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AeroScapes Dataset

3269122795
Tagdrones
Tasksemantic segmentation
Release YearMade in 2018
LicenseCC BY-SA 4.0
Download695 MB

Introduction #

Released 2018-05-25 Β·Ishan Nigam, Chen Huang, Deva Ramanan

As stated by the authors of The AeroScapes Semantic Segmentation Dataset, they explore methods for learning across train and test distributions that dramatically differ in scene structure, viewpoints, and object statistics. Authors are motivated by the proliferation of aerial drone robotics, and consider the target task of semantic segmentation from aerial viewpoints. Inspired by the impact of Cityscapes, the authors introduce AeroScapes, a new dataset of 3269 images of aerial scenes (captured with a fleet of drones) annotated with dense semantic segmentation.

This dataset differs from existing segmentation datasets (that focus on ground-view or indoor scene domains) in terms of viewpoint, scene composition, and object scales. The authors propose a simple but effective approach for transferring knowledge from such diverse domains (for which considerable annotated training data exists) to the target task. To do so, authors train multiple models for aerial segmentation via progressive fine-tuning through each source domain. They then treat these collections of models as an ensemble that can be aggregated to significantly improve performance. Authors demonstrate large absolute improvements (8.12%) over widely-used standard baselines.

Traditional localization benchmarks focus primarily on object recognition in images, often neglecting the context in which these objects are situated. Background elements, such as terrain and aerial features, offer crucial semantic and geometric context to foreground objects. For instance, an autonomous car uses identified roads within its line of sight for navigation, avoiding parking attempts in sky or water areas. Hence, it’s vital to train terrain-based and aerial autonomous agents to recognize both foreground and background elements.

Real-time autonomous systems heavily rely on scene understanding to make decisions, requiring evaluation benchmarks to incorporate labeled image sequences. Agents using visual scene understanding must also integrate temporal information into their representations, making video data integration a necessity. The AeroScapes dataset comprises 3269 images from 141 video sequence, with some of it being temporally downsampled. The class distribution within AeroScapes reflects the common data imbalance in outdoor images, including both stuff and thing annotations, with the thing classes representing only approximately 1.51% of the data.

Aerial robots provide the advantage of exploring diverse environments and viewpoints that ground-based autonomous cars cannot access. This led to the creation of the AeroScapes Dataset, featuring images captured by drones at altitudes of 5-50 meters. These images come with segmentation maps, labeling both stuff classes (vegetation, roads, sky, construction) and thing classes (person, bikes, cars, drones, boats, obstacles, animals).

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

The AeroScapes Semantic Segmentation Dataset is a dataset for a semantic segmentation task. It is used in the drone inspection domain.

The dataset consists of 3269 images with 18897 labeled objects belonging to 12 different classes including background, vegetation, road, and other: person, obstacle, construction, bike, car, sky, drone, animal, and boat.

Images in the AeroScapes dataset have pixel-level semantic segmentation annotations. All images are labeled (i.e. with annotations). There are 2 splits in the dataset: train (2621 images) and val (648 images). Additionally, every image contains id of its video sequence (total 141). The dataset was released in 2018 by the Carnegie Mellon University.

Here are the visualized examples for the classes:

Explore #

AeroScapes dataset has 3269 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 AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
OpenSample annotation mask from AeroScapesSample image from AeroScapes
πŸ‘€
Have a look at 3269 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 12 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 12
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
backgroundβž”
mask
3269
3269
1
23.12%
vegetationβž”
mask
2983
2983
1
40.63%
roadβž”
mask
2804
2804
1
34.12%
personβž”
mask
2597
2597
1
0.51%
obstacleβž”
mask
2150
2150
1
0.86%
constructionβž”
mask
1729
1729
1
8.4%
bikeβž”
mask
1264
1264
1
0.14%
carβž”
mask
1005
1005
1
1.2%
skyβž”
mask
520
520
1
28.77%
droneβž”
mask
410
410
1
0.23%

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.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

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 12
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
background
mask
3269
23.11%
99.99%
0%
123px
17.08%
720px
100%
706px
98.11%
1px
0.08%
1280px
100%
vegetation
mask
2983
40.63%
99.15%
0.01%
23px
3.19%
720px
100%
611px
84.92%
9px
0.7%
1280px
100%
road
mask
2804
34.12%
99.09%
0%
1px
0.14%
720px
100%
621px
86.22%
1px
0.08%
1280px
100%
person
mask
2597
0.51%
13.64%
0%
8px
1.11%
720px
100%
181px
25.17%
4px
0.31%
1237px
96.64%
obstacle
mask
2150
0.86%
11.34%
0%
6px
0.83%
720px
100%
301px
41.84%
4px
0.31%
1280px
100%
construction
mask
1729
8.4%
83.18%
0.02%
6px
0.83%
720px
100%
340px
47.21%
16px
1.25%
1280px
100%
bike
mask
1264
0.14%
1.75%
0%
6px
0.83%
513px
71.25%
85px
11.87%
3px
0.23%
852px
66.56%
car
mask
1005
1.2%
31.94%
0.01%
8px
1.11%
720px
100%
176px
24.42%
12px
0.94%
1263px
98.67%
sky
mask
520
28.77%
59.27%
0.07%
17px
2.36%
700px
97.22%
293px
40.64%
70px
5.47%
1280px
100%
drone
mask
410
0.23%
2.71%
0.01%
9px
1.25%
430px
59.72%
73px
10.18%
10px
0.78%
329px
25.7%

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 18897 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 18897
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”
vegetation
mask
041005_028.jpg
720 x 1280
717px
99.58%
1279px
99.92%
77.64%
2βž”
bike
mask
041005_028.jpg
720 x 1280
86px
11.94%
26px
2.03%
0.06%
3βž”
road
mask
041005_028.jpg
720 x 1280
718px
99.72%
373px
29.14%
20.96%
4βž”
obstacle
mask
041005_028.jpg
720 x 1280
56px
7.78%
746px
58.28%
0.35%
5βž”
person
mask
041005_028.jpg
720 x 1280
45px
6.25%
34px
2.66%
0.1%
6βž”
background
mask
041005_028.jpg
720 x 1280
720px
100%
1280px
100%
0.89%
7βž”
vegetation
mask
041005_058.jpg
720 x 1280
717px
99.58%
1279px
99.92%
90.39%
8βž”
road
mask
041005_058.jpg
720 x 1280
718px
99.72%
570px
44.53%
9.07%
9βž”
background
mask
041005_058.jpg
720 x 1280
720px
100%
1280px
100%
0.55%
10βž”
person
mask
310021_032.jpg
720 x 1280
251px
34.86%
152px
11.88%
1.92%

License #

The AeroScapes Semantic Segmentation Dataset is under CC BY-SA 4.0 license.

Source

Citation #

If you make use of the AeroScapes data, please cite the following reference:

Ensemble Knowledge Transfer for Semantic Segmentation
Ishan Nigam, Chen Huang, Deva Ramanan
Proceedings of the 2018 IEEE Winter Conference on Applications of Computer Vision

Source

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

@misc{ visualization-tools-for-aeroscapes-dataset,
  title = { Visualization Tools for AeroScapes Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/aeroscapes } },
  url = { https://datasetninja.com/aeroscapes },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Dataset AeroScapes can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='AeroScapes', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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