当前地球观测领域的大规模多模态模型（LMMs）主要在平面光学任务上进行评估，往往忽略垂直维度；然而，垂直几何结构在灾害响应与城市形态分析等应用中至关重要。高程感知推理的研究进展亦受限于系统性评测手段的缺失：现有基准极少在像素级、目标级和场景级推理任务中同步提供光学影像与高程产品。为填补这一空白，我们提出 GeoHeight-Bench——一个面向高程感知遥感理解的大规模基准，并进一步构建更具挑战性的地形导向扩展版本 GeoHeight-Bench+。该基准通过可扩展的视觉语言模型（VLM）驱动生成流程构建，融合元数据提取与提示工程，并采用人工参与的验证协议评估其质量。为探究是否能仅从光学影像中学习高程感知推理能力，我们还提供了 GeoHeightChat——一种高程感知基线方法，将隐式高程相关几何表征迁移至光学 LMM 中。对一系列闭源与开源 LMM 的评估表明，当前模型在高程信息推理方面仍存在明显局限，而对齐隐式高程先验则可显著提升多数依赖高程的任务性能。然而，若干任务（尤其是坡度推理与基于地形的洪涝易感性制图）仍未得到有效解决，凸显了高程感知地理人工智能（GeoAI）领域亟待突破的具体开放问题。数据集与代码将于 \href{https://teriri1999.github.io/GeoHeight/}{此处} 发布。

Current Large Multimodal Models (LMMs) in Earth observation are predominantly evaluated on planar optical tasks and often neglect the vertical dimension, although vertical geometric structure can be critical in applications such as disaster response and urban-morphology analysis. Progress on height-aware reasoning is also hindered by the absence of systematic evaluation: few benchmarks pair optical imagery with height products across pixel-, object-, and scene-level reasoning. To address this gap, we introduce GeoHeight-Bench, a large-scale benchmark for height-aware remote sensing understanding, together with a more challenging terrain-oriented extension, GeoHeight-Bench+. The benchmark is constructed through a scalable, VLM-driven generation pipeline that combines metadata extraction with prompt engineering, and its quality is assessed through a human-in-the-loop verification protocol. To examine whether height-aware reasoning can be learned from optical imagery, we further provide GeoHeightChat, a height-aware baseline that transfers implicit height-related geometric representations into an optical LMM. Evaluations of a broad range of closed- and open-source LMMs show that current models remain limited in their ability to reason about height information, while aligning implicit height priors improves most height-dependent tasks. However, several tasks, particularly slope reasoning and terrain-based flood-susceptibility mapping, remain largely unsolved, highlighting concrete open problems for height-aware GeoAI. Dataset and Code will be released \href{https://teriri1999.github.io/GeoHeight/}{here}.