用U-Net进行滑坡分割：评估不同的采样方法和斑块大小
Landslide Segmentation with U-Net: Evaluating Different Sampling Methods and Patch Sizes
摘要：
滑坡目录图是验证预测滑坡模型的关键;然而，由于大多数制图方法依赖于视觉解释或专家知识，详细的目录图仍然缺乏。这项研究使用了一种名为U-net的完全卷积深度学习模型，自动分割位于巴西东南部里约热内卢山脉的Nova Friburgo市的山体滑坡。其目的是评估斑块大小、采样方法和数据集对模型整体准确性的影响。训练数据使用RapidEye卫星的光学信息和ALOS卫星L波段传感器产生的数字高程模型。使用随机和规则网格方法对数据进行采样，并以三种尺寸（32 x32，64 x64和128 x128像素）进行修补。这些模型在两个方面进行了评估，包括精确度，召回率，f1分数和平均交集（mIoU）指标。结果表明，使用32 x32瓦片训练的模型由于更高的真阳性率而往往具有更高的召回值;然而，它们将更多的背景区域错误分类为滑坡（误报）。使用128 x128 tiles训练的模型通常可以获得更高的精度值，因为它们产生的假阳性错误更少。在这两个测试领域，DEM和增强提高了模型的准确性。随机抽样有助于模型的推广。使用RapidEye图像，DEM信息和增强数据的128 x128随机瓦片训练的模型获得了最高的f1分数，在测试区域1中为0.55，在测试区域2中为0.58。本研究中取得的结果与文献中发现的其他完全卷积模型相当，增加了该领域的知识。
英文摘要：
Landslide inventory maps are crucial to validate predictive landslide models; however, since most mapping methods rely on visual interpretation or expert knowledge, detailed inventory maps are still lacking. This study used a fully convolutional deep learning model named U-net to automatically segment landslides in the city of Nova Friburgo, located in the mountainous range of Rio de Janeiro, southeastern Brazil. The objective was to evaluate the impact of patch sizes, sampling methods, and datasets on the overall accuracy of the models. The training data used the optical information from RapidEye satellite, and a digital elevation model (DEM) derived from the L-band sensor of the ALOS satellite. The data was sampled using random and regular grid methods and patched in three sizes (32x32, 64x64, and 128x128 pixels). The models were evaluated on two areas with precision, recall, f1-score, and mean intersect over union (mIoU) metrics. The results show that the models trained with 32x32 tiles tend to have higher recall values due to higher true positive rates; however, they misclassify more background areas as landslides (false positives). Models trained with 128x128 tiles usually achieve higher precision values because they make less false positive errors. In both test areas, DEM and augmentation increased the accuracy of the models. Random sampling helped in model generalization. Models trained with 128x128 random tiles from the data that used the RapidEye image, DEM information, and augmentation achieved the highest f1-score, 0.55 in test area one, and 0.58 in test area two. The results achieved in this study are comparable to other fully convolutional models found in the literature, increasing the knowledge in the area.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
