The INRIA Aerial Image Labeling dataset is comprised of 360 RGB tiles of 5000×5000px with a spatial resolution of 30cm/px on 10 cities across the globe. Half of the cities are used for training and are associated to a public ground truth of building foo
数据集内容:城市建筑物检测的遥感图像数据集,标记只有建筑/非建筑两种,且是像素级别,用于语义分割。该数据集于 2017 年由 Inria 发布 数据集功能:语义分割 下载链接: 链接:https://pan.baidu.com/s/1G_HyEFjv6lq1hbT8JaZhPA 提取码:0wa8 彩蛋1:
In this paper, we propose an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Moreover, the cities included in the test set are different from those of the training set. We also experiment with convolutional...
> the image tiles tendtobe self-similar and with uniformcolorhistograms 所以,提出一个开放的数据集合: Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m Ground truth data for two ...
In this paper, we propose an aerial image labeling dataset that covers a wide range of urban settlement appearances, from different geographic locations. Moreover, the cities included in the test set are different from those of the training set. We also experiment with convolutional neural ...
Paper: A Spatial-Temporal Attention-Based Method and a New Dataset for Remote Sensing Image Change Detection Download: Link WHU-CD Paper: Fully convolutional networks for multisource building extraction from an open aerial and satellite imagery data set Download: Link S2Looking Paper: S2Looking: ...
Building Extraction from remote sensing image using Vision Transformer, IEEE Transactions on Geoscience and Remote Sensing, 2022 - BuildFormer/inria_patch_split.py at main · WangLibo1995/BuildFormer
> the image tiles tendtobe self-similar and with uniformcolorhistograms 所以,提出一个开放的数据集合: Dataset features: Coverage of 810 km² (405 km² for training and 405 km² for testing) Aerial orthorectified color imagery with a spatial resolution of 0.3 m ...
AerialImageDataset(2 directories) get_app fullscreen chevron_right folder test 1 directories folder train 2 directories lightbulb See what others are saying about this dataset What have you used this dataset for? Learning 0Research 0Application 0LLM Fine-Tuning 0 ...
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