Augmenting Depth Estimation
with Geospatial Context

Scott Workman and Hunter Blanton, ICCV 2021

Table of Contents

• Table of Contents
• Get Started
  • Installation
  • Train and Evaluate
  • Visualize
• HoliCity-Overhead Dataset
  • Download
  • Precompute Context
• Publications
• License

Get started

Installation

1. Clone the repository.

git clone https://github.com/scottworkman/geodepth
cd geodepth

2. Create and activate the environment (e.g., using conda).

conda env create -f resources/environment.yml
conda activate geodepth

Train and Evaluate

To train our approach:

python main.py

To train other variants of our approach, as described in the paper, adjust the input arguments:

python main.py --help

For example:

python main.py --method=ground

To evaluate:

cd eval
python compute_metrics.py

Visualize

The example notebook idea/geodepth.ipynb demonstrates the core idea of this work. For visualizing results, see visualize/predictions.ipynb.

HoliCity-Overhead Dataset

Our dataset can be obtained using the links below. The scripts in this repository asume the dataset lives at holicity-overhead/ under the root directory. Extract the dataset wherever you like and then create a symbolic link:

ln -s /path/to/dataset holicity-overhead

Disclaimer: The overhead imagery is owned and copyrighted by Microsoft and must not be used for any commercial purposes.

Download

• HoliCity-Overhead
  • overhead imagery and height maps
• HoliCity-Overhead (full)
  • HoliCity-Overhead combined with the necessary components from HoliCity to support training

Precompute Context

Methods that start from a known height map use precomputed geospatial context (in the form of a synthetic depth image) to reduce computational overhead:

cd scripts
python precompute_context.py

Publications

Please cite our paper if you find this work helpful:

@inproceedings{workman2021augmenting,
  author={Scott Workman and Hunter Blanton},
  title={{Augmenting Depth Estimation with Geospatial Context}},
  booktitle={{IEEE International Conference on Computer Vision (ICCV)}},
  year=2021
}

This project builds on the HoliCity dataset:

@article{zhou2020holicity,
  author={Zhou, Yichao and Huang, Jingwei and Dai, Xili and Liu, Shichen and Luo,
          Linjie and Chen, Zhili and Ma, Yi},
  title={HoliCity: A city-scale data platform for learning holistic 3D structures},
  journal={arXiv preprint arXiv:2008.03286},
  year={2020}
}

License

The code is distributed under the CC BY-NC-SA 4.0 License. See LICENSE for more information.

# Copyright © Scott Workman. 2025. All rights reserved.
# Licensed under CC BY-NC-SA 4.0 (non-commercial use only).