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Machine Learning and Remote Sensing for Geohazards

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (15 August 2023) | Viewed by 21161

Special Issue Editors


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Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4, 50121 Florence, Italy
Interests: landslides; engineering geology; monitoring; civil engineering; remote sensing; natural hazards; InSAR; satellite-based monitoring; GIS; subsidence; modelling of environmental processes
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Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4-50121 Firenze, Italy
Interests: landslide; subsidence; risk analysis; monitoring; InSAR
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth Sciences, University of Florence, Via La Pira, 4 - 50121 Firenze, Italy
Interests: landslide mapping and monitoring; land subsidence; remote sensing data interpretation; geohazard monitoring; EO techniques
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth and Sea Sciences, University of Palermo, Via Archirafi 22, 90123 Palermo, Italy
Interests: landslide modeling; geomorphology; mapping; machine learning; slope stability assessment; GIS

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Guest Editor
German Remote Sensing Data Center (DFD) - Geo-Risks and Civil Security, German Aerospace Center (DLR), 82234 Weßling, Germany
Interests: remote sensing of natural hazards (volcanoes, earthquakes, landslides, floods, fires); SAR polarimetry; SAR interferometry; thermal remote sensing; satellite-based monitoring of volcanoes
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Earth, Environmental and Life Sciences, University of Genoa, 16132 Genoa, Italy
Interests: machine Learning modeling; Landslides; multi-spectral imageries; natural hazards; InSAR analyses

Special Issue Information

Dear Colleagues,

Geohazards, or geological hazards, can be defined as “events caused by geological, geomorphological, and climatic conditions or processes that represent serious threats to human lives, property, and the natural and built environment”. According to the Emergency Events Database (https://public.emdat.be/), in 2021, about 250 geohazards occurred, claiming the lives of more than 150 people and affecting almost 20 million people in total. The detection and mapping of geological hazards are paramount activities for land management and risk reduction policies worldwide. Remote sensing technologies can be helpful due to their high spatial and temporal coverage, allowing relevant information to be obtained worldwide for the investigation, characterization, monitoring and modeling of geohazards. Together with remote sensing, Artificial Intelligence or machine learning represents a significant innovation for the analysis of geohazards. Such kinds of approaches have widely demonstrated their suitability in many scientific fields, being characterized by high accuracy and specific advantages in different study areas, and for different sets of factors. Machine learning is increasingly implemented on remotely sensed data, providing support to the processing of datasets; for the classification of imagery; or for the modeling of hazards, susceptibility or risk. This Special Issue of Remote Sensing invites papers that apply machine learning techniques to remotely sensed data to address challenges around geohazards. Topics of interest include, but are not limited to, the following:

  • Application of remotely sensed data to physical- and statistical-based hazard and risk models;
  • Processing of remote sensing data with machine learning algorithms;
  • Machine learning classification of remote sensing data;
  • Processing of RS time-series;
  • Machine learning for the mapping and/or monitoring of geohazards;
  • Landslide or subsidence analysis.

Dr. Pierluigi Confuorto
Dr. Federico Raspini
Dr. Matteo Del Soldato
Dr. Chiara Cappadonia
Dr. Simon Plank
Dr. Mariano Di Napoli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • modeling
  • monitoring
  • landslides
  • subsidence
  • susceptibility
  • risk analysis
  • GIS
  • machine learning

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Published Papers (10 papers)

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Research

15 pages, 17485 KiB  
Article
Time-Series InSAR with Deep-Learning-Based Topography-Dependent Atmospheric Delay Correction for Potential Landslide Detection
by Hao Zhou, Keren Dai, Xiaochuan Tang, Jianming Xiang, Rongpeng Li, Mingtang Wu, Yangrui Peng and Zhenhong Li
Remote Sens. 2023, 15(22), 5287; https://doi.org/10.3390/rs15225287 - 09 Nov 2023
Cited by 1 | Viewed by 1227
Abstract
Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address [...] Read more.
Synthetic aperture radar interferometry (InSAR) has emerged as an effective technique for monitoring potentially unstable landslides and has found widespread application. Nevertheless, in mountainous reservoir regions, the precision of time-series InSAR outcomes is often constrained by topography-dependent atmospheric delay (TDAD) effects. To address this limitation, we propose a novel InSAR time-series method that integrates TDAD correction. This approach employs advanced deep learning algorithms to individually model and mitigate TDAD for each interferogram, thereby enhancing the accuracy of small baseline subset InSAR (SBAS-InSAR) and stacking InSAR time-series analyses. Utilizing Sentinel-1 data, we apply this method to identify potential landslides in the Baihetan reservoir area, located in southwestern China, where we successfully identified 26 potential landslide sites. Comparative experimental results demonstrate a significant reduction (averaging 70% and reaching up to 90%) in phase standard deviation (StdDev) in the corrected interferograms, indicating a marked decrease in phase–topography correlation. Furthermore, the corrected time-series InSAR results effectively remove TDAD signals, leading to clearer displacement boundaries and a remarkable reduction in other spurious displacement signals. Overall, this method efficiently addresses TDAD in time-series InSAR, enabling precise identification of potentially unstable landslides influenced by TDAD, and providing essential technical support for early landslide hazard detection using time-series InSAR. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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22 pages, 76880 KiB  
Article
A Multifactor-Based Random Forest Regression Model to Reconstruct a Continuous Deformation Map in Xi’an, China
by Xinxin Guo, Chaoying Zhao, Guangrong Li, Mimi Peng and Qin Zhang
Remote Sens. 2023, 15(19), 4795; https://doi.org/10.3390/rs15194795 - 01 Oct 2023
Cited by 2 | Viewed by 885
Abstract
The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly [...] Read more.
The synthetic aperture radar interferometry (InSAR) technique is an effective means to monitor ground deformation with high spatial resolution over large areas. However, it is still difficult to obtain the spatially continuous deformation map due to SAR decorrelation or SAR distortion, which greatly limits the usage of the InSAR deformation map, especially for spatiotemporal characterizing and mechanism inversion. Some conventional methods (e.g., spatial interpolation) rely only on the deformation measurements without considering the influence factors, leading to the inaccuracy of the deformation prediction. So, we propose a multifactor-based machine learning model, namely the K-RFR model, that combines K-means clustering and random forest regression algorithm to reconstruct a continuous deformation map, where the influence factors on ground deformation are considered, such as land use, geological engineering, and under groundwater extraction. We take the city of Xi’an, China, as the study area where SBAS-InSAR was used to obtain the ground deformation maps from 2012 to 2015. Fourteen influence factors are employed, including confined water level, change of confined water, phreatic water level, change of phreatic water, rainfall, ground fissures, stratigraphic lithology, landform, hydrogeology, engineering geology, type of land use, soil type, GDP, and DEM, where the K-means clustering method is used to reduce the influence of spatial heterogeneity. The study area is divided into three homogeneous regions and modeled independently, where the mean squared errors of region I–III are 2.9 mm, 2.3 mm, and 3.9 mm, respectively, and the mean absolute errors are 2.5 mm, 1.0 mm, and 2.8 mm, respectively. Finally, the continuous ground deformation maps of Xi’an from 2012 to 2015 are reconstructed. We compared the new method with two interpolation methods. Results show that the correlation coefficient between prediction and InSAR measurements of the new model is 0.94, whereas the ordinary Kriging method is 0.69, and the IDW method is only 0.63. This study provides an effective means to predict the continuous surface deformation over a large area. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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17 pages, 7639 KiB  
Article
A Super-Resolution Network for High-Resolution Reconstruction of Landslide Main Bodies in Remote Sensing Imagery Using Coordinated Attention Mechanisms and Deep Residual Blocks
by Huajun Zhang, Chengming Ye, Yuzhan Zhou, Rong Tang and Ruilong Wei
Remote Sens. 2023, 15(18), 4498; https://doi.org/10.3390/rs15184498 - 13 Sep 2023
Cited by 1 | Viewed by 878
Abstract
The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) [...] Read more.
The lack of high-resolution training sets for intelligent landslide recognition using high-resolution remote sensing images is a major challenge. To address this issue, this paper proposes a method for reconstructing low-resolution landslide remote sensing images based on a Super-Resolution Generative Adversarial Network (SRGAN) to fully utilize low-resolution images in the process of constructing high-resolution landslide training sets. First, this paper introduces a novel Enhanced Depth Residual Block called EDCA, which delivers stable performance compared to other models while only slightly increasing model parameters. Secondly, it incorporates coordinated attention and redesigns the feature extraction module of the network, thus boosting the learning ability of image features and the expression of high-frequency information. Finally, a residual stacking-based landslide remote sensing image reconstruction strategy was proposed using EDCA residual blocks. This strategy employs residual learning to enhance the reconstruction performance of landslide images and introduces LPIPS for evaluating the test images. The experiment was conducted using landslide data collected by drones in the field. The results show that compared with traditional interpolation algorithms and classic deep learning reconstruction algorithms, this approach performs better in terms of SSIM, PSNR, and LPIPS. Moreover, the network can effectively handle complex features in landslide scenes, which is beneficial for subsequent target recognition and disaster monitoring. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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27 pages, 8444 KiB  
Article
An Assessment of Negative Samples and Model Structures in Landslide Susceptibility Characterization Based on Bayesian Network Models
by Sahand Khabiri, Matthew M. Crawford, Hudson J. Koch, William C. Haneberg and Yichuan Zhu
Remote Sens. 2023, 15(12), 3200; https://doi.org/10.3390/rs15123200 - 20 Jun 2023
Cited by 2 | Viewed by 1507
Abstract
Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, [...] Read more.
Landslide susceptibility mapping (LSM) characterizes landslide potential, which is essential for assessing landslide risk and developing mitigation strategies. Despite the significant progress in LSM research over the past two decades, several long-standing issues, such as uncertainties related to training samples and model selection, remain inadequately addressed in the literature. In this study, we employed a physically based susceptibility model, PISA-m, to generate four different non-landslide data scenarios and combine them with mapped landslides from Magoffin County, Kentucky, for model training. We utilized two Bayesian network model structures, Naïve Bayes (NB) and Tree-Augmented Naïve Bayes (TAN), to produce LSMs based on regional geomorphic conditions. After internal validation, we evaluated the robustness and reliability of the models using an independent landslide inventory from Owsley County, Kentucky. The results revealed considerable differences between the most effective model in internal validation (AUC = 0.969), which used non-landslide samples extracted exclusively from low susceptibility areas predicted by PISA-m, and the models’ unsatisfactory performance in external validation, as manifested by the identification of only 79.1% of landslide initiation points as high susceptibility areas. The obtained results from both internal and external validation highlighted the potential overfitting problem, which has largely been overlooked by previous studies. Additionally, our findings also indicate that TAN models consistently outperformed NB models when training datasets were the same due to the ability to account for variables’ dependencies by the former. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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21 pages, 19647 KiB  
Article
Globally vs. Locally Trained Machine Learning Models for Landslide Detection: A Case Study of a Glacial Landscape
by Alexandra Jarna Ganerød, Erin Lindsay, Ola Fredin, Tor-Andre Myrvoll, Steinar Nordal and Jan Ketil Rød
Remote Sens. 2023, 15(4), 895; https://doi.org/10.3390/rs15040895 - 06 Feb 2023
Cited by 4 | Viewed by 2116
Abstract
Landslide risk mitigation is limited by data scarcity; however, this could be improved using continuous landslide detection systems. To investigate which image types and machine learning models are most useful for landslide detection in a Norwegian setting, we compared the performance of five [...] Read more.
Landslide risk mitigation is limited by data scarcity; however, this could be improved using continuous landslide detection systems. To investigate which image types and machine learning models are most useful for landslide detection in a Norwegian setting, we compared the performance of five different machine learning models, for the Jølster case study (30 July 2019), in Western Norway. These included three globally pre-trained models; (i) the continuous change detection and classification (CCDC) algorithm, (ii) a combined k-means clustering and random forest classification model, and (iii) a convolutional neural network (CNN), and two locally trained models, including; (iv) classification and regression Trees and (v) a U-net CNN model. Images used included Sentinel-1, Sentinel-2, as well as digital elevation model (DEM) and slope. The globally trained models performed poorly in shadowed areas and were all outperformed by the locally trained models. A maximum Matthew’s correlation coefficient (MCC) score of 89% was achieved with a CNN U-net deep learning model, using combined Sentinel-1 and -2 images as input. This is one of the first attempts to apply deep learning to detect landslides with both Sentinel-1 and -2 images. Using Sentinel-1 images only, the locally-trained deep-learning model significantly outperformed the conventional machine learning model. These findings contribute to developing a national continuous monitoring system for landslides. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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27 pages, 14057 KiB  
Article
Creation of Wildfire Susceptibility Maps in Plumas National Forest Using InSAR Coherence, Deep Learning, and Metaheuristic Optimization Approaches
by Arip Syaripudin Nur, Yong Je Kim and Chang-Wook Lee
Remote Sens. 2022, 14(17), 4416; https://doi.org/10.3390/rs14174416 - 05 Sep 2022
Cited by 10 | Viewed by 2565
Abstract
Plumas National Forest, located in the Butte and Plumas counties, has experienced devastating wildfires in recent years, resulting in substantial economic losses and threatening the safety of people. Mapping damaged areas and assessing wildfire susceptibility are necessary to prevent, mitigate, and manage wildfires. [...] Read more.
Plumas National Forest, located in the Butte and Plumas counties, has experienced devastating wildfires in recent years, resulting in substantial economic losses and threatening the safety of people. Mapping damaged areas and assessing wildfire susceptibility are necessary to prevent, mitigate, and manage wildfires. In this study, a wildfire susceptibility map was generated using a CNN and metaheuristic optimization algorithms (GWO and ICA) based on images of areas damaged by wildfires. The locations of damaged areas were identified using the damage proxy map (DPM) technique from Sentinel-1 synthetic aperture radar (SAR) data collected from 2016 to 2020. The DPMs’ depicting areas damaged by wildfires were similar to fire perimeters obtained from the California Department of Forestry and Fire Protection (CAL FIRE). Data regarding damaged areas were divided into a training set (50%) for modeling and a testing set (50%) for assessing the accuracy of the models. Sixteen conditioning factors, categorized as topographical, meteorological, environmental, and anthropological factors, were selected to construct the models. The wildfire susceptibility models were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) and root mean square error (RMSE) analysis. The evaluation results revealed that the hybrid-based CNN-GWO model (AUC = 0.974, RMSE = 0.334) exhibited better performance than the CNN (AUC = 0.934, RMSE = 0.780) and CNN-ICA (AUC = 0.950, RMSE = 0.350) models. Therefore, we conclude that optimizing a CNN with metaheuristics considerably increased the accuracy and reliability of wildfire susceptibility mapping in the study area. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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29 pages, 10941 KiB  
Article
Landslide Displacement Prediction Based on a Two-Stage Combined Deep Learning Model under Small Sample Condition
by Chunxiao Yu, Jiuyuan Huo, Chaojie Li and Yaonan Zhang
Remote Sens. 2022, 14(15), 3732; https://doi.org/10.3390/rs14153732 - 04 Aug 2022
Cited by 3 | Viewed by 1725
Abstract
The widely distributed “Step-type” landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of local geological hazard early warning systems for the realization of [...] Read more.
The widely distributed “Step-type” landslides in the Three Gorges Reservoir (TGR) area have caused serious casualties and heavy economic losses. The prediction research of landslide displacement will be beneficial to the establishment of local geological hazard early warning systems for the realization of scientific disaster prevention and mitigation. However, the number of observed data like landslide displacement, rainfall, and reservoir water level in this area is very small, which results in difficulties for the training of advanced deep learning model to obtain more accurate prediction results. To solve the above problems, a Two-stage Combined Deep Learning Dynamic Prediction Model (TC-DLDPM) for predicting the typical “Step-type” landslides in the TGR area under the condition of small samples is proposed. The establishment process of this method is as follows: (1) the Dynamic Time warping (DTW) method is used to enhance the small samples of cumulative displacement data obtained by the Global Positioning System (GPS); (2) A Difference Decomposition Method (DDM) based on sequence difference is proposed, which decomposes the cumulative displacement into trend displacement and periodic displacement, and then the cubic polynomial fitting method is used to predict the trend displacement; (3) the periodic displacement component is predicted by the proposed TC-DLDPM model combined with external environmental factors such as rainfall and reservoir water level. The TC-DLDPM model combines the advantages of Convolutional Neural Network (CNN), Attention mechanism, and Long Short-term Memory network (LSTM) to carry out two-stage learning and parameter transfer, which can effectively realize the construction of a deep learning model for high-precision under the condition of small samples. A variety of advanced prediction models are compared with the TC-DLDPM model, and it is verified that the proposed method can accurately predict landslide displacement, especially in the case of drastic changes in external factors. The TC-DLDPM model can capture the spatio-temporal characteristics and dynamic evolution characteristics of landslide displacement, reduce the complexity of the model, and the number of model training calculations. Therefore, it provides a better solution and exploration idea for the prediction of landslide displacement under the condition of small samples. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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30 pages, 11779 KiB  
Article
Landslide Susceptibility Mapping of Landslides with Artificial Neural Networks: Multi-Approach Analysis of Backpropagation Algorithm Applying the Neuralnet Package in Cuenca, Ecuador
by Esteban Bravo-López, Tomás Fernández Del Castillo, Chester Sellers and Jorge Delgado-García
Remote Sens. 2022, 14(14), 3495; https://doi.org/10.3390/rs14143495 - 21 Jul 2022
Cited by 17 | Viewed by 2463
Abstract
Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational [...] Read more.
Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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17 pages, 15894 KiB  
Article
Landslide Segmentation with Deep Learning: Evaluating Model Generalization in Rainfall-Induced Landslides in Brazil
by Lucas Pedrosa Soares, Helen Cristina Dias, Guilherme Pereira Bento Garcia and Carlos Henrique Grohmann
Remote Sens. 2022, 14(9), 2237; https://doi.org/10.3390/rs14092237 - 06 May 2022
Cited by 15 | Viewed by 3712
Abstract
Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models [...] Read more.
Automatic landslide mapping is crucial for a fast response in a disaster scenario and improving landslide susceptibility models. Recent studies highlighted the potential of deep learning methods for automatic landslide segmentation. However, only a few works discuss the generalization capacity of these models to segment landslides in areas that differ from the ones used to train the models. In this study, we evaluated three different locations to assess the generalization capacity of these models in areas with similar and different environmental aspects. The model training consisted of three distinct datasets created with RapidEye satellite images, Normalized Vegetation Index (NDVI), and a digital elevation model (DEM). Here, we show that larger patch sizes (128 × 128 and 256 × 256 pixels) favor the detection of landslides in areas similar to the training area, while models trained with smaller patch sizes (32 × 32 and 64 × 64 pixels) are better for landslide detection in areas with different environmental aspects. In addition, we found that the NDVI layer helped to balance the model’s results and that morphological post-processing operations are efficient for improving the segmentation precision results. Our research highlights the potential of deep learning models for segmenting landslides in different areas and is a starting point for more sophisticated investigations that evaluate model generalization in images from various sensors and resolutions. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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21 pages, 5304 KiB  
Article
Machine Learning for Defining the Probability of Sentinel-1 Based Deformation Trend Changes Occurrence
by Pierluigi Confuorto, Camilla Medici, Silvia Bianchini, Matteo Del Soldato, Ascanio Rosi, Samuele Segoni and Nicola Casagli
Remote Sens. 2022, 14(7), 1748; https://doi.org/10.3390/rs14071748 - 05 Apr 2022
Cited by 8 | Viewed by 2418
Abstract
The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of [...] Read more.
The continuous monitoring of displacements occurring on the Earth surface by exploiting MTInSAR (Multi Temporal Interferometry SAR) Sentinel-1 data is a solid reality, as testified by the ongoing operational ground motion service in the Tuscany region (Central Italy). In this framework, anomalies of movement, i.e., accelerations or deceleration as seen by the time series of displacement of radar targets, are identified. In this work, a Machine Learning algorithm such as the Random Forest has been used to assess the probability of occurrence of the anomalies induced by slope instability and subsidence. About 20,000 anomalies (about 7000 and 13,000 for the slope instability and the subsidence, respectively) were collected between 2018 and 2020 and were used as input, while ten different variables were selected, five related to the morphological and geological setting of the study area and five to the radar characteristics of the data. The resulting maps may provide useful indications of where a sudden change of displacement trend may occur, analyzing the contribution of each factor. The cross-validation with the anomalies collected in a following timespan (2020–2021) and with official landslide and subsidence inventories provided by the regional authority has confirmed the reliability of the final maps. The adoption of a map for assessing the probability of the occurrence of MTInSAR anomalies may serve as an enhanced geohazard prevention measurement, to be periodically updated and refined in order to have the most precise knowledge possible of the territory. Full article
(This article belongs to the Special Issue Machine Learning and Remote Sensing for Geohazards)
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