Semantic Drone - Dataset Ninja

Introduction #

Released 2019-01-25 ·Christian Mostegel, Michael Maurer, Nikolaus Heranet al.

The primary goal of the Semantic Drone Dataset is to enhance the safety of autonomous drone flight and landing procedures through improved semantic comprehension of urban environments. This dataset comprises imagery captured from a bird’s-eye (nadir) perspective, showcasing over 20 houses, taken at altitudes ranging from 5 to 30 meters above the ground. The images are acquired using a high-resolution camera with a size of 6000x4000 pixels (24 megapixels). The training set includes 400 publicly accessible images, while the test set consists of 200 private images. Additionally, the dataset provides bounding box annotations for person detection within both the training and test sets.

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Summary #

Semantic Drone Dataset v1.1 is a dataset for a semantic segmentation task. It is used in the drone inspection domain.

The dataset consists of 400 images with 40169 labeled objects belonging to 24 different classes including obstacle, paved-area, person, and other: vegetation, dirt, gravel, wall, grass, fence, rocks, roof, tree, fence-pole, ar-marker, bicycle, window, water, bald-tree, car, pool, door, dog, unlabeled, and conflicting.

Images in the Semantic Drone dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There is 1 split in the dataset: train (400 images). The dataset was released in 2019 by the Institute of Computer Graphics and Vision (ICG), Graz University of Technology, Austria.

Here are the visualized examples for the classes:

Explore #

Semantic Drone dataset has 400 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

Sample annotation mask from Semantic Drone

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Have a look at 400 images

Because of dataset's license preview is limited to 12 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 24 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Rows 1-10 of 24

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
obstacle➔ mask	389	6806	17.5	3.64%
paved-area➔ mask	380	4496	11.83	39.72%
person➔ mask	367	1525	4.16	1.15%
vegetation➔ mask	359	7736	21.55	7.91%
dirt➔ mask	332	6296	18.96	3.86%
gravel➔ mask	330	3499	10.6	8.86%
wall➔ mask	292	1903	6.52	3.69%
grass➔ mask	273	1843	6.75	29.28%
fence➔ mask	212	986	4.65	1.81%
rocks➔ mask	210	1279	6.09	1.37%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Rows 1-10 of 22

Class	Object count	Avg area	Max area	Min area	Min height	Min height	Max height	Max height	Avg height	Avg height	Min width	Min width	Max width	Max width
vegetation mask	7736	0.37%	30.34%	0%	1px	0.03%	4000px	100%	259px	6.49%	1px	0.02%	5992px	99.87%
obstacle mask	6806	0.21%	18.12%	0%	1px	0.03%	3999px	99.97%	242px	6.04%	1px	0.02%	5997px	99.95%
dirt mask	6296	0.2%	19.31%	0%	1px	0.03%	4000px	100%	222px	5.54%	1px	0.02%	5999px	99.98%
paved-area mask	4496	3.36%	99.79%	0%	1px	0.03%	4000px	100%	569px	14.22%	1px	0.02%	6000px	100%
gravel mask	3499	0.84%	29.9%	0%	1px	0.03%	4000px	100%	511px	12.78%	1px	0.02%	5999px	99.98%
wall mask	1903	0.57%	15.78%	0%	1px	0.03%	4000px	100%	509px	12.71%	1px	0.02%	5996px	99.93%
grass mask	1843	4.34%	99.41%	0%	1px	0.03%	4000px	100%	655px	16.38%	1px	0.02%	6000px	100%
person mask	1525	0.28%	5.58%	0%	8px	0.2%	1680px	42%	301px	7.53%	13px	0.22%	2527px	42.12%
rocks mask	1279	0.22%	9.06%	0%	2px	0.05%	4000px	100%	299px	7.47%	1px	0.02%	2883px	48.05%
fence mask	986	0.39%	7.39%	0%	1px	0.03%	4000px	100%	635px	15.86%	1px	0.02%	3504px	58.4%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Objects #

Table contains all 40169 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Rows 1-10 of 40169

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	grass mask	457.jpg	4000 x 6000	3997px	99.92%	6000px	100%	99.11%
2➔	grass mask	457.jpg	4000 x 6000	43px	1.07%	61px	1.02%	0.01%
3➔	grass mask	457.jpg	4000 x 6000	68px	1.7%	38px	0.63%	0%
4➔	grass mask	457.jpg	4000 x 6000	14px	0.35%	8px	0.13%	0%
5➔	bicycle mask	457.jpg	4000 x 6000	114px	2.85%	133px	2.22%	0.03%
6➔	bicycle mask	457.jpg	4000 x 6000	120px	3%	133px	2.22%	0.02%
7➔	bicycle mask	457.jpg	4000 x 6000	96px	2.4%	210px	3.5%	0.05%
8➔	bicycle mask	457.jpg	4000 x 6000	45px	1.12%	117px	1.95%	0.01%
9➔	bicycle mask	457.jpg	4000 x 6000	56px	1.4%	94px	1.57%	0.01%
10➔	dirt mask	457.jpg	4000 x 6000	66px	1.65%	82px	1.37%	0.02%

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).

Source

Citation #

If you use this dataset in your research, please cite the following URL:

http://dronedataset.icg.tugraz.at

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-semantic-drone-dataset,
  title = { Visualization Tools for Semantic Drone Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/semantic-drone } },
  url = { https://datasetninja.com/semantic-drone },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Please visit dataset homepage to download the data.

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