---
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](/static-proxy?url=https%3A%2F%2Fcdn-uploads.huggingface.co%2Fproduction%2Fuploads%2F6422eab8e2029ade06eeee2c%2FMpD7VOzEZ0YbsNzWr_GD8.png)

*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.

---