Work

HeightCLIP: A vision-language network for height estimation from monocular remote sensing imagery

Year: 2026

Type: article

Abstract: Accurate height estimation from monocular remote sensing imagery is a fundamental task with broad applications in urban planning and management. However, existing vision-based methods rely solely on visual features, which limits their ability to fully exploit height-related semantic information. Vision-language models, e.g., CLIP, offer a promising alternative by leveraging multimodal information, yet directly adapting them to height regression remains challenging due to their weak perception of continuous numerical values. To overcome this limitation, we propose HeightCLIP , a novel vision-language framework tailored for height estimation from monocular remote sensing imagery. The task-adaptive textual prompts and image-side representations are optimized under height supervision to derive pixel-text similarity features for accurate height estimation. Specifically, we design an interval-based prompting strategy that integrates learnable context tokens and a fixed natural language template with parameterized interval values, allowing CLIP to better comprehend height-related semantics. In addition, we incorporate a reciprocal cross-attention fusion mechanism to facilitate bidirectional interaction between visual and textual features, thereby improving cross-modal feature integration. Finally, building upon this enriched feature space, we impose cross-modal consistency constraints to ensure pixel-level correspondence between height descriptions and visual features, providing more informative representations for subsequent height prediction. Extensive experiments on three benchmark datasets i.e., Vaihingen, Potsdam, and Data Fusion Contest 2019 (DFC2019), demonstrate that HeightCLIP achieves competitive or superior performance compared with existing state-of-the-art methods, and comprehensive ablation studies confirm the effectiveness of each proposed component. The source code will be made available at https://github.com/songtaowhu/HeightCLIP .

Source: International Journal of Applied Earth Observation and Geoinformation

DOI: 10.1016/j.jag.2026.105499

Authors: Tao Zhang, Furong Shi, Yuanping Zhu, Xin Huang

Institutions: Tianjin Normal University, Tianjin University of Technology, Wuhan University

Language: English

Topic: Automated Road and Building Extraction

Subfield: Ocean Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,550

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, Tianjin Municipal Science and Technology Bureau