[Full Picture] Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection

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Evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection - CORE

Source: core.ac.uk

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Summary Analysis Research

Article summary:

1. This study evaluates the potential of machine learning methods, such as artificial neural networks (ANN), support vector machines (SVM) and random forest (RF), and different deep-learning convolution neural networks (CNNs) for landslide detection.

2. The accuracy assessment yields the best result of 78.26% mean intersection-over-union (mIOU) for a small window size CNN, which uses spectral information only.

3. CNNs do not automatically outperform ANN, SVM and RF, although this is sometimes claimed; rather, their performance depends on their design, i.e., layer depth, input window sizes and training strategies.

Article analysis:

This article provides an evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection in the Rasuwa district in Nepal. The authors use two training zones and one test zone to independently evaluate the performance of different methods through a mean intersection-over-union (mIOU) metric and other common metrics. The results show that the best result was achieved with a small window size CNN using spectral information only, with an mIOU of 78.26%.

The article is generally reliable in its reporting of the research findings; however, there are some points that could be improved upon to make it more trustworthy and reliable. For example, while the authors note that CNNs do not necessarily outperform ANNs, SVMs or RFs as is sometimes claimed, they do not provide any evidence to back up this claim or explore any counterarguments to it. Additionally, while they mention that the effects of augmentation strategies to artificially increase the number of existing samples are not well understood yet, they do not provide any further details on this point or discuss any potential risks associated with it. Furthermore, while they note that most researchers will either use predefined parameters in solutions like Google TensorFlow or apply different settings in a trial-and-error manner when using CNNs for landslide mapping, they do not provide any insights into how this could potentially lead to biased results or partiality in reporting due to lack of understanding of these parameters’ effects on classification accuracy.

In conclusion, while this article provides a thorough evaluation of different machine learning methods and deep-learning convolutional neural networks for landslide detection in Nepal’s Rasuwa district using two training zones and one test zone with various metrics including mIOU metric for accuracy assessment yielding an mIOU result of 78.26%, there are some points that could be improved upon to make it more trustworthy and reliable such as providing evidence for claims made about CNNs not necessarily outperforming ANNs etc., exploring counterarguments to these claims as well as discussing potential risks associated with augmentation strategies used to artificially increase sample numbers etc., providing insights into how predefined parameters used by researchers can lead to biased results etc., thus making it more comprehensive overall.

Topics for further research:

Effects of augmentation strategies on classification accuracy Risks associated with artificial sample increase Comparison of ANNs, SVMs and RFs for landslide detection Biased results due to predefined parameters in CNNs Counterarguments to CNNs outperforming ANNs Mean intersection-over-union (mIOU) metric for accuracy assessment