916 research outputs found
Rapid Mapping of Landslides in the Western Ghats (India) Triggered by 2018 Extreme Monsoon Rainfall Using a Deep Learning Approach
Rainfall-induced landslide inventories can be compiled using remote sensing and topographical data, gathered using either traditional or semi-automatic supervised methods. In this study, we used the PlanetScope imagery and deep learning convolution neural networks (CNNs) to map the 2018 rainfall-induced landslides in the Kodagu district of Karnataka state in theWestern Ghats of India.We used a fourfold cross-validation (CV) to select the training and testing data to remove any random results of the model. Topographic slope data was used as auxiliary information to increase the performance of the model. The resulting landslide inventory map, created using the slope data with the spectral information, reduces the false positives, which helps to distinguish the landslide areas from other similar features such as barren lands and riverbeds. However, while including the slope data did not increase the true positives, the overall accuracy was higher compared to using only spectral information to train the model. The mean accuracies of correctly classified landslide values were 65.5% when using only optical data, which increased to 78% with the use of slope data. The methodology presented in this research can be applied in other landslide-prone regions, and the results can be used to support hazard mitigation in landslide-prone regions
DFPENet-geology: A Deep Learning Framework for High Precision Recognition and Segmentation of Co-seismic Landslides
The following lists two main reasons for withdrawal for the public. 1. There
are some problems in the method and results, and there is a lot of room for
improvement. In terms of method, "Pre-trained Datasets (PD)" represents
selecting a small amount from the online test set, which easily causes the
model to overfit the online test set and could not obtain robust performance.
More importantly, the proposed DFPENet has a high redundancy by combining the
Attention Gate Mechanism and Gate Convolution Networks, and we need to revisit
the section of geological feature fusion, in terms of results, we need to
further improve and refine. 2. arXiv is an open-access repository of electronic
preprints without peer reviews. However, for our own research, we need experts
to provide comments on my work whether negative or positive. I then would use
their comments to significantly improve this manuscript. Therefore, we finally
decided to withdraw this manuscript in arXiv, and we will update to arXiv with
the final accepted manuscript to facilitate more researchers to use our
proposed comprehensive and general scheme to recognize and segment seismic
landslides more efficiently.Comment: 1. There are some problems in the method and results, and there is a
lot of room for improvement. Overall, the proposed DFPENet has a high
redundancy by combining the Attention Gate Mechanism and Gate Convolution
Networks, and we need to further improve and refine the results. 2. For our
own research, we need experts to provide comments on my work whether negative
or positiv
AN OVERVIEW OF GEOINFORMATICS STATE-OF-THE-ART TECHNIQUES FOR LANDSLIDE MONITORING AND MAPPING
Abstract. Natural hazards such as landslides, whether they are driven by meteorologic or seismic processes, are constantly shaping Earth's surface. In large percentage of the slope failures, they are also causing huge human and economic losses. As the problem is complex in its nature, proper mitigation and prevention strategies are not straightforward to implement. One important step in the correct direction is the integration of different fields; as such, in this work, we are providing a general overview of approaches and techniques which are adopted and integrated for landslide monitoring and mapping, as both activities are important in the risk prevention strategies. Detailed landslide inventory is important for providing the correct information of the phenomena suitable for further modelling, analysing and implementing suitable mitigation measures. On the other hand, timely monitoring of active landslides could provide priceless insights which can be sufficient for reducing damages. Therefore, in this work popular methods are discussed that use remotely-sensed datasets with a particular focus on the implementation of machine learning into landslide detection, susceptibility modelling and its implementation in early-warning systems. Moreover, it is reviewed how Citizen Science is adopted by scholars for providing valuable landslide-specific information, as well as couple of well-known platforms for Volunteered Geographic Information which have the potential to contribute and be used also in the landslide studies. In addition to proving an overview of the most popular techniques, this paper aims to highlight the importance of implementing interdisciplinary approaches
Automating global landslide detection with heterogeneous ensemble deep-learning classification
With changing climatic conditions, we are already seeing an increase in
extreme weather events and their secondary consequences, including landslides.
Landslides threaten infrastructure, including roads, railways, buildings, and
human life. Hazard-based spatial planning and early warning systems are
cost-effective strategies to reduce the risk to society from landslides.
However, these both rely on data from previous landslide events, which is often
scarce. Many deep learning (DL) models have recently been applied for landside
mapping using medium- to high-resolution satellite images as input. However,
they often suffer from sensitivity problems, overfitting, and low mapping
accuracy. This study addresses some of these limitations by using a diverse
global landslide dataset, using different segmentation models, such as Unet,
Linknet, PSP-Net, PAN, and DeepLab and based on their performances, building an
ensemble model. The ensemble model achieved the highest F1-score (0.69) when
combining both Sentinel-1 and Sentinel-2 bands, with the highest average
improvement of 6.87 % when the ensemble size was 20. On the other hand,
Sentinel-2 bands only performed very well, with an F1 score of 0.61 when the
ensemble size is 20 with an improvement of 14.59 % when the ensemble size is
20. This result shows considerable potential in building a robust and reliable
monitoring system based on changes in vegetation index dNDVI only.Comment: Author 1 and Author 2 contributed equally to this wor
Landslide detection by deep learning of non-nadiral and crowdsourced optical images
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
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