The recent development of mobile surveying platforms and crowdsourced geoinformation has produced a huge amount of non-validated data that are now available for research and application. In the field of risk analysis, with particular reference to landslide hazard, images generated by autonomous platforms (such as UAVs, ground-based acquisition systems, satellite sensors) and pictures obtained from web data mining are easily gathered and contribute to the fast surge in the amount of non-organized information that may engulf data storage facilities. Therefore, the high potential impact of such methods is severely reduced by the need of a massive amount of human intelligence tasks (HITs), which is necessary to filter and classify the data, whatever the final purpose. In this work, we present a new set of convolutional neural networks (CNNs) specifically designed for the automated recognition of landslides and mass movements in non-standard pictures that can be used in automated image classification, in supporting UAV autonomous guidance and in the filtering of data-mined information. Computer vision can be of great help in fostering the autonomous capability of intelligent systems to complement, or completely substitute, HITs. Image and object recognition are at the forefront of this research field. The deep learning procedure has been accomplished by applying transfer learning to some of the top-performer CNNs available in the literature. Results show that the deep learning machines, calibrated on a relevant dataset of validated images of landforms, may supply reliable predictions with computational time and resource requirements compatible with most of the UAV platforms and web data mining applications in landslide hazard studies. Average accuracy achieved by the proposed methods ranges between 87 and 90% and is consistently higher than that obtained by general-purpose state-of-the-art image recognition convolutional neural networks. The method can be applied to early warning, vulnerability assessment, residual risk estimation, model parameterisation and landslide mapping. Specific advantages will be the reduction of the present limitations in the intelligent guidance of landslide mapping drones, the classification of fake news, the validation of post-disaster information and the correct interpretation of an impending change in the environment.

中文翻译：

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通过非天底和众包光学图像的深度学习进行滑坡检测

最近移动测量平台和众包地理信息的发展产生了大量未经验证的数据，这些数据现在可用于研究和应用。在风险分析领域，特别是滑坡灾害，自主平台（如无人机、地基采集系统、卫星传感器）生成的图像和网络数据挖掘获得的图片很容易收集，有助于快速增长可能吞没数据存储设施的无组织信息量。因此，由于需要大量的人类智能任务 (HIT)，无论最终目的是什么，都需要对数据进行过滤和分类，从而大大降低了此类方法的潜在影响。在这项工作中，我们提出了一组新的卷积神经网络 (CNN)，专门设计用于自动识别非标准图片中的滑坡和群众运动，可用于自动图像分类、支持无人机自主引导和过滤数据挖掘信息。计算机视觉可以极大地帮助培养智能系统的自主能力，以补充或完全替代 HIT。图像和物体识别处于该研究领域的前沿。深度学习过程是通过将迁移学习应用于文献中可用的一些表现最佳的 CNN 来完成的。结果表明，在经过验证的地形图像的相关数据集上校准的深度学习机器，可以提供可靠的预测，其计算时间和资源要求与滑坡灾害研究中的大多数无人机平台和网络数据挖掘应用程序兼容。所提出的方法实现的平均准确率在 87% 到 90% 之间，并且始终高于通用最先进的图像识别卷积神经网络所获得的准确率。该方法可应用于早期预警、脆弱性评估、剩余风险估计、模型参数化和滑坡绘图。具体优势将是减少目前在滑坡测绘无人机智能引导、假新闻分类、灾后信息验证以及对即将发生的环境变化的正确解释方面的局限性。