Datasets:
Tasks:
Image Segmentation
Modalities:
Image
Formats:
imagefolder
Size:
1K - 10K
Tags:
building
License:
File size: 4,552 Bytes
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---
license: mit
task_categories:
- image-segmentation
tags:
- building
size_categories:
- 1K<n<10K
---
# Building Contour Detection and Height Estimation Problem
## Dataset Summary
The **building_height_estimation** dataset is a collection of satellite images with annotated building footprints (polygons) and corresponding building heights.
It is designed for the **joint tasks of building contour detection (segmentation)** and **height estimation (regression)** from monocular aerial images.
* **Source & Owner**: The dataset originates from the [AlgoTester Building Contour Detection & Height Estimation Contest](https://algotester.com/en/ContestProblem/DisplayWithFile/135254).
* **Hugging Face Host**: [MElHuseyni / building_height_estimation](https://huggingface.co/datasets/MElHuseyni/building_height_estimation).
* **License**: MIT
* **Tags**: `building`, `segmentation`, `regression`, `remote sensing`
*Example of a dataset sample: a 512×512 satellite image with annotated building footprints (polygon overlays) and their corresponding height values.*
---
## Dataset Description
Each sample consists of:
* **Image**: RGB satellite image (512×512 px).
* **Annotations**: A LabelMe-like JSON containing:
* `points`: Polygon vertices (x, y) marking building footprints
* `group_id`: Numeric building height (meters)
The dataset supports training and evaluation for:
* Building footprint extraction
* Height estimation from monocular imagery
* Joint segmentation + regression architectures
---
## Dataset Structure
```
/
├── images/ # Training images
│ ├── img0001.png
│ ├── ...
│
├── ground_truth_files/ # Training labels (JSON)
│ ├── img0001.json
│ ├── ...
│
└── test_images/ # Test set (no ground truth provided)
├── test0001.png
├── ...
```
**Example Label (JSON):**
```json
{
"shapes": [
{
"points": [[316, 486], [307, 510], [312, 512]],
"group_id": 9
},
{
"points": [[416, 457], [435, 446], [421, 423], [402, 434]],
"group_id": 7
}
]
}
```
---
## Evaluation & Scoring
The official contest defines the score as:
```
Score = max(0, ⌊ (Precision + Recall − 4 × HeightError) × 5 × 10⁴ ⌋)
```
* **Precision**: Correct predicted building area ÷ total predicted area
* **Recall**: Correctly matched ground truth area ÷ total ground truth area
* **HeightError**: Weighted RMSE of predicted vs. true heights for matched buildings
**Important constraints:**
* ≤ 1,000 buildings per image
* ≤ 300 vertices per polygon
* Total vertices per JSON ≤ 5,000
* Height ∈ [0, 1000] meters
* Coordinates within [0, 512]
* No self-intersecting polygons
* Overlap between two buildings ≤ 10% of smaller area
---
## Usage
```python
from datasets import load_dataset
ds = load_dataset("MElHuseyni/building_height_estimation")
sample = ds["train"][0]
print(sample["image"])
print(sample["buildings"])
```
Each `building` entry contains a list of polygon points and a height value.
---
## Limitations
* **Monocular Input**: Heights are inferred from a single RGB image, no stereo or LiDAR.
* **Annotation Noise**: Minor misalignments or errors in footprints may exist.
* **Imbalance**: Height distribution may be skewed (low-rise dominant).
* **Test Labels**: Hidden; only evaluable via AlgoTester scoring scripts.
---
## Citation
If you use this dataset, please cite both the Hugging Face dataset and the original AlgoTester contest:
```
@misc{building_height_estimation_MElHuseyni,
title = {building_height_estimation},
author = {MElHuseyni},
year = {2025},
howpublished = {Hugging Face Dataset},
url = {https://huggingface.co/datasets/MElHuseyni/building_height_estimation}
}
@online{algotester_building_height,
title = {Building Contour Detection & Height Estimation Contest},
author = {AlgoTester},
year = {2024},
url = {https://algotester.com/en/ContestProblem/DisplayWithFile/135254}
}
```
---
## Acknowledgements
* Dataset originally prepared and hosted by **AlgoTester** for their contest.
* Curated and published on Hugging Face by **[MElHuseyni](https://huggingface.co/MElHuseyni)**.
* Licensed under MIT for research and development purposes.
---
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