cohorts. To mitigate risk for planning applications, the address-to prediction pipeline includes calibrated confidence estimates and flags low-confidence cases for manual review. This multi-agent LLM system not only assists in gathering structured data but also helps energy demand planners optimize district-heating networks and target low-carbon sustainable energy systems.
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