# Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction

arXiv: https://arxiv.org/abs/2505.06905 · HTML: https://mlsanity.com/paper/2505.06905/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 109 references that are in the map.
- First posted on arXiv 11 May 2025.

## Builds on (most cited first, 30 of 36)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Visualizing and Understanding Convolutional Networks](https://mlsanity.com/paper/1311.2901/) (2013)
- [Visual Prompt Tuning](https://mlsanity.com/paper/2203.12119/) (2022)
- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [Depth Anything: Unleashing the Power of Large-Scale Unlabeled Data](https://mlsanity.com/paper/2401.10891/) (2024)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://mlsanity.com/paper/2205.13535/) (2022)
- [Deeper Depth Prediction with Fully Convolutional Residual Networks](https://mlsanity.com/paper/1606.00373/) (2016)
- [Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields](https://mlsanity.com/paper/1502.07411/) (2015)
- [Explainable Machine Learning for Scientific Insights and Discoveries](https://mlsanity.com/paper/1905.08883/) (2019)
- [MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformer](https://mlsanity.com/paper/2208.03543/) (2022)
- [How do neural networks see depth in single images?](https://mlsanity.com/paper/1905.07005/) (2019)
- [RarePlanes: Synthetic Data Takes Flight](https://mlsanity.com/paper/2006.02963/) (2020)
- [BRIGHT: A globally distributed multimodal building damage assessment dataset with very-high-resolution for all-weather disaster response](https://mlsanity.com/paper/2501.06019/) (2025)
- [IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network](https://mlsanity.com/paper/1802.10249/) (2018)
- [Visualization of Convolutional Neural Networks for Monocular Depth Estimation](https://mlsanity.com/paper/1904.03380/) (2019)
- [The Synthinel-1 dataset: a collection of high resolution synthetic overhead imagery for building segmentation](https://mlsanity.com/paper/2001.05130/) (2020)
- [SyntheWorld: A Large-Scale Synthetic Dataset for Land Cover Mapping and Building Change Detection](https://mlsanity.com/paper/2309.01907/) (2023)
- [Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery](https://mlsanity.com/paper/2301.04581/) (2023)
- [Beyond single receptive field: A receptive field fusion-and-stratification network for airborne laser scanning point cloud classification](https://mlsanity.com/paper/2207.10278/) (2022)
- [There Are No Data Like More Data- Datasets for Deep Learning in Earth Observation](https://mlsanity.com/paper/2310.19231/) (2023)
- [Height estimation from single aerial images using a deep ordinal regression network](https://mlsanity.com/paper/2006.02801/) (2020)
- [3D Building Reconstruction from Monocular Remote Sensing Images with Multi-level Supervisions](https://mlsanity.com/paper/2404.04823/) (2024)
- [OpenEarthMap-SAR: A Benchmark Synthetic Aperture Radar Dataset for Global High-Resolution Land Cover Mapping](https://mlsanity.com/paper/2501.10891/) (2025)

## Built on by (most cited first, 4 of 4)

- [Seamless High-Resolution Terrain Reconstruction: A Prior-Based Vision Transformer Approach](https://mlsanity.com/paper/2507.09681/) (2025)
- [The First Assessment of PhiSat-2 Imagery for Monocular Building Height Estimation](https://mlsanity.com/paper/2603.29245/) (2026)
- [Test-Time Adaptation for Height Completion via Self-Supervised ViT Features and Monocular Foundation Models](https://mlsanity.com/paper/2604.02009/) (2026)
- [Rapid Debris-Volume Estimation from Post-Hurricane Aerial Imagery](https://mlsanity.com/paper/2608.17165/) (2026)

## Abstract (arXiv)

Monocular height estimation (MHE) from very-high-resolution (VHR) optical imagery remains challenging due to limited structural cues and the high cost and geographic constraints of conventional elevation data such as airborne LiDAR and multi-view stereo. Although recent MHE and monocular depth estimation (MDE) models show strong performance, their robustness under varied illumination and scene conditions is still limited. We introduce a fully automated correction pipeline that integrates sparse, imperfect global LiDAR measurements from ICESat-2 with deep learning predictions to enhance accuracy and stability. The workflow relies entirely on publicly available models and data and requires only a single georeferenced optical image to produce corrected height maps, enabling low-cost and globally scalable deployment. We also establish the first benchmark for this task, evaluating two random forest based approaches, four parameter efficient fine tuning methods, and full fine tuning. Experiments across six diverse regions at 0.5 m resolution (297 km2), covering the urban cores of Tokyo, Paris, and Sao Paulo as well as suburban and forested areas, show substantial gains. The best method reduces the MHE model's mean absolute error (MAE) by 30.9 percent and improves its F1HE score by 44.2 percent. For the MDE model, MAE improves by 24.1 percent and the F1HE score by 25.1 percent. These results validate the effectiveness of our correction pipeline and demonstrate how sparse global LiDAR can systematically strengthen both MHE and MDE models, enabling scalable and widely accessible 3D height mapping.