LandCoverPT Dataset

LandCoverPT - A dataset for training Machine Learning models to classify the Portuguese territory land cover. The creation of the dataset used 26 Sentinel-2 products, captured in June and August 2019, and the products where divided into 153347 patches with 120×120 pixels each. The products cover the Portuguese mainland. The Sentinel-2 data was complemented with Corine Land Cover 2018 data. Each patch includes B01, B02, B03, B04, B05, B06, B07, B08, B8A, B09, B11, and B12 Sentinel-2 bands, and the CLC 2018 layer. The dataset is stored in 3 TFRecord files, for training, validation, and test. The training set contains 98141 patches, the validation set includes 24536 patches, and the test set contains 30670 patches. It is also provided code files to experiment with the dataset: - two configuration files in JSON format - a python file with the U-Net model - a python file with a class to create a tf.data.Dataset based on the TFRecord files - a notebook for training the U-Net model on the LandCoverPT dataset - a notebook for evaluating the U-Net model trained on the LandCoverPT dataset - a notebook to make predictions with the U-Net model trained on the LandCoverPT dataset.LandCoverPT数据集——一款用于训练机器学习模型以分类葡萄牙国土土地覆盖类型的数据集。该数据集的构建采用了26景2019年6月与8月采集的哨兵二号（Sentinel-2）影像产品，将这些影像划分为153347幅尺寸为120×120像素的图像块，覆盖葡萄牙本土全域。数据集以哨兵二号影像数据为基础，并辅以科里内土地覆盖2018（Corine Land Cover 2018）数据进行补充完善。每幅图像块均包含哨兵二号影像的B01、B02、B03、B04、B05、B06、B07、B08、B8A、B09、B11及B12共12个波段，以及科里内土地覆盖2018层数据。该数据集以3个TFRecord文件存储，分别对应训练集、验证集与测试集。其中训练集包含98141幅图像块，验证集包含24536幅，测试集则包含30670幅。同时还提供了用于该数据集实验的配套代码文件： - 两份JSON格式的配置文件 - 一个包含U-Net模型的Python脚本 - 一个用于基于TFRecord文件构建tf.data.Dataset数据集类的Python脚本 - 一份用于在LandCoverPT数据集上训练U-Net模型的Jupyter Notebook - 一份用于评估基于LandCoverPT数据集训练得到的U-Net模型的Jupyter Notebook - 一份用于使用基于LandCoverPT数据集训练得到的U-Net模型进行预测的Jupyter Notebook。