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<!DOCTYPE html>
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<title>遥感图像智能解译技术挑战赛</title>
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<h2 style="text-align:center; margin-top:60px; font-weight: bold;">
The 1st Workshop on
</h2>
<h1 style="text-align:center; font-weight: bold; font-size: 38px;color:#FF9900">
Learning to Understand Aerial Images
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<h2 style="text-align:center; font-weight: bold; font-style: italic">
in conjunction with IEEE ICCV 2021</span>
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October 11, 2021, Montreal, Canada.
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The 1st Workshop on
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<h1 style="text-align:center; margin-top:1.7em; font-weight: bold ; color:#FF9900">
遥感图像智能解译技术挑战赛
</h1>
<h3 style="text-align:center; font-weight: bold; font-style:normal">
第四届中国模式识别与计算机视觉大会
</h3>
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2021-10-29 ~ 2021-11-1 北京国际会议中心
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<h2 style="text-align:left; margin-bottom:10px; margin-top:20px; ">
遥感图像建筑物变化检测
</h2><br>
<h3>数据规模
</h3>
<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. -->
<!-- 语义变化检测竞赛数据来源于SECOND数据集。为确保数据的多样性，SECOND数据集从多个传感器平台收集了4662对尺寸为512 x 512的航空图像（PNG格式）。这些图像对分布在杭州，成都和上海等城市，并由专业的遥感数据专家小组进行像素级的地物类别标注（PNG格式）以确保标注的准确性。SECOND数据集主要包含涉及非植被地表，树木，低矮植被，水体，建筑物和游乐场等6类地物的30种土地覆盖变化类型（包括非变化）。值得注意的是，非植被地表（简称n.v.g. surface）主要包含不透水地表和荒地。通过对图像数据的随机采样，SECOND反映了土地覆盖类别变化类型的真实分布。本竞赛语义变化检测数据集部分样本及标注数据如图所示: -->
本次遥感图像建筑物变化检测竞赛数据来源于航天宏图信息技术股份有限公司提供的全国主要一线城市的多时相遥感影像以及SECOND数据集。
全部图像数据共10000对，尺寸为512 x 512，主要分布于北京、上海、广州以及杭州等城市。
为保证数据标注的准确性，变化标签由专家小组进行标注与整理（PNG格式）。
其中用于比赛的数据统计如下（<font color="blue">登录后可以下载数据：</font><a href="http://47.108.71.49:9005/login/">http://47.108.71.49:9005/login/</a>）：
</br>
<li>训练集：6000对图像<font color="blue">（已发布）</font></li>
<li>验证集：2000对图像<font color="blue">（已发布）</font></li>
<li> 测试样本：2000对图像<font color="blue">（已发布）</font></li>
</p>
<h3>标注样例
</h3>
<p>
本竞赛数据集主要包含涉及建筑物的地物变化，通过对图像数据的随机采样，竞赛数据集反映了城市建筑物变化的真实分布。本竞赛建筑物变化检测数据集部分样本及标注数据如下图所示。
<div class="container">
<img src="images/SECOND.png" class="img-rounded" alt="Cinque Terre" width="100%">
</div>
</p>
<!-- <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://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>
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<img src="images/GID.jpg" class="img-rounded" alt="Cinque Terre" width="100%">
</div>
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