Aerial Semantic Drone Dataset

The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above the ground. A high-resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.

This dataset is taken from https://www.kaggle.com/awsaf49/semantic-drone-dataset. We remove and add files and information that we needed for our research purpose. We create our tiff files with a resolution of 1200x800 pixel in 24 channel with each channel represent classes that have been preprocessed from png files label. We reduce the resolution and compress the tif files with tiffile python library.

If you have any problem with tif dataset that we have been modified you can contact nunenuh@gmail.com and gaungalif@gmail.com.

This dataset was a copy from the original dataset (link below), we provide and add some improvement in the semantic data and classes. There are the availability of semantic data in png and tiff format with a smaller size as needed.

Semantic Annotation

The images are labelled densely using polygons and contain the following 24 classes:

unlabeled paved-area dirt grass gravel water rocks pool vegetation roof wall window door fence fence-pole person dog car bicycle tree bald-tree ar-marker obstacle conflicting

Directory Structure and Files

> images
> labels/png
> labels/tiff
 - class_to_idx.json
 - classes.csv
 - classes.json
 - idx_to_class.json

Included Data

• 400 training images in jpg format can be found in "aerial_semantic_drone/images"
• Dense semantic annotations in png format can be found in "aerial_semantic_drone/labels/png"
• Dense semantic annotations in tiff format can be found in "aerial_semantic_drone/labels/tiff"
• Semantic class definition in csv format can be found in "aerial_semantic_drone/classes.csv"
• Semantic class definition in json can be found in "aerial_semantic_drone/classes.json"
• Index to class name file can be found in "aerial_semantic_drone/idx_to_class.json"
• Class name to index file can be found in "aerial_semantic_drone/idx_to_class.json"

Contact

aerial@icg.tugraz.at

Citation

If you use this dataset in your research, please cite the following URL: www.dronedataset.icg.tugraz.at

License

The Drone Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Graz University of Technology) do not accept any responsibility for errors or omissions. That you include a reference to the Semantic Drone Dataset in any work that makes use of the dataset. For research papers or other media link to the Semantic Drone Dataset webpage.

That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character. That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain. That all rights not expressly granted to you are reserved by us (Graz University of Technology).

Unmanned Aerial Vehicles Dataset:

The Unmanned Aerial Vehicle (UAV) Image Dataset consists of a collection of images containing UAVs, along with object annotations for the UAVs found in each image. The annotations have been converted into the COCO, YOLO, and VOC formats for ease of use with various object detection frameworks. The images in the dataset were captured from a variety of angles and under different lighting conditions, making it a useful resource for training and evaluating object detection algorithms for UAVs. The dataset is intended for use in research and development of UAV-related applications, such as autonomous flight, collision avoidance and rogue drone tracking and following. The dataset consists of the following images and detection objects (Drone):

It is advised to further enhance the dataset so that random augmentations are probabilistically applied to each image prior to adding it to the batch for training. Specifically, there are a number of possible transformations such as geometric (rotations, translations, horizontal axis mirroring, cropping, and zooming), as well as image manipulations (illumination changes, color shifting, blurring, sharpening, and shadowing).

**NOTE** If you use this dataset in your research/publication please cite us using the following

Rafael Makrigiorgis, Nicolas Souli, & Panayiotis Kolios. (2022). Unmanned Aerial Vehicles Dataset (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7477569

Semantic Drone Dataset

The Semantic Drone Dataset focuses on semantic understanding of urban scenes for increasing the safety of autonomous drone flight and landing procedures. The imagery depicts more than 20 houses from nadir (bird's eye) view acquired at an altitude of 5 to 30 meters above ground. A high resolution camera was used to acquire images at a size of 6000x4000px (24Mpx). The training set contains 400 publicly available images and the test set is made up of 200 private images.

Semantic Annotation

The images are labeled densely using polygons and contain the following 22 classes:

• unlabeled
• paved-area
• dirt
• grass
• gravel
• water
• rocks
• pool
• vegetation
• roof
• wall
• window
• door
• fence
• fence-pole
• person
• dog
• car
• bicycle
• tree
• bald-tree
• ar-marker
• obstacle

Included Data

• 400 training images
• Dense semantic annotations in png format can be found in trainingset/gt/semantic/labelimages/
• Dense semantic annotations as LabelMe xml files can be found in trainingset/gt/semantic/labelme_xml/
• Semantic class definition can be found in trainingset/gt/semantic/classdict.csv
• Bounding boxes of persons as LabelMe xml files found "in trainingset/gt/boundingbox/labelmexml
• Bounding boxes of persons as mask images found in trainingset/gt/boundingbox/masks
• Bounding boxes of individual persons as mask images found in trainingset/gt/boundingbox/masks_instances
• Bounding boxes of persons as python pickle file found in trainingset/gt/boundingbox/bounding_boxes/person/

Contact

aerial@icg.tugraz.at

Citation

If you use this dataset in your research, please cite the following URL: www.dronedataset.icg.tugraz.at

License

The Drone Dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree:

• That the dataset comes "AS IS", without express or implied warranty. Although every effort has been made to ensure accuracy, we (Graz University of Technology) do not accept any responsibility for errors or omissions.
• That you include a reference to the Semantic Drone Dataset in any work that makes use of the dataset. For research papers or other media link to the Semantic Drone Dataset webpage.
• That you do not distribute this dataset or modified versions. It is permissible to distribute derivative works in as far as they are abstract representations of this dataset (such as models trained on it or additional annotations that do not directly include any of our data) and do not allow to recover the dataset or something similar in character.
• That you may not use the dataset or any derivative work for commercial purposes as, for example, licensing or selling the data, or using the data with a purpose to procure a commercial gain.
• That all rights not expressly granted to you are reserved by us (Graz University of Technology).