Dataset Ninja LogoDataset Ninja:

Semantic Drone Dataset

400241
Tagaerial, drones
Tasksemantic segmentation
Release YearMade in 2019
Licensecustom

Introduction #

Released 2019-01-25 Β·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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Dataset LinkHomepage

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.

OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
OpenSample annotation mask from Semantic DroneSample image from Semantic Drone
πŸ‘€
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.

Search
Rows 1-10 of 24
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
obstacleβž”
mask
389
6806
17.5
3.64%
paved-areaβž”
mask
380
4496
11.83
39.72%
personβž”
mask
367
1525
4.16
1.15%
vegetationβž”
mask
359
7736
21.55
7.91%
dirtβž”
mask
332
6296
18.96
3.86%
gravelβž”
mask
330
3499
10.6
8.86%
wallβž”
mask
292
1903
6.52
3.69%
grassβž”
mask
273
1843
6.75
29.28%
fenceβž”
mask
212
986
4.65
1.81%
rocksβž”
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.

Search
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.

Spatial Heatmap

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.

Search
Rows 1-10 of 40169
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
grass
mask
457.jpg
4000 x 6000
3997px
99.92%
6000px
100%
99.11%
2βž”
grass
mask
457.jpg
4000 x 6000
43px
1.07%
61px
1.02%
0.01%
3βž”
grass
mask
457.jpg
4000 x 6000
68px
1.7%
38px
0.63%
0%
4βž”
grass
mask
457.jpg
4000 x 6000
14px
0.35%
8px
0.13%
0%
5βž”
bicycle
mask
457.jpg
4000 x 6000
114px
2.85%
133px
2.22%
0.03%
6βž”
bicycle
mask
457.jpg
4000 x 6000
120px
3%
133px
2.22%
0.02%
7βž”
bicycle
mask
457.jpg
4000 x 6000
96px
2.4%
210px
3.5%
0.05%
8βž”
bicycle
mask
457.jpg
4000 x 6000
45px
1.12%
117px
1.95%
0.01%
9βž”
bicycle
mask
457.jpg
4000 x 6000
56px
1.4%
94px
1.57%
0.01%
10βž”
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:

  1. That the dataset comes β€œAS 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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 = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

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