Doodleverse/Segmentation Zoo Res-UNet model for NOAA ERI/4-class segmentation of RGB 512x512 images

This Residual-UNet model is trained on 1,179 pairs of human-generated segmentation labels and images from Emergency Response Imagery (ERI) collected by US National Oceanic and Atmospheric Administration (NOAA) after Hurricane Barry, Delta, Dorian, Florence, Ida, Laura, Michael, Sally, Zeta, and Tropical Storm Gordon. The dataset is available here: https://doi.org/10.5281/zenodo.7268082 Models have been created using Segmentation Gym: Code - https://github.com/Doodleverse/segmentation_gym Paper - https://doi.org/10.1029/2022EA002332 The model takes input images that are 512 x 512 x 3 pixels, and the output is 512 x 512 x 4, corresponding to 4 classes: water bare sediment vegetation development (roads, buildings, power lines, parking lots, etc.) Included here are 6 files with the same root name: '.json' config file: this is the file that was used by Segmentation Gym to create the weights file. It contains instructions for how to make the model and the data it used, as well as instructions for how to use the model for prediction. '.h5' weights file: this is the file that was created by the Segmentation Gym function `train_model.py`. It contains the trained model's parameter weights. It can called by the Segmentation Gym function `seg_images_in_folder.py`. '_model_history.npz' model training history file: this numpy archive file contains numpy arrays describing the training and validation losses and metrics. It is created by the Segmentation Gym function `train_model.py` '.png' model training loss and mean IoU plot: this png file contains plots of training and validation losses and mean IoU scores during model training. A subset of data inside the .npz file. It is created by the Segmentation Gym function `train_model.py` '.zip' of the model in the Tensorflow ‘saved model’ format. It is created by the Segmentation Gym function `utils/gen_saved_model.py` '_modelcard.json' model card file: this is a json file containing fields that collectively describe the model origins, training choices, and dataset that the model is based upon. There is some redundancy between this file and the `config` file (described above) that contains the instructions for the model training and implementation. The model card file is not used by the program but is important metadata so it is important to keep with the other files that collectively make the model and is such is considered part of the model Additionally, BEST_MODEL.txt contains the name of the model with the best validation loss and mean IoU本残差U-Net（Residual-UNet）模型基于1179组人工标注分割标签与影像对进行训练，数据源自美国国家海洋和大气管理局（National Oceanic and Atmospheric Administration, NOAA）收集的应急响应影像（Emergency Response Imagery, ERI），涵盖飓风巴里（Hurricane Barry）、德尔塔（Delta）、多里安（Dorian）、佛罗伦斯（Florence）、艾达（Ida）、劳拉（Laura）、迈克尔（Michael）、萨利（Sally）、泽塔（Zeta）以及热带风暴戈登（Tropical Storm Gordon）灾后的相关影像。该数据集可通过以下链接获取：https://doi.org/10.5281/zenodo.7268082。本模型依托Segmentation Gym工具构建：代码仓库地址为https://github.com/Doodleverse/segmentation_gym，相关论文DOI为https://doi.org/10.1029/2022EA002332。 模型输入为512×512×3像素的影像，输出为512×512×4的分割结果，对应4类地物：水体、裸沉积物、植被、开发用地（道路、建筑、电力线路、停车场等）。 本次发布包含6个同名根文件，具体说明如下： 1. `.json`配置文件：该文件为Segmentation Gym生成权重文件时所使用的配置文件，包含模型构建规则、数据集使用说明以及模型预测操作指南。 2. `.h5`权重文件：该文件由Segmentation Gym的`train_model.py`脚本生成，存储了训练完成后的模型参数权重，可通过Segmentation Gym的`seg_images_in_folder.py`脚本调用。 3. `_model_history.npz`模型训练历史文件：该NumPy归档文件包含描述模型训练与验证损失、评估指标的NumPy数组，由`train_model.py`脚本生成。 4. `.png`模型训练损失与平均交并比（mean Intersection over Union, mIoU）曲线文件：该PNG文件可视化了模型训练过程中的训练与验证损失、平均交并比得分，为`.npz`文件中数据的子集，由`train_model.py`脚本生成。 5. `.zip` TensorFlow "saved model"格式的模型归档文件：该文件由Segmentation Gym的`utils/gen_saved_model.py`脚本生成。 6. `_modelcard.json`模型卡片文件：该JSON文件包含描述模型来源、训练设置以及基准数据集的相关字段，与上文提及的配置文件存在部分冗余，但前者为非程序调用的元数据文件，需与其他模型相关文件一同留存，属于模型组件的一部分。 此外，`BEST_MODEL.txt`文件存储了在验证集上取得最优损失与平均交并比的模型名称。