Landslide susceptibility prediction based on image semantic segmentation

Du, Bowen; Zhao, Zirong; Hu, Xiao; Wu, Guanghui; Han, Liangzhe; Sun, Leilei; Gao, Qiang

doi:10.1016/j.cageo.2021.104860

Cited by 46 publications

(22 citation statements)

References 30 publications

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“…The complete dataset consists of 256 × 256 pixels images and their corresponding labels, which are raster data in png format with a bit depth of 24. The dataset was enhanced by rotating 90° and 180° to avoid over‐fitting and improve robustness during model training (Du, Zhao, Xiao, et al, 2021; Liu et al, 2021). Visual interpretation of abandoned terraces depends on numerous field investigations, reconnaissance, and image analysis.…”

Section: Methodsmentioning

confidence: 99%

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

et al. 2023

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Terraces are an important cultivated land resource. Terrace abandonment affects the soil quality, soil and water conservation benefits, and biodiversity of terraces. Therefore, it is important to quantify the number and spatial distribution of abandoned terraces to protect cultivated land and food security. However, the traditional remote sensing method cannot identify small plots and make accurate assessment of abandoned farmland quickly in mountainous areas. To accurately identifying abandoned terraces, this study used semantic segmentation based on deep learning and change detection to identify abandoned terraces and their spatial distribution in a small watershed on the Loess Plateau in 2021. A comparative analysis of the accuracy of three deep learning models revealed that RefineNet is superior to DeepLabv3+ and DeepLabv3 for identifying abandoned terraces. The user's accuracy, producer's accuracy, overall accuracy, and appa values for RefineNet were 0.817, 0.894, 0.800, and 0.539, respectively. For change detection, the corresponding values were 0.821, 0.753, 0.731, and 0.426, respectively. In addition, semantic segmentation produced better recognition results than change detection in complex terrain and geomorphological areas. The abandoned terraces in the study area were mainly distributed in mountainous areas far from residential areas and more likely at high elevations with large slopes. This study provides a new method for recognizing abandoned terraces and spatial distribution information for managing and utilizing abandoned terraces.

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Section: Methodsmentioning

confidence: 99%

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

et al. 2023

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“…Semantic segmentation of remote sensing images aims to produce pixel-wise categorical labels to facilitate interpretation of the remote sensing data [1], [2], [3]. The semantically parsed annotation enables an intuitive perception of targets, therefore, has been widely adopted in downstream tasks, such as land-cover mapping [4], [5], water resources management [6], [7] and disaster assessment [8], [9], among others.…”

Section: Introductionmentioning

confidence: 99%

A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images

Liu

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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In remotely sensed images, high intra-class variance and inter-class similarity are ubiquitous due to complex scenes and objects with multivariate features, making semantic segmentation a challenging task. Deep convolutional neural networks can solve this problem by modelling the context of features and improving their discriminability. However, current learning paradigms model the feature affinity in spatial dimension and channel dimension separately and then fuse them in a sequential or parallel manner, leading to suboptimal performance. In this study, we first analyze this problem practically and summarize it as attention bias that reduces the capability of network in distinguishing weak and discretely distributed objects from widerange objects with internal connectivity, when modeled only in spatial or channel domain. To jointly model both spatial and channel affinity, we design a synergistic attention module (SAM), which allows for channel-wise affinity extraction while preserving spatial details. In addition, we propose a synergistic attention perception neural network (SAPNet) for the semantic segmentation of remote sensing images. The hierarchicalembedded synergistic attention perception module aggregates SAM-refined features and decoded features. As a result, SAPNet enriches inference clues with desired spatial and channel details. Experiments on three benchmark datasets show that SAPNet is competitive in accuracy and adaptability compared with stateof-the-art methods. The experiments also validate the hypothesis of attention bias and the efficiency of SAM.

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“…Landslide geological disasters are a serious type of geological disaster that occur worldwide, inducing serious threats and losses to the development of human society. In recent years, under the influence of extreme global climate change, seismic activities, coupled with the rapid development of human engineering activities, have become more intense interferences to the natural environment, directly leading to geological disasters with greater intensity and higher frequency [1,2]. This increases the difficulty of developing landslide disaster reduction strategies [3].…”

Section: Introductionmentioning

confidence: 99%

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

Lin

Liang

et al. 2022

Mathematics

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In landslide displacement prediction, random factors that would affect the performance of prediction are usually ignored by using a time series analysis method. In order to solve this problem, in this paper, a landslide displacement prediction model, the local mean decomposition-bidirectional long short-term memory (LMD-BiLSTM), is proposed based on the time-frequency analysis method. The model uses the local mean decomposition (LMD) algorithm to decompose landslide displacement and obtains several subsequences of landslide displacement with different frequencies. This paper analyzes the internal relationship between the landslide displacement and rainfall, reservoir water level, and landslide state. The maximum information coefficient (MIC) algorithm is used to calculate the intrinsic correlation between each subsequence of landslide displacement and rainfall, reservoir water level, and landslide state. Subsequences of influential factors with high correlation are selected as input variables of the bidirectional long short-term memory (BiLSTM) model to predict each subsequence. Finally, the predicted results of each of the subsequences are added to obtain the final predicted displacement. The proposed LMD-BiLSTM model effectiveness is verified based on the Baishuihe landslide. The prediction results and evaluation indexes show that the model can accurately predict landslide displacement.

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Landslide susceptibility prediction based on image semantic segmentation

Cited by 46 publications

References 30 publications

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

Abandoned terrace recognition based on deep learning and change detection on the Loess Plateau in China

A Synergistical Attention Model for Semantic Segmentation of Remote Sensing Images

Landslide Displacement Prediction Based on Time-Frequency Analysis and LMD-BiLSTM Model

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