Availability of high-resolution optical imagery and advances in image processing technologies have significantly improved our ability to map landslides. In recent years Object-based image analysis (OBIA) has been gaining in popularity for landslide mapping due to its ability to incorporate spectral, textural, morphological and topographical properties. Many studies have been conducted based on commercial software. In this study, we create an open source Semi-Automatic Landslide Detection (SALaD) system utilizing OBIA and machine learning. Configured to run in Linux environment, it uses various open source Python packages and modules. This system was tested in 575 km² area along the Pasang Lhamu Highway, Nepal where large numbers of landslides were triggered by the 2015 Gorkha earthquake. Comparison with a manual inventory highlighted that this system was able to detect 70% of the landslide area. The speed and efficiency with which this system was able to detect landslides makes it a viable alternative to manual techniques for landslide mapping over large areas, when establishing approximate landslide locations is of prime importance.

高分辨率光学图像的可用性和图像处理技术的进步显着提高了我们绘制滑坡的能力。近年来，基于对象的图像分析（OBIA）因其具有结合光谱，纹理，形态和地形特性的能力而在滑坡测绘中获得了越来越多的关注。已经基于商业软件进行了许多研究。在这项研究中，我们利用OBIA和机器学习创建了一个开源的半自动滑坡检测（SALaD）系统。配置为在Linux环境中运行，它使用各种开源Python软件包和模块。该系统已在575 km ^2中进行了测试尼泊尔Pasang Lhamu公路沿线地区，2015年的Gorkha地震引发大量滑坡。与手动清点的比较表明，该系统能够检测到70％的滑坡面积。当建立近似的滑坡位置非常重要时，该系统能够检测滑坡的速度和效率使其成为替代手动技术在大面积上进行地形绘制的可行选择。