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<!DOCTYPE html>
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<title>Learning to Understand Aerial Images (LUAI)</title>
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Challenge-2021 on
</h2>
<h1 style="text-align:center; font-weight: bold; font-size: 38px;color:#FF9900">
Learning to Understand Aerial Images
</h1>
<h2 style="text-align:center; font-weight: bold; font-style: italic">
<span class="subheading" style="text-align:center; font-weight:bold; font-style: italic"></span>
October 11, 2021, Montreal, Canada.
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Challenge-2021 on
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<h1 style="text-align:center; font-weight: bold ; color:#FF9900">
<nobr>Learning to Understand Aerial Images</nobr>
</h1>
</h3 style="text-align:center; font-weight: bold; font-style: italic">
October 11, 2021, Montreal, Canada.
</h3>
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<h2 style="text-align:left; margin-bottom:10px; margin-top:20px; ">
Object detection
</h2>
<p style="text-align:justify">
<!-- DOTA-v1.5 is an updated version of <a href="https://captain-whu.github.io/DOTA">DOTA-v1.0</a>. Both of them use the
same aerial images but DOTA-v1.5 has revised and updated the annotation of objects, where many small object instances about or below 10 pixels
that were missed in DOTA-v1.0 have been additionally annotated. The categories of DOTA-v1.5 is also extended. -->
Object detection track is based on <a href="https://captain-whu.github.io/DOTA/dataset.html">DOTA-v2.0</a>. DOTA-v2.0 collects more Google Earth, GF- 2 Satellite, and aerial images. There are 18 common categories, 11,268 images and
1,793,658 instances in DOTA-v2.0. Compared to DOTA-v1.5, it further adds the new categories of ”airport” and ”helipad”. The 11,268 images of DOTA are split into training, validation, test-dev, and test-challenge sets. To avoid the
problem of overfitting, the proportion of the training and validation set is smaller than the test set. Furthermore, we have two test sets, namely test-dev and test-challenge. Training contains 1,830 images and 268,627 instances. Validation
contains 593 images and 81,048 instances. We released the images and ground truths for training and validation sets. Test-dev contains 2,792 images and 353,346 instances. We released the images but not the ground truths. Test-challenge
contains 6,053 images and 1,090,637 instances. The images and ground truths of the test-challenge will be available only during the challenge.
<!-- </p> -->
<!-- <h2 style="text-align:left; margin-bottom:10px; margin-top:20px; ">
Image Source and Usage License
</h2>
<p style="text-align:justify;">
In consistent with DOTA-v.1.0, the images in DOTA-v1.5 are mainly collected from the Google Earth, satellite JL-1, and satellite
GF-2 of the China Centre for Resources Satellite Data and Application.
Use of the images from Google Earth must respect the corresponding terms of use:
<a href="https://www.google.com/permissions/geoguidelines.html">"Google Earth" terms of use</a>. All images and their associated annotations in DOTA-v1.5
<strong style="color:blue"></strong>can be used for academic purposes only, but any commercial use is prohibited</strong>.
</p>
<h2>
Object Category
</h2>
<p>
The object categories in DOTA-v1.5 include: plane, ship, storage tank, baseball diamond, tennis court,
basketball court, ground track field, harbor, bridge, small vehicle, large vehicle, helicopter, roundabout, soccer ball
field, swimming pool and container crane.
</p>
<h2>
Annotation format
</h2> -->
<!-- <p style="text-align:justify; padding-bottom: 0px; margin-bottom: 0px;"> -->
</r>
In the dataset, each instance's location is annotated by a quadrilateral bounding box, which can be denoted as
<strong style="color:blue"> "x
<sub>1</sub>, y
<sub>1</sub>, x
<sub>2</sub>, y
<sub>2</sub>, x
<sub>3</sub>, y
<sub>3</sub>, x
<sub>4</sub>, y
<sub>4</sub>" </strong> where (x
<sub>i</sub>, y
<sub>i</sub>) denotes the positions of the oriented bounding boxes' vertices in the image. The vertices are arranged in clockwise order. The following is the visualization of the adopted annotation method. The yellow point represents
the
<strong style="color:blue">starting point</strong>. Which refers to: (a) the top left corner of a plane, (b) the top left corner of a large vehicle diamond, (c) the center of sector-shaped baseball.
</p>
<p>
Except for the annotation of location, a category label is assigned for each instance, which comes from one of the above 15 selected categories, and meanwhile, a difficult label is provided which indicates whether the instance is difficult to be detected(1
for difficult, 0 for not difficult). Annotations for an image are saved in a text file with the same file name. In the first line, 'imagesource'(from GoogleEarth, GF-2 or JL-1) is given. In the second line, ’gsd’(ground sample distance,
the physical size of one image pixel, in meters) is given.
<strong style="color:blue">Note if the 'gsd' is missing, it is annotated to be 'null'.</strong> From the third line to the last line in the annotation text file, the annotation for each instance is given. The annotation format is:
</p>
<div class="alert alert-secondary" role="alert" style="font-size:18px;font-style: italic;font-family:'Times New Roman', Times, serif;
padding-top:10px; margin-top:10px;">
'imagesource':imagesource
<br> 'gsd':gsd
<br> x
<sub>1</sub>, y
<sub>1</sub>, x
<sub>2</sub>, y
<sub>2</sub>, x
<sub>3</sub>, y
<sub>3</sub>, x
<sub>4</sub>, y
<sub>4</sub>, category, difficult
<br> x
<sub>1</sub>, y
<sub>1</sub>, x
<sub>2</sub>, y
<sub>2</sub>, x
<sub>3</sub>, y
<sub>3</sub>, x
<sub>4</sub>, y
<sub>4</sub>, category, difficult
<br> ...
</div>
<div class="container">
<img src="images/annotation.png" class="img-rounded" alt="Cinque Terre" width="100%">
</div>
<!-- <h2>-->
<!-- Development kit-->
<!-- </h2>-->
<!-- <p>-->
<!-- We have provided <a href="https://github.com/CAPTAIN-WHU/DOTA_devkit">development kit</a>-->
<!-- that includes some useful functions such as visualizing data,-->
<!-- calculating mAP, splitting and merging data.-->
<!-- </p>-->
<!-- <h2>-->
<!-- Download-->
<!-- </h2>-->
<!-- <p style="text-align:justify; margin-bottom:10px; padding-bottom:0px;">-->
<!-- You can download DOTA-v2.0 from either Baidu Drive or Google Drive, according to your network connections. Make sure you-->
<!-- download the labelTxt of version 1.5.-->
<!-- </p>-->
<!-- <ul>-->
<!-- <li>-->
<!-- DOTA-v1.5 on Baidu Drive:-->
<!-- <a href="https://pan.baidu.com/s/1kWyRGaz">Training set</a>,-->
<!-- <a href="https://pan.baidu.com/s/1qZCoF72">Validation set</a>,-->
<!-- <a href="https://pan.baidu.com/s/1i6ly9Id">Testing images</a>-->
<!-- </li>-->
<!-- <li>-->
<!-- DOTA-v1.5 on Google Drive:-->
<!-- <a href="https://drive.google.com/drive/folders/1gmeE3D7R62UAtuIFOB9j2M5cUPTwtsxK?usp=sharing">Training set</a>,-->
<!-- <a href="https://drive.google.com/drive/folders/1n5w45suVOyaqY84hltJhIZdtVFD9B224?usp=sharing">Validation set</a>,-->
<!-- <a href="https://drive.google.com/drive/folders/1mYOf5USMGNcJRPcvRVJVV1uHEalG5RPl?usp=sharing">Testing images</a>-->
<!-- </li>-->
<!-- <br>-->
<!-- <br>-->
<!-- </ul>-->
<!-- <br>
<h2>
Development kit
</h2>
<p>
We have provided <a href="https://github.com/CAPTAIN-WHU/DOTA_devkit">development kit</a>
that includes some useful functions such as visualizing data,
calculating mAP, splitting and merging data.
</p>
<h2>
Download
</h2>
<p style="text-align:justify; margin-bottom:10px; padding-bottom:0px;">
You can download DOTA-v1.5 from either Baidu Drive or Google Drive, according to your network connections. Make sure you
download the labelTxt of version 1.5.
</p>
<ul>
<li>
DOTA-v1.5 on Baidu Drive:
<a href="https://pan.baidu.com/s/1kWyRGaz">Training set</a>,
<a href="https://pan.baidu.com/s/1qZCoF72">Validation set</a>,
<a href="https://pan.baidu.com/s/1i6ly9Id">Testing images</a>
</li>
<li>
DOTA-v1.5 on Google Drive:
<a href="https://drive.google.com/drive/folders/1gmeE3D7R62UAtuIFOB9j2M5cUPTwtsxK?usp=sharing">Training set</a>,
<a href="https://drive.google.com/drive/folders/1n5w45suVOyaqY84hltJhIZdtVFD9B224?usp=sharing">Validation set</a>,
<a href="https://drive.google.com/drive/folders/1mYOf5USMGNcJRPcvRVJVV1uHEalG5RPl?usp=sharing">Testing images</a>
</li>
<br> -->
<!-- <br>-->
<!-- </ul>-->
<!-- <h2 style="text-align:left; margin-bottom:10px; margin-top:20px; ">-->
<!-- Instance segmentation-->
<!-- </h2>-->
<!-- <p style="text-align:justify">-->
<!-- Instance segmentation track is based on <a href="https://captain-whu.github.io/iSAID/">iSAID</a>. Existing Earth Vision datasets are either suitable for semantic segmentation or object detection. iSAID is the first benchmark dataset-->
<!-- for instance segmentation in aerial images. This large-scale and densely annotated dataset contains 655,451 object instances for 15 categories across 2,806 high-resolution images. The distinctive characteristics of iSAID are the following:-->
<!-- (a) a large number of images with high spatial resolution, (b) fifteen important and commonly occurring categories, (c) a large number of instances per category, (d) large count of labeled instances per image, which might help in learning-->
<!-- contextual information, (e) huge object scale variation, containing small, medium and large objects, often within the same image, (f) Imbalanced and uneven distribution of objects with varying orientation within images, depicting real-life-->
<!-- aerial conditions, (g) several small size objects, with ambiguous appearance, can only be resolved with contextual reasoning, (h) precise instance-level annotations carried out by professional annotators, cross-checked and validated-->
<!-- by expert annotators complying with well-defined guidelines. The annotated examples are shown as follows:-->
<!-- </p>-->
<!-- <div class="container">-->
<!-- <img src="images/iSAID.jpg" class="img-rounded" alt="Cinque Terre" width="100%">-->
<!-- </div>-->
<h2 style="text-align:left; margin-bottom:10px; margin-top:20px; ">
Semantic Segmentation
</h2>
<p style="text-align:justify">
Semantic segmentation track is based on <a href="https://captain-whu.github.io/GID15/">GID15</a>.
GID15 is a new large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images.
This new dataset, which is named as Gaofen Image Dataset with 15 categories (GID-15),
has superiorities over the existing land-cover dataset because of its large coverage, wide distribution,
and high spatial resolution. The large-scale remote sensing semantic segmentation set contains 150 pixel-level annotated GF-2 images,
which is labeled in 15 categories. The annotated examples are shown as follows:
<!-- Semantic segmentation track is based on <a href="https://x-ytong.github.io/project/GID.html">GID</a>. GID is a large-scale land-cover dataset with Gaofen-2 (GF-2) satellite images. This new dataset, which is named as Gaofen Image Dataset-->
<!-- (GID), has superiorities over the existing land-cover dataset because of its large coverage, wide distribution, and high spatial resolution. GID consists of two parts: a large-scale classification set and a fine land-cover classification-->
<!-- set. The large-scale classification set contains 150 pixel-level annotated GF-2 images, and the fine classification set is composed of 30,000 multi-scale image patches coupled with 10 pixel-level annotated GF-2 images. The training-->
<!-- and validation data with 15 categories is collected and re-labeled based on the training and validation images with 5 categories, respectively. The annotated examples are shown as follows:-->
</p>
<div class="container">
<img src="images/GID.jpg" class="img-rounded" alt="Cinque Terre" width="100%">
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