# IM2HEIGHT: Height Estimation from Single Monocular Imagery via Fully Residual Convolutional-Deconvolutional Network

arXiv: https://arxiv.org/abs/1802.10249 · HTML: https://mlsanity.com/paper/1802.10249/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2018.

## Built on by (most cited first, 30 of 31)

- [Shadow Neural Radiance Fields for Multi-view Satellite Photogrammetry](https://mlsanity.com/paper/2104.09877/) (2021)
- [Change Detection Methods for Remote Sensing in the Last Decade: A Comprehensive Review](https://mlsanity.com/paper/2305.05813/) (2023)
- [Elevation Estimation-Driven Building 3D Reconstruction from Single-View Remote Sensing Imagery](https://mlsanity.com/paper/2301.04581/) (2023)
- [Large-scale Building Height Retrieval from Single SAR Imagery based on Bounding Box Regression Networks](https://mlsanity.com/paper/2111.09460/) (2021)
- [Inferring 3D change detection from bitemporal optical images](https://mlsanity.com/paper/2205.15903/) (2022)
- [Single View Geocentric Pose in the Wild](https://mlsanity.com/paper/2105.08229/) (2021)
- [THE Benchmark: Transferable Representation Learning for Monocular Height Estimation](https://mlsanity.com/paper/2112.14985/) (2021)
- [HTC-DC Net: Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2309.16486/) (2023)
- [MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images](https://mlsanity.com/paper/2104.02846/) (2021)
- [Deep-Learning-Based Single-Image Height Reconstruction from Very-High-Resolution SAR Intensity Data](https://mlsanity.com/paper/2111.02061/) (2021)
- [Aerial Scene Understanding in The Wild: Multi-Scene Recognition via Prototype-based Memory Networks](https://mlsanity.com/paper/2104.11200/) (2021)
- [GAMUS: A Geometry-aware Multi-modal Semantic Segmentation Benchmark for Remote Sensing Data](https://mlsanity.com/paper/2305.14914/) (2023)
- [GroundUp: Rapid Sketch-Based 3D City Massing](https://mlsanity.com/paper/2407.12739/) (2024)
- [Enhancing Monocular Height Estimation via Sparse LiDAR-Guided Correction](https://mlsanity.com/paper/2505.06905/) (2025)
- [Disentangled Latent Transformer for Interpretable Monocular Height Estimation](https://mlsanity.com/paper/2201.06357/) (2022)
- [HeightFormer: A Multilevel Interaction and Image-adaptive Classification-regression Network for Monocular Height Estimation with Aerial Images](https://mlsanity.com/paper/2310.07995/) (2023)
- [Enhancing Monocular Height Estimation from Aerial Images with Street-view Images](https://mlsanity.com/paper/2311.02121/) (2023)
- [Reconstructing Building Height from Spaceborne TomoSAR Point Clouds Using a Dual-Topology Network](https://mlsanity.com/paper/2601.00658/) (2026)
- [Single-View Height Estimation with Conditional Diffusion Probabilistic Models](https://mlsanity.com/paper/2304.13214/) (2023)
- [SC-Lane: Slope-aware and Consistent Road Height Estimation Framework for 3D Lane Detection](https://mlsanity.com/paper/2508.10411/) (2025)
- [TSE-Net: Semi-supervised Monocular Height Estimation from Single Remote Sensing Images](https://mlsanity.com/paper/2511.13552/) (2025)
- [Geo3DVQA: Evaluating Vision-Language Models for 3D Geospatial Reasoning from Aerial Imagery](https://mlsanity.com/paper/2512.07276/) (2025)
- [BuildMamba: A Visual State-Space Based Model for Multi-Task Building Segmentation and Height Estimation from Satellite Images](https://mlsanity.com/paper/2603.08523/) (2026)
- [Focusing on Shadows for Predicting Heightmaps from Single Remotely Sensed RGB Images with Deep Learning](https://mlsanity.com/paper/2104.10874/) (2021)
- [Mapping Vulnerable Populations with AI](https://mlsanity.com/paper/2107.14123/) (2021)
- [ZRG: A Dataset for Multimodal 3D Residential Rooftop Understanding](https://mlsanity.com/paper/2304.13219/) (2023)
- [Estimate the building height at a 10-meter resolution based on Sentinel data](https://mlsanity.com/paper/2405.00989/) (2024)
- [Enhancing Monocular Height Estimation via Weak Supervision from Imperfect Labels](https://mlsanity.com/paper/2506.02534/) (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)

## Abstract (arXiv)

In this paper we tackle a very novel problem, namely height estimation from a single monocular remote sensing image, which is inherently ambiguous, and a technically ill-posed problem, with a large source of uncertainty coming from the overall scale. We propose a fully convolutional-deconvolutional network architecture being trained end-to-end, encompassing residual learning, to model the ambiguous mapping between monocular remote sensing images and height maps. Specifically, it is composed of two parts, i.e., convolutional sub-network and deconvolutional sub-network. The former corresponds to feature extractor that transforms the input remote sensing image to high-level multidimensional feature representation, whereas the latter plays the role of a height generator that produces height map from the feature extracted from the convolutional sub-network. Moreover, to preserve fine edge details of estimated height maps, we introduce a skip connection to the network, which is able to shuttle low-level visual information, e.g., object boundaries and edges, directly across the network. To demonstrate the usefulness of single-view height prediction, we show a practical example of instance segmentation of buildings using estimated height map. This paper, for the first time in the remote sensing community, attempts to estimate height from monocular vision. The proposed network is validated using a large-scale high resolution aerial image data set covered an area of Berlin. Both visual and quantitative analysis of the experimental results demonstrate the effectiveness of our approach.