Train and Evaluation Code, Road Classification Models and Test set of the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road classification models corresponding to the paper "Impact of Image Resolution and Image Overlap on the Prediction Performance of Convolutional Neural Networks Trained for Road Classification". The scripts make use of the Tensorflow with Keras framework and the additional required dependencies. The training and validation set is based on the binary SROADEX dataset (https://zenodo.org/records/6482346) that was re-split into tiles that feature the image resolutions (256 x 256, 512 x 512, and 1024 x 1024 pixels) and image overlaps (0% and 12.5%) considered in this study. The data have been generated using scripts developed in Python using Open Source libraries (GDAL/OGR and MapScript) for rasterization of vector cartography that represents the axes of the different types of roads (urban, interurban and rural). This binary road data contains information from 16 full orthoimages (28.5 km * 18.5 km) with spatial resolution of 0.5 m/pixel from the insular and peninsular Spanish territory. Due to the size on disk of approximately 546 gigabytes, this training and validation data is only available upon request from the corresponding author. The test set has been generated from a novel area of 28.5 km * 18.5 km and features binary road labels. The test sets are provided in the repository for each resolution (with no overlap), so that additional DL models can be evaluated on the same data and compared with the results achieved in this study. The structure of the information shared in this repository is as follows:The scripts have been grouped by tile resolution (256, 512 and 1024). First, the test set and the evaluation script can be found. For each tile resolution, there are two subfolders (corresponding to the "no overlap" and "12.5% overlap"). In each case, the Python scripts for training the models in the three repetitions are shared, and the trained models (H5 format) are shared in compressed form. Finally, for each resolution we also share the testing dataset which consists of two folders. The material is distributed under a CC-BY 4.0 license.本仓库包含为训练与评估该研究实现方案所编写的Python脚本，配套测试数据，以及对应于论文《图像分辨率与图像重叠度对用于道路分类的卷积神经网络（Convolutional Neural Networks）预测性能的影响》的训练所得道路分类模型。本脚本基于搭载Keras框架的TensorFlow开发，并依赖其他必要的第三方依赖库。 训练与验证集基于二元SROADEX数据集（https://zenodo.org/records/6482346）构建，该数据集已被切分为符合本研究设定的图像分辨率（256×256、512×512及1024×1024像素）与图像重叠度（0%与12.5%）的分块数据。本数据集通过Python编写的脚本生成，使用开源库GDAL/OGR与MapScript对矢量制图进行栅格化处理，矢量制图涵盖城市道路、城际道路与乡村道路三类不同道路的轴线信息。该二元道路数据集源自西班牙半岛与岛屿地区的16幅完整正射影像，单幅影像覆盖范围为28.5 km × 18.5 km，空间分辨率为0.5 m/像素。由于该数据集磁盘占用约546吉字节，训练与验证数据仅可通过联系通讯作者获取。测试集源自一块全新的28.5 km × 18.5 km区域，包含二元道路标签。本仓库提供了各分辨率下（无重叠）的测试集，以便研究者在相同数据上评估额外的深度学习模型，并与本研究所得结果进行对比。 本仓库共享的信息结构如下：脚本按图像分块分辨率（256、512与1024）进行分组。首先可找到测试集与评估脚本；针对每种分块分辨率，均包含两个子文件夹，分别对应“无重叠”与“12.5%重叠度”场景。在每个子文件夹中，均共享了用于三次重复训练模型的Python脚本，以及以压缩格式存储的已训练模型（H5格式）。最后，针对每种分辨率，我们还共享了由两个文件夹组成的测试数据集。 本材料采用知识共享署名4.0（CC-BY 4.0）许可协议进行分发。