Abstract
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.
Highlights
The science of natural hazards, including landslides, has lately been positively impacted by the quick growth of remote sensing and crowdsourced platforms such as satellites, UAVs, social networks, sensor networks and public online data storages
When dealing in particular with image classification and object recognition, the highest performances, at the present state of the art, are those provided by deep learning tools, such as convolutional neural networks (CNNs), that are capable of performing classification tasks directly from images rather than by using pre-selected features of them (Krizhevsky et al 2012; He et al 2015a; Shin et al 2016)
0.08 trained CNNs on single images was of 0.025 s for both Go-LanDLC and GP-LanDLC, 0.030 s for In-LanDLC and 0.105 s for ReLanDLC, within Matlab
Summary
The science of natural hazards, including landslides, has lately been positively impacted by the quick growth of remote sensing and crowdsourced platforms such as satellites, UAVs, social networks, sensor networks and public online data storages. An even stronger increase has been observed in the availability of crowdsourced information generated by data mining web resources of various type, with a special relevance of geo-tagged unclassified and potentially useful images. This has generated an exponential surge in the amount of available data that contain large quantities of noisy and nonvalidated information. To be usable, such big data require automation and the support of machine learning methods for selection, classification and storage (Catani et al 2013; Smith et al 2017; Intrieri et al 2017; Du et al 2019).
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