Very high resolution aerial photography and annotated land cover data of the Peak District National Park

License Contents of compressed file (zip) from Van der Plas, Geikie, Alexander and Simms, upcoming publication titled <em>Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale</em> This data set contains the RGB image data and human land cover annotation for 1027 patches of 512 pixels x 512 pixels ( 64 m x 64 m spatial resolution). For more information on how the data can be used, the land cover schema and other details, please see our paper. For code examples of how to use the data, please see the github repository at https://github.com/pdnpa/cnn-land-cover The data is given in two formats: python and tiff. The Python format can be directly loaded by the code in the repository into Pytorch DataLoaders. The tiff format is independent of progamming language and application. This data is released under the CC BY 4.0 license, which means if you use this data set, we ask you to cite along with our paper above. If you use the RGB images, you must acknowledge the following copyright: "Aerial Photography for Great Britain, © Bluesky International Limited and Getmapping Plc [2022]" - README land cover patch data.txt - lc_label_names.json contains mapping from land cover label (integer) to land cover class name - python_format - images_python_all all (train and test) RGB images in .npy format (each of shape (3, 512, 512)) - masks_python_all all (train and test) land cover masks in .npy format (each of shape (512, 512)) - train_test_split_80tiles_2023-03-22-2131.json train/test split in json format - train_test_split_80tiles_2023-03-22-2131.pkl train/test split in pickle format (to be used with the data class in the repository) - tiff_format - images_masks_tiff_train train set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512)) - images_masks_tiff_test test set only patches, containing both the RGB image (first 3 bands) and the land cover annotation (4th band) (each of shape (4, 512, 512))

本ZIP压缩包的数据源自Van der Plas、Geikie、Alexander与Simms团队即将发表的论文《多阶段语义分割量化景观尺度下小型生境的破碎化程度》（英文原标题：*Multi-stage semantic segmentation quantifies fragmentation of small habitats at a landscape scale*）。
本数据集包含1027个空间分辨率为64米×64米的512像素×512像素斑块的RGB图像数据，以及人工标注的土地覆盖标签。
如需了解数据使用规范、土地覆盖分类体系及其他相关细节，请参阅本团队的上述论文；如需获取数据使用的代码示例，请访问GitHub仓库：https://github.com/pdnpa/cnn-land-cover。
本数据集提供两种存储格式：Python格式与TIFF格式。其中Python格式可直接通过仓库中的代码加载至PyTorch数据加载器（DataLoader）中；TIFF格式不依赖特定编程语言与应用场景，通用性更强。
本数据集采用知识共享署名4.0（CC BY 4.0）许可协议发布。若您使用本数据集，请务必引用上述提及的论文；若您使用其中的RGB图像，则必须注明以下版权声明："英国航空摄影 © Bluesky International Limited和Getmapping Plc [2022]"。
文件清单如下：
- README land cover patch data.txt：土地覆盖斑块数据说明文档
- lc_label_names.json：存储土地覆盖标签（整数形式）与土地覆盖类别名称的映射关系文件
- python_format：Python格式数据目录
- images_python_all：包含全部训练集与测试集的RGB图像，以.npy格式存储，单张图像的形状为(3, 512, 512)
- masks_python_all：包含全部训练集与测试集的土地覆盖掩码，以.npy格式存储，单张掩码的形状为(512, 512)
- train_test_split_80tiles_2023-03-22-2131.json：JSON格式的训练/测试集划分文件
- train_test_split_80tiles_2023-03-22-2131.pkl：Pickle格式的训练/测试集划分文件，需配合仓库中的数据类使用
- tiff_format：TIFF格式数据目录
- images_masks_tiff_train：仅包含训练集斑块的文件，同时存储RGB图像（前3个波段）与土地覆盖标注（第4个波段），单份数据的形状为(4, 512, 512)
- images_masks_tiff_test：仅包含测试集斑块的文件，同时存储RGB图像（前3个波段）与土地覆盖标注（第4个波段），单份数据的形状为(4, 512, 512)