A Systematic Review of Deep Learning Approaches for Building Extraction and Height Estimation Using Optical Remote Sensing

作者：D. Susetyo, Tolga Bakırman · 发表于：Photogrammetric Record · 年份：2026 · DOI：10.1111/phor.70063

Accurate and up‐to‐date 3D building information is increasingly essential for urban planning, disaster management, and geospatial analytics. At the same time, rapid advances in deep learning (DL) have enabled the automated extraction of building‐related information from remote sensing imagery. The reviewed literature indicates that most studies focus on 2D building footprint extraction, whereas the incorporation of building height information is comparatively less addressed. In this context, this review synthesises DL‐based methods for building height estimation from optical satellite and aerial imagery. The reviewed studies are classified according to their analytical objectives, modelling strategies, and input data sources. We analyse research trends and commonly used datasets, followed by an in‐depth discussion of five major pipelines for building height estimation. The paper concludes with recommended evaluation and minimum reporting standards, key challenges, and practical guidance for future work.