Landslides are considered to be among the most alarming natural hazards. Therefore, there is a growing demand for databases and inventories of these events worldwide, since they are a vital resource for landslide risk assessment applications. Given the recent advances in the field of image processing, the objective of this study is to evaluate the performance of a deep convolutional neural network architecture called U-Net for the mapping of landslide scars from satellite imagery. The question that drives the study is: can fully convolutional neural networks be successfully applied as the backbone of automatic frameworks for building landslide inventories, keeping or improving the identification accuracy and agility when compared to other methods? To seek for an answer to it, scenes from the Landsat-8 satellite of a region of Nepal were obtained and processed in order to compose a landslide image database that served as the basis for the training, validation and test of deep convolutional neural networks. The U-Net architecture was applied and the results indicate that it has the potential to identify landslide scars, improving over previously published research on the topic for the same study region. The validation process resulted in recall, precision and F1-score values of 0.74, 0.61 and 0.67, respectively, thus higher than those from previous studies using different methodologies. The results indicate the potential of the method to be applied in dynamic mapping systems for landslide scar identification, which paves the way to the composition and updating of landslide scar databases. These, in turn, can support a great deal of quantitative landslide susceptibility mapping methods that heavily rely on data to provide accurate results.

滑坡被认为是最令人震惊的自然灾害之一。因此，全世界对这些事件的数据库和清单的需求不断增长，因为它们是滑坡风险评估应用程序的重要资源。鉴于图像处理领域的最新进展，本研究的目的是评估一种称为U-Net的深度卷积神经网络体系结构的性能，该体系结构可用于绘制卫星图像中的滑坡疤痕。推动该研究的问题是：完全卷积神经网络能否成功地用作构建滑坡清单的自动框架的主干，与其他方法相比，可以保持或提高识别的准确性和敏捷性？为了寻求答案，获取并处理了来自尼泊尔某个地区的Landsat-8卫星的场景，以构成一个滑坡图像数据库，该数据库可作为深度卷积神经网络的训练，验证和测试的基础。应用了U-Net架构，结果表明它具有识别滑坡疤痕的潜力，比以前针对同一研究区域对该主题发表的研究有所改进。验证过程导致召回率，精确度和F1得分分别为0.74、0.61和0.67，因此高于使用不同方法的先前研究得出的值。结果表明该方法在滑坡疤痕动态映射系统中的应用潜力，为滑坡疤痕数据库的组成和更新铺平了道路。这些反过来，