Landing Approach Runway Detection (LARD) is a dataset of aerial front view images of runways designed for aircraft landing phase. It contains over 17K synthetic images of various runways, enriched with more than 1800 annotated pictures from real landing footages for comparison.

We also provide a synthetic image generator based on Google Earth Studio if you want to enrich your dataset, or fatten your LARD. Starting from a database of runway positions, our generator produces high quality synthetic pictures of airport runways with their metadata. Through geometric transformations, these pictures can then be automatically annotated with the position of the runway or any targeted element in the aerial picture.

Synthetic and real runways	Synthetic landing sequence

🚀 Quickstart

• 💾 Download LARD dataset | secondary mirror
• 🔥 Generate scenarios (notebook)
• 🛠️ Export tool (notebook)
• 📜 Read paper

📚 Table of contents

• ✈️ LARD dataset
• ⚙️ Synthetic generator
  1. Enrich the runway database
  2. Generate scenarios
  3. Label automatically
• 🛠️ Dataset exploitation
• 👀 See Also
• 🎓 Authors
• 🗞️ Citation
• 📝 License

✈️ LARD Dataset

The LARD Dataset is declared and stored at data.gouv.fr under the DOI https://doi.org/10.57745/MZSH2Y. It is available to download directly on this page, or on a secondary mirror.

• 💾 LARD - Primary Download
• 💾 LARD - Secondary Mirror

[New 08/2023] Over 1700 images were annotated and added to the test set of real images

This dataset is dedicated to the detection of a runway from an aircraft during approach and landing phases. Therefore the majority of images in this dataset were generated using conventional landing trajectories: the set of possible positions and orientation of the aircraft during landing was defined as a generic landing approach cone which corresponds to the following ranges of parameters:

Parameter name	value ranges
Distance	[0.08, 3] NM
Vertical deviation	[2.2°, 3.8]°
Lateral deviation	[-4, 4]°
Yaw	[-10,10]°
Pitch	[-8,0]°
Roll	[-10,10]°

The first 3 parameters correspond to the position of the aircraft relative to the runway, while the last 3 parameters correspond to its orientation.

📁 Dataset structure

As of today, the dataset contains around 17K images in total, divided into the following main folders:

LARD_test_real/
LARD_test_synth/
LARD_train.csv
LARD_train_BIRK_LFST/
LARD_train_DAAG_DIAP/
LARD_train_domain_adaptation/
LARD_train_KMSY/
LARD_train_LFMP_LFPO/
LARD_train_LFQQ/
LARD_train_LPPT_SRLI/
LARD_train_VABB/

• All LARD_train_* folders contain images dedicated to training, from one or more airports
• The LARD_test_real folder is dedicated to testing models on real images, and is further divided into 3 subfolders with nominal_cases, edge_case with a poor runway visibility, and domain_adaptation
• The LARD_test_synth folder is dedicated to testing models on synthetic images
• The metadata are provided in a single file in each folder or subfolder, and the metadata of the whole set of training images is also provided as a single file at the root of the folder (LARD_train.csv)

💡 Note that this dataset may be updated in the future, with more images for new runways and a greater variety of sources, for both the train set and the test set (and possibly other sets)

🏷️ Metadata

The images of the dataset are provided in a images/ folder together with a .csv file which contains all the metadata. Some metadata are not always available for both real and synthetic images, but the following columns are common:

• First, a few general information about the pictures are provided, such as the relative path of the picture or its width and height. It is worth noting that the synthetic images all come in the same 2448×2648 resolution, while the pictures from real footage are in majority 3840×2160 or 1920×1080 resolutions.

• Then, data about the origin of the picture are given, such as the initial scenario which generated the image, or the type of data, which is either real or earth_studio for now, but could be completed with other sources such as flight simulators.

• The next columns indicate the airport and runway targeted during landing, when they are known. Note that the runway is not a number but an identifier which may contain a letter (L/C/R for Left/Center/Right).

• Finally, the last columns provide the pixel coordinates of each corner of the runway on the picture.

Due to limitations, the remaining columns are specific to only one source of data, but could also be used jointly by a new source of data such as a flight simulator supporting live weather.

For real images, three additional metadata are provided:

• weather, which primarily indicates that the runway visibility is reduced due to the presence of rain, on the windscreen or in the sky.

• night, a boolean value which is true if the image was taken during night time, and by extension if the runway lights are on.

• time to landing, a duration in seconds before reaching the aiming point, retrieved from the initial video footage.

For synthetic images, weather is not provided, and the night view is simulated with a simple reduction of ambient brightness, which produces far from realistic images since the runway lights stay unlit. The time to landing is also unknown, however in contrast to real images, the position of the camera relative to the runway is defined in the initial scenario and can be used to compute relevant data about the aircraft position, namely the slant distance, the along track distance, the height above runway, the lateral path angle and the vertical path angle. These values represent the relative position of the aircraft with respect to specific points on the runway.

Please refer to the infos.md file for a detailed description of each metadata.

🎥 Real footage

The following channels were used to build the set of real images of LARD. These content creators provided the rights to use their footage videos for research and for the sole purpose of building this open-source dataset, and their videos can be used to obtain more images of runways, which must then be manually annotated:

• The Great Flyer
• D.N.Hug
• High Pressure Aviation Films
• SOG Pilot
• 737 Aviation
• InZeAir84

⚙️ Synthetic Generator

The simplified internal working of the generator is as follows:

⚡️ In short:

1. 🔥 Update runways database (notebook): You can enrich the runway database used as input with new airports and runways.
2. 🔥 Generate scenarios (notebook): We offer the capability to generate highly configurable landing scenarios
3. 🔥 Label automatically (notebook): We provide the tool to automatically annotate the resulting images

🔌 Setup

• pip install: You can use the requirements.txt to install packages through:

pip install -r requirements.txt

1. Enrich the runway database

• 🔥 Notebook - Database generation
  • This notebook provides a comprehensive example for adding or update runways and airports in the database.
• The database used for LARD is provided in data/runways_database.json
• The database is a simple json file which contains the position of each corner in both lat/lon/alt coordinates and the corresponding ECEF (Earth-centered Earth-fixed) coordinates (A,B,C and D are the corners names):

    "AIRPORT": {
      "RUNWAY": {
        "A": {
            "position": {
                "x": -2183272.4689240726,
                "y": 4357496.814901311,
                "z": 4103132.159236785
            },
            "coordinate": {
                "latitude": 40.09288445386455,
                "longitude": 116.61262013286958,
                "altitude": 30.0
            }
        ...

Note that only the WGS84 coordinates (lat/lon/alt) are needed, as the ECEF coordinates are automatically computed by the script.

2. Generate a scenario

• 🔥 Notebook - Scenario generation
  • This notebook provides a comprehensive example for generating a scenario for a specific runway.
• 🖳 CLI - Alternately, you can generate news scenarios in command line :
  1. Configure your scenario as a .yml file. An example is provided in params/example_generation.yml
  2. Run the script with the .yml as an input:

python src/scenario/write_scenario.py params/example_generation.yml

The correspondance between parameter names and names used in the code are as follows:

Usual name	parameter name
Distance	min_distance_m, max_distance_m
Vertical deviation / Glide slope angle	alpha_v_deg, std_alpha_v_deg
Lateral deviation	alpha_h_deg, std_alpha_h_deg
Yaw	yaw_deg, std_yaw_deg
Pitch	pitch_deg, std_pitch_deg
Roll	roll_deg, std_roll_deg

However all these parameters except the distance are not defined as ranges, but rather with gaussian noise around a fixed center and a standard deviation (std_***).

This scenario generation will produce both a .esp file and a .yaml file. The first one is needed for Google Earth Studio, while the second one is needed for the labeling process.

2-bis Google Earth generation recommendations

The .esp file produced at step #2 can be directly loaded into Google Earth Studio. User can then verify the image sequence, especially to treat any problem relative to time of day in case of night images, which may require a new scenario generation, or potential occlusions. Then the sequence of images can be rendered by the tool, with the following recommendations:

• The image size should not be changed
• The attribution should be placed as far as possible in the corner at the bottom of the image
• In 'advanced' settings, The .json file for 3D data must be generated with a 'global' coordinate space

❗️ Important: This generation will create a folder, in which the .yaml file produced at step #2 must be copied or moved, to obtain a directory organized like the example on the right.

3. Automatic labeling

• 🔥 Notebook - Labeling
  • This notebook provides a comprehensive example to automatically label one or multiple Earth Studio generation results and export the corresponding dataset.
• 🖳 CLI - Alternately, you can generate news scenarios in command line :
  1. Configure your scenario as a .yml file. An example is provided in params/export_train_dataset.yml
  2. Run the script with the .yml as an input:

python src/labeling/generate_dataset.py params/export_train_dataset.yml

🛠️ Dataset Exploitation

• 🔥 Notebook - Export tool
  • This notebook provides a comprehensive example to export the dataset in a specific format such as COCO.
• This tool allows to generate bounding boxes around the corners of the runway, in several possible format, to choose between multiple label files or a single label file, and to crop the watermark.
• The dataset before export should respect a specific structure which is detailed in the notebook, and looks like this:

    DATASET_PATH/
    | metadata.csv
    | images/
    | | nameimage1.jpeg
    | | nameimage2.jpeg
    | | ...

• 🖳 CLI - Alternately, you can export a dataset in command line:

python src/dataset/lard_export.py --train data/multiple_train --test data/test_dataset -o data/converted_coco -b "xywh" -n -lf "multiple" -c -s " "

Please refer to the provided notebook (export tool) for comprehensive details about the possible export options and the folder structures.

Documentation

Technical Documentation The technical documentation of the different python modules and python methods implemented can be found in "./doc/build". You can open "./doc/build/index.html" in a web browser to navigate through it.

👀 See Also

More from the DEEL project:

• Xplique a Python library exclusively dedicated to explaining neural networks.
• deel-lip a Python library for training k-Lipschitz neural networks on TF.
• Influenciae Python toolkit dedicated to computing influence values for the discovery of potentially problematic samples in a dataset.
• PUNCC Puncc (Predictive uncertainty calibration and conformalization) is an open-source Python library that integrates a collection of state-of-the-art conformal prediction algorithms and related techniques for regression and classification problems
• OODEEL OODeel is a library that performs post-hoc deep OOD detection on already trained neural network image classifiers. The philosophy of the library is to favor quality over quantity and to foster easy adoption
• deel-torchlip a Python library for training k-Lipschitz neural networks on PyTorch.
• DEEL White paper a summary of the DEEL team on the challenges of certifiable AI and the role of data quality, representativity and explainability for this purpose.

🎓 Authors

        

This project is a joint research work from ONERA, IRT Saint Exupéry and AIRBUS. It received funding from the French ”Investing for the Future – PIA3” program within the Artificial and Natural Intelligence Toulouse Institute (ANITI).

🗞️ Citation

You can read our paper at https://hal.science/hal-04056760.

If you use LARD as part of your workflow in a scientific publication, please consider citing the following paper:

@unpublished{ducoffe:hal-04056760,
  TITLE = {{LARD - Landing Approach Runway Detection - Dataset for Vision Based Landing}},
  AUTHOR = {Ducoffe, M{\'e}lanie and Carrere, Maxime and F{\'e}liers, L{\'e}o and Gauffriau, Adrien and Mussot, Vincent and Pagetti, Claire and Sammour, Thierry},
  URL = {https://hal.science/hal-04056760},
  NOTE = {working paper or preprint},
  YEAR = {2023},
  MONTH = Apr,
  KEYWORDS = {open-source dataset ; vision based landing ; ML},
  PDF = {https://hal.science/hal-04056760/file/main.pdf},
  HAL_ID = {hal-04056760},
  HAL_VERSION = {v1},
}

📝 License

The package is released under MIT license.