发表机构
Institute of Theoretical and Applied Informatics, Polish Academy of Sciences; Space Research Centre, Polish Academy of Sciences（波兰科学院理论与应用信息学研究所; 波兰科学院空间研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究不同区域类型中哨兵2影像建筑检测问题，利用多时间数据集及卷积分割主干，经特定场景微调与跨时间推理，量化多种影响因素，为哨兵2建筑分类提供不同采集期和定居特征下的实用指导。
AI 中文摘要
哨兵2影像具有开放获取、全球覆盖和频繁重访等优势，适合大规模实用建筑制图。但其10米分辨率使得建筑与非建筑分类具有挑战性，尤其是对于小或亚像素建筑，且性能会随季节和建成环境的异质性而变化。本文介绍了一个哨兵2建筑物检测框架，旨在系统量化这些影响并支持更形式化、面向实践的模型选择。通过构建华沙地区的多时间哨兵2数据集，利用官方波兰地形数据库（BDOT10k）的建筑足迹生成二进制地面真值掩码。使用两个既定的卷积分割主干进行特定场景微调，然后进行跨时间推理，评估不同因素的影响，最后基于结果为不同采集期和定居特征下的常规哨兵2建筑分类提供实用指导。
英文摘要
Sentinel-2 imagery offers open access, global coverage, and frequent revisit times, making it attractive for practical building mapping at scale; however, its native 10m resolution makes building vs non-building classification challenging, particularly for small or sub-pixel buildings, and performance can vary with both seasonality and the heterogeneity of built-up environments. This paper introduces a Sentinel-2 building-detection framework designed to systematically quantify these effects and to support more formalised, practice-oriented model selection. We construct a dedicated multi-temporal Sentinel-2 dataset over the Warsaw region and derive binary ground-truth masks by rasterising official Polish topographic database (BDOT10k) building footprints onto the Sentinel-2 pixel grid. Using two established convolutional segmentation backbones (U-Net and DeepLabV3+), we first perform scene-specific fine-tuning to select a robust architecture and identify the best monthly models for L1C and L2A products separately. We then conduct cross-temporal inference by applying each best monthly model to all scenes, enabling an assessment of (i) which months provide favourable training and inference conditions, (ii) how performance transfers between seasons, (iii) the impact of processing level, and (iv) how these effects differ across built-up typologies. Based on these results, we provide practical guidance for routine Sentinel-2 building classification under varying acquisition periods and settlement characteristics.
