Train and Evaluation Code, Road Classification Models and Test set of the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography"

This repository contains the Python scripts built for training and evaluation of the implementation, together with the test data and the resulting road segmentation models corresponding to the paper "Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography". The scripts make use of the Tensorflow with Keras framework and their 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 492 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 from Palencia (Spain) and features 18 million pixels labelled with the positive "Road" class. 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实现所编写的Python脚本，配套测试数据以及对应论文《航空正射影像道路地表提取语义分割（Semantic Segmentation）模型的图像重叠率与图像尺寸影响分析》（Insights into the Effects of Image Overlap and Image Size on Semantic Segmentation Models Trained for Road Surface Area Extraction from Aerial Orthophotography）的道路分割模型。本脚本基于搭载Keras的TensorFlow框架开发，并依赖其额外所需的相关库。训练与验证集基于二分类SROADEX数据集（https://zenodo.org/records/6482346）构建，该数据集被重新切分为图像块，覆盖本研究中涉及的图像分辨率（256×256、512×512及1024×1024像素）与图像重叠率（0%与12.5%）。本数据集通过Python脚本生成，该脚本依托开源库GDAL/OGR与MapScript实现矢量地图的栅格化，矢量地图涵盖城市、城郊及乡村三类道路的轴线信息。该二分类道路数据集包含来自西班牙本土与岛屿地区的16幅完整正射影像（28.5 km ×18.5 km），空间分辨率为0.5米/像素。由于该训练与验证集磁盘占用约492吉字节，仅可通过联系通讯作者获取。测试集生成自西班牙帕伦西亚（Palencia）的全新区域，包含1800万标记为"道路"正类的像素。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其他深度学习（Deep Learning）模型，并与本研究的结果进行对比。本仓库共享的信息结构如下：脚本按图像块分辨率（256、512及1024）进行分组。首先可找到测试集与评估脚本。针对每种图像块分辨率，均设有两个子文件夹，分别对应"无重叠"与"12.5%重叠"场景。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，以及压缩格式存储的已训练模型（H5格式）。此外，各分辨率下的测试数据集均包含两个文件夹。本仓库所有素材采用CC-BY 4.0协议进行分发。