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AID/OpenDataLab___AID/README.md

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+# 数据集介绍
+  ## 简介
+  AID是一个新的大规模航空图像数据集，通过从谷歌地球图像中收集样本图像。请注意，尽管Google Earth图像是使用原始光学航空图像的RGB效果图进行后处理的，但事实证明，即使在像素级土地使用/覆盖映射中，Google Earth图像与真实光学航空图像之间也没有显着差异。因此，Google Earth图像也可以用作航空图像来评估场景分类算法。新数据集由以下30种航空场景类型组成: 机场，裸地，棒球场，海滩，桥梁，中心，教堂，商业，密集住宅，沙漠，农田，森林，工业，草甸，中等住宅，山地，公园，停车场，游乐场，池塘，港口，火车站，度假村，河流，学校，稀疏住宅，广场，体育场，储罐和高架桥。所有的图像都由遥感图像解释领域的专家标记，每个类别的一些样本如图1所示。总之，AID数据集在30个类中具有多个10000图像。
+  ## 引文
+  ```
+ @article{simonyan2014very,
+ title={Very deep convolutional networks for large-scale image recognition},
+ author={Simonyan, Karen and Zisserman, Andrew},
+ journal={arXiv preprint arXiv:1409.1556},
+ year={2014}
+ }
+ ```
+  ‌​‌‌​​​​‌​​​‌‌‌‌‌​​‌‌​‌​‌​​‌​​​‌‌​‌‌‌​‌‌‌​​‌‌‌‌​‌​​​‌​‌‌‌​​‌‌‌‌​‌​‌‌​​‌‌‌​​‌‌‌‌​‌​​‌‌‌​‌

AID/OpenDataLab___AID/metafile.yaml

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+displayName: AID (Aerial Image Dataset)
+labelTypes:
+- Semantic_seg_map
+- Box2D
+license:
+- Unknown
+mediaTypes:
+- Image
+paperUrl: https://arxiv.org/pdf/1608.05167v1.pdf
+publishDate: "2017"
+publishUrl: https://captain-whu.github.io/AID/
+publisher:
+- Wuhan University
+- Huazhong University of Science and Technology
+tags:
+- Beach
+- Bridge
+taskTypes:
+- Semantic Segmentation
+- Object Detection
+‌​‌‌​​​​‌​​​‌‌‌‌‌​​‌‌​‌​‌​​‌​​​‌‌​‌‌‌​‌‌‌​​‌‌‌‌​‌​​​‌​‌‌‌​​‌‌‌‌​‌​‌‌​​‌‌‌​​‌‌‌‌​‌​​‌‌‌​‌

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