Landslide Detection Using Densely Connected Convolutional Networks and Environmental Conditions

Abstract

A complete and accurate landslide map is necessary for landslide susceptibility and risk assessment. Currently, deep learning faces the dilemma of insufficient application, scarce samples, and poor efficiency in landslide recognition. This study utilizes the advantages of dense convolutional networks (DenseNets) and their modified technique to solve the three proposed problems. For this purpose, we created a new landslide sample library. On the original remote sensing image, 12 geological, topographic, hydrological and land cover factors that can directly or indirectly reflect the landslide are superimposed. Then, landslide detection was carried out in the Three Gorges Reservoir area in China to test the performance of the improved method. The quantitative evaluation of the landslide detection map shows that the combination of environmental factors and DenseNet can improve the accuracy of the detection model. Compared with the optical image, kappa and F1 increased by 9.7% and 9.1% respectively. Compared with other traditional neural networks and machine learning algorithms, DenseNet has the highest kappa and F1 values. Based on the base Densenet, through data augmentation and fine-tuning optimization technology, the kappa and F1 values reach the highest values of 0.9474 and 0.9505, respectively. The proposed method has promising applicability in large area landslide identification scenarios.

A novel Dynahead-Yolo neural network for the detection of landslides with variable proportions using remote sensing images

“…A recent development of the U-Net model for landslide detection could be referred to Fu et al (2022). Cai et al (2021) introduced dense convolutional networks into landslide detection, and significantly improved the detection ability of the model through measures such as feature reuse and feature enhancement. It is challenging to detect landslides in complex backgrounds based on CNN, but there are some excellent studies that have achieved such goals.…”

Susceptibility-Guided Landslide Detection Using Fully Convolutional Neural Network

“…With advances in computer hardware and pattern recognition techniques, the accuracy of computer vision algorithms gradually approaches visual interpretation in various domains, which also benefits landslide detection. Remote sensing-based landslide detection is now moving from visual interpretation toward automatic interpretation [16], among which machine learning (ML)-based methods are the most representative. By extracting and utilizing low-level features of remote sensing images, various traditional ML models, such as maximum likelihood [17], support vector machine (SVM) [18, 2 JSTARS-2022-01167 19], and random forest (RF) [20], have raised the efficiency of landslide detection with acceptable accuracy.…”

“…Introducing auxiliary information is a promising strategy to improve landslides' detection accuracy further. Some researchers [16,42,[46][47][48][49][50] fed remote sensing images and auxiliary information such as elevation, aspect, land cover, and normalized difference vegetation index (NDVI) into CNN. It allows CNN to explore richer high-level features, especially the connection between landslide occurrence and surrounding environmental conditions, achieving higher detection accuracy.…”

A Siamese Network Based U-Net for Change Detection in High Resolution Remote Sensing Images

“…The emergence of deep learning (DL) has brought in a new direction to the research on RS, and associated research has been carried out in numerous fields, such as ecological evaluation [21], [22], RS segmentation [23], landslide detection [24], [25], image classification [26]- [28], etc. In the field of CD, researchers exploit DL to alleviate the problems of complex feature detection, strong noise interference, poor separability, and low automation in RSICD.…”