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Landslides are destructive geohazards to people and infrastructure, resulting in hundreds of deaths and billions of dollars of damage every year. Therefore, mapping the rate of deformation of such geohazards and understanding their mechanics is of paramount importance to mitigate the resulting impacts and properly manage the associated risks. In this paper, the main outcomes relevant to the joint European Space Agency (ESA) and the Chinese Ministry of Science and Technology (MOST) Dragon-5 initiative cooperation project ID 59,339 "Earth observation for seismic hazard assessment and landslide early warning system" are reported. The primary goals of the project are to further develop advanced SAR/InSAR and optical techniques to investigate seismic hazards and risks, detect potential landslides in wide regions, and demonstrate EO-based landslide early warning system over selected landslides. This work only focuses on the landslide hazard content of the project, and thus, in order to achieve these objectives, the following tasks were developed up to now: a) a procedure for phase unwrapping errors and tropospheric delay correction; b) an improvement of a cross-platform SAR offset tracking method for the retrieval of long-term ground displacements; c) the application of polarimetric SAR interferometry (PolInSAR) to increase the number and quality of monitoring points in landslide-prone areas; d) the semiautomatic mapping and preliminary classification of active displacement areas on wide regions; e) the modeling and identification of landslides in order to identify triggering factors or predict future displacements; and f) the application of an InSAR-based landslide early warning system on a selected site. The achieved results, which mainly focus on specific sensitive regions, provide essential assets for planning present and future scientific activities devoted to identifying, mapping, characterizing, monitoring and predicting landslides, as well as for the implementation of early warning systems. [ABSTRACT FROM AUTHOR]

In recent years, RS and GIS technologies have played an increasingly important role in various aspects of rainfall induced landslide research. In order to systematically understand their application situation, this paper extensively used various visualization analysis technologies for in-depth analysis of 1,161 documents collected by the WOS data platform in the past 27 years by combining quantitative and qualitative methods. Then, this article focuses on sub domain analysis from four aspects: landslide detection and monitoring, prediction models, sensitivity mapping, and risk assessment. The study found that the number of literature in this field has steadily increased and is expected to continue to rise. This literature review has attracted widespread attention from the academic community, but it is challenging to meet research needs. Frequent and effective cooperation between countries, institutions, and authors is very beneficial for promoting progress in this field. The future development direction is a new intelligent hybrid model that integrates multiple research methods. This study can provide researchers in this field with the core research force, hot topic evolution, and future development trends of future rainfall-induced landslides and contribute to landslide prevention and control decision-making and achieving the United Nations'sustainable development goals. [ABSTRACT FROM AUTHOR]

This paper accompanies the Maps of Geohazard features of the Cilento and the Calabro- Tyrrhenian continental margin in the southern Tyrrhenian Sea (Italy). The main geohazardrelated features were derived from extensive seafloor mapping through the collection of high-resolution multibeam data acquired during several oceanographic cruises. They encompass many fluids seepage features, fault scarps, landslides scars, gullies, channels, and canyons. Hazards related to coastal landslides and shelf-indenting canyons are very high in these sectors (especially in southern Calabria) due to active seismicity coupled with rapid uplift, high sedimentation rates and narrow or totally absent continental shelf, thus promoting a direct connection between steep slopes and coastal areas. In this setting, masswasting features can directly impact coastal or submarine infrastructures or indirectly create local tsunami waves, as observed in historical times. Moreover, this physiographic setting of the margin facilitates the transfer of marine litter toward deep-sea areas. [ABSTRACT FROM AUTHOR]

The MaGIC project (Marine Geohazard along the Italian Coasts) had the aim of mapping the geohazard in the Italian seas and resulted in the production of numerous maps covering parts of the Italian Seas. In this paper, we present the maps: 'The submerged portions of the Aeolian volcanic islands and the north-eastern Sicilian margin', located in the south-eastern Tyrrhenian Sea. Both areas are affected by active geological processes, which represent important geohazards elements. Inthe submarine parts of the Stromboli volcanoremobilization of volcaniclastic deposits occur along the Sciara del Fuoco, where small-scale instabilities may represent a source of geohazard. Hydrothermal activity occurs on Enarete and Enaretino conical seamounts. The north-eastern Sicilian margin has a narrow continental shelf. Numerous canyon heads indent the shelf and, sometimes, reach close to the coast. Canyons have often a retrogradational trend and further eventual landward shift through sliding can iendangeri coastal or offshore infrastructures. Many of the canyons connect with leveed channels with widespread sediment instability. In the Gioia Basin, some of the channels connect to form the Stromboli slope Valley. Volcanic unrest or local and regional earthquakes are proven to have caused submarine landslides and tsunamis. [ABSTRACT FROM AUTHOR]

Landslide susceptibility mapping (LSM) research is vital for averting and mitigating regional landslide disasters. Nevertheless, there has been a lack of systematic analysis regarding LSM's developmental drivers. Utilizing SciMAT, a scientometric tool, we analyzed 1661 papers on LSM from the Web of Science core collection database, spanning 1993 to 2022. We employed cluster and thematic evolution analysis in SciMAT to unveil trends. The results indicate a consistent upward trend over the past three decades. LSM modeling methods, geological data, and contributing factors are major focal points. Notably, the evolution of LSM models, with a rising adoption of machine learning and deep learning for risk assessment, emerges as a central knowledge pathway. This study offers valuable insights to scholars by identifying literature gaps and highlighting crucial research directions, facilitating informed decision-making in the realm of LSM. SciMAT tool initially employs cluster analysis and thematic evolution analysis to pinpoint the current popular themes and evolutionary trends of landslide susceptibility. The main trajectory of knowledge lies in the progression of landslide modeling methods. The latest trend in landslide susceptibility research is centered on the use of deep learning techniques, particularly convolutional neural networks, to achieve precise risk zonation. The study aims to highlight critical research directions, predict the future of LSM technology, and provide guidance for scholars. [ABSTRACT FROM AUTHOR]

Geomorphological mapping can be described as a group of techniques employed to systematically record the morphology of the ground, landforms, landscape-forming processes and materials that constitute the surface of the Earth (Griffiths et al., [13]; Lee, [16]). In the paper by [10], the "geo-historical" map is described as a synthesis of historical and geomorphological data for landslides and landslide occurrence in the Ceno Torrent valley (Northern Apennines, Italy). The knowledge of flood-induced ground effects and flood-water dynamics is underlined as a crucial issue for hydro-geomorphic hazard assessment and mapping. This special issue presents contributions about the methodologies and tools for representing and mapping geomorphological hazards, with a focus upon hazard and risk classification and tools for land planning, risk reduction and mitigation. [Extracted from the article]

This paper focuses on Deep-Seated Gravitational Slope Deformations (DSGSDs) identified in the Molise region (Italy), along the central-southern sector of the Apennines chain. These phenomena were detected and mapped through a combination of aerial-photo interpretation, morphological analyses, and field surveys, integrated through satellite SAR interferometry (InSAR). The obtained general map is organized in different sections: inventory map with identification of DSGSDs and main geological and orographic features, geostructural scheme, representative cross sections, list and characteristics of DSGSDs, geomorphometric maps, landslides map, and seismotectonic setting. This study allowed us to realize a complete inventory of DSGSDs in the Molise region and define the main characteristics of each phenomenon. Through this research, it was possible to establish a general correlation between the analyzed deformations and the geological and geomorphological setting of the region as well as with its geodynamic context. [ABSTRACT FROM AUTHOR]

This paper explores the scope for upscaling and transnational transfer of participatory landslide risk management strategies for informal settlements in Latin America. Drawing on lessons from transdisciplinary action-research in three neighbourhoods in Medellín, Colombia, and one in São Paulo, Brazil, the paper discusses how bottom-up approaches were developed to co-produce landslide risk management in both Global South cities, in a way that optimises the collaboration between communities and relevant governmental bodies. The analysis focuses on mitigation and 'agreement-seeking', from the perspectives of scale, power and knowledge, which help understand the parallels between co-production of landslide risk management and co-production of urban services. Two key conclusions are that landslide risk management should be built into neighbourhood upgrading and management, and that both community and the state have stronger roles to play in landslide risk management from their respective capacities. The paper also reflects on the role of academia in enabling co-production of landslide risk management through engaging with local communities. [ABSTRACT FROM AUTHOR]

The Piuro 1618 landslide is renowned as one of the most catastrophic historical events of the Central Alps. The landscape of the Bregaglia Valley has been successively modified to such an extent to make it difficult to ascertain the source of the landslide, the extent of its deposits and the setting of the ancient village. This study focuses on the identification of the source area of the landslide, its dynamics, the extent of its deposits and the estimation of the involved volumes. The geological map here proposed permitted to reconstruct the relationships between the pre-1618 setting, the 1618 restructuration of the landscape and the post-event changes that occurred owing to erosion by the trunk river, deposition of debris flow fans and slope dynamics. The paper shows how geological mapping may help to unravel landscape evolution of an Alpine valley even at the high-resolution required by historical and archaeological studies. [ABSTRACT FROM AUTHOR]

Reliable assessment of landslide susceptibility in broad areas of terrain remains challenging due to complex topography and poor representation of randomly selected negative samples. Assessment in broad areas is now primarily based on grid units, which do not have a clear physical meaning like slope units, and their accuracy is not ideal. Nevertheless, the large amount of manual editing, due to the incorrectly generated horizontal and vertical lines during slope unit partitioning, limits using slope units for rapid assessment over large areas. Hence, this paper proposes a reliable susceptibility assessment approach to solve this problem based on optimal slope units and negative samples involving prior knowledge. Precisely, an algorithm to automatically extract slope units is designed to eliminate fragmented and erroneous units. Second, a samples labeling index (SLI) is defined based on the certainty factors model to select negative samples reasonably. Sichuan Province, China is selected for experimental analysis, with the results demonstrate that the optimized slope unit and the negative samples selection strategy consider prior knowledge achieve better results in the random forest model, support vector machine model, and artificial neural network model. In particular, the composite performance index AUC of artificial neural network model improved from 0.81 to 0.90. [ABSTRACT FROM AUTHOR]

Starting on 24th August 2016, Central Italy was struck by a six-month earthquake sequence that caused 303 victims and extensive major damages to urban areas and infrastructures, in some cases entire villages needed complete rebuilding. In this paper we present a map that portrays the overall susceptibility to multiple landslide types and the exposure to landslides of the rural-urban areas of the Castelsantangelo sul Nera Municipality, a typical village of the central Italian Apennine. The map is based on a procedure that ingests geomorphological data and models and groups the individual landslide susceptibility maps in a joint susceptibility and exposure map based on expert-defined criteria. The procedure has been applied to built-up and to undeveloped areas to highlight their exposure and was used as a tool for planning post-seismic reconstruction. We advise that such maps are used also as basic tool for ordinary urban planning. [ABSTRACT FROM AUTHOR]

The paper describes the multitemporal landslide inventory map prepared for the urban areas of Motta Montecorvino and Volturino, two municipalities located in the Southern Apennines (Apulia Region, Italy). These territories show a high propensity to landslides of different types and magnitude, which periodically interfere with the anthropic structures and infrastructures. For the study area, the spatial and temporal distribution of landslides is detected for the period between 1954 and 2003, through the visual interpretation of multiple sets of black and white digital stereoscopic aerial photographs at different scales. The analysis reveals locally high frequency of landslide occurrence and built-up areas on existing landslides, either on the body or on the crown areas. In particular, we show that over the years new residential areas were developed despite the presence of large old mass movements. [ABSTRACT FROM AUTHOR]

In this paper we present geomorphological hazard mapping of the San Vito Peninsula offshore (Sicilian margin of Tyrrhenian Sea), characterised by a very narrow continental shelf and a very dipping, tectonically active continental slope, no far from a seismogenic belt. The data set consists of morpho-bathymetric models achieved by means of multibeam survey and scattered high resolution seismic profiles. The morpho-bathymetric study highlighted some potential sites of geomorphological hazards, the most representative of which are: (i) two canyons developing from the upper slope off San Vito Cape down to the Erice basin; (ii) a dense network of small incisions across the upper slope; (iii) a very active landslide, developed onland and extending in the continental shelf close to the Scopello village. The geomorphological features that can generate submarine hazard are represented by: unstable sediment packages into canyon heads or along the upper slope; submarine canyons with regressive erosion; coastal landslides. [ABSTRACT FROM AUTHOR]

In this paper, a geomorphological map of Pescara del Tronto area (Sibillini Mts, Marche Region) is presented. The work focuses on the geomorphological analysis performed in a zone strongly struck by the 2016–2017 seismic sequence of Central Apennines. The geomorphological map (1:7,500 scale) was obtained through an integrated approach that incorporates geological-geomorphological field mapping and geomorphological profile drawing, supported by air-photo interpretation and GIS analysis. The main purpose of the work is to describe a geomorphological approach for representing and mapping the evidence of several debris flows and landslides recognized in the framework of seismic microzonation (SM) activities. Finally, in order to elevate geomorphological maps into effective tools for land management and risk reduction, it could provide a scientific and methodological basis to demonstrate that accurate mapping provides important information, readily available for local administrations and decision-makers, for the implementation of sustainable territorial planning and loss-reduction measures. [ABSTRACT FROM AUTHOR]

In landslide susceptibility modeling, the selection of the mapping units is a very relevant topic both in terms of geomorphological adequacy and suitability of the models and final maps. In this paper, a test to integrate pixels and slope units is presented. MARS (Multivariate Adaptive Regression Splines) modeling was applied to assess landslide susceptibility based on a 12 predictors and a 1608 cases database. A pixel-based model was prepared and the scores zoned into 10 different types of slope units, obtained by differently combining two half-basin (HB) and four landform classification (LCL) coverages. The predictive performance of the 10 models were then compared to select the best performing one, whose prediction image was finally modified to consider also the propagation stage. The results attest integrating HB with LCL as more performing than using simple HB classification, with a very limited loss in predictive performance with respect to the pixel-based model. [ABSTRACT FROM AUTHOR]

Rapid and accurate landslide inventory mapping is significant for emergency rescue and post-disaster reconstruction. Nowadays, deep learning methods exhibit excellent performance in supervised landslide detection. However, due to differences between cross-scene images, the performance of existing methods is significantly degraded when directly applied to another scene, which limits the application of rapid landslide inventory mapping. In this study, we propose a novel Domain Style and Feature Adaptation (DSFA) method for cross-scene landslide detection from high spatial resolution images, which can leverage labeled source domain images and unlabeled target domain images to mine robust landslide representations for different scenes. Specifically, we mitigate the large discrepancy between domains at the dataset level and feature level. At the dataset level, we introduce a domain style adaptation strategy to shift landslide styles, which not only bridges the domain gap, but also increases the diversity of landslide samples. At the feature level, adversarial learning and domain distance minimization are integrated to narrow large feature distribution discrepancies for learning domain-invariant information. In addition, to avoid information omission, we improve the U-Net3+ model. Extensive experimental results demonstrate that DSFA has superior detection capability and outperforms other methods, showing its great application potential in unsupervised landslide domain detection. [ABSTRACT FROM AUTHOR]

Accurate landslide extraction is significant for landslide disaster prevention and control. Remote sensing images have been widely used in landslide investigation, and landslide extraction methods based on deep learning combined with remote sensing images (such as U-Net) have received a lot of attention. However, because of the variable shape and texture features of landslides in remote sensing images, the rich spectral features, and the complexity of their surrounding features, landslide extraction using U-Net can lead to problems such as false detection and missed detection. Therefore, this study introduces the channel attention mechanism called the squeeze-and-excitation network (SENet) in the feature fusion part of U-Net; the study also constructs an attention U-Net landside extraction model combining SENet and U-Net, and uses Sentinel-2A remote sensing images for model training and validation. The extraction results are evaluated through different evaluation metrics and compared with those of two models: U-Net and U-Net Backbone (U-Net Without Skip Connection). The results show that proposed the model can effectively extract landslides based on Sentinel-2A remote sensing images with an F1 value of 87.94%, which is about 2% and 3% higher than U-Net and U-Net Backbone, respectively, with less false detection and more accurate extraction results. [ABSTRACT FROM AUTHOR]

The assessment of landslide hazards is crucial for disaster prevention and mitigation, but it has not considered the dynamic influencing factors that trigger landslides. The timeliness and practical value of the assessment results still need to be further improved. This study constructed a dynamic landslide hazard assessment system using information value model, dynamic precipitation data, and Backpropagation Neural Network (BPNN) model. Taking the Qingjiang Reservoir landslide in Changyang County, Hubei Province, China as an example, based on dynamic precipitation data and the BPNN model were used to develop a dynamic landslide hazard prediction model, and the temporal assessment and spatial distribution results of slope unit hazards in the study area from the 1980s to the 2010s, 2025, and 2030 were evaluated and predicted. It is predicted that the percentage of very high and high areas in 2025 and 2030 will be 50.5% and 57.5% respectively. [ABSTRACT FROM AUTHOR]

Traditional landslide susceptibility assessment methods often rely on single models, which can be biased and less accurate. In this article, we introduce a two-tiered strategy to enhance landslide susceptibility predictions. Initially, we employ an ensemble stacking technique that combines the strengths of three machine learning classifiers. This combination leverages the support vector classifier (SVC) as the key meta-classifier to optimize and refine predictions. Subsequently, we integrate the extreme gradient boosting (XGB), random forest (RF) and gradient boosting decision tree (GBDT) models with SVC to create hybrid approaches. In this study, we evaluate and compare the effectiveness of six machine learning algorithms for predicting landslide susceptibility in Anhua County, Hunan Province, China. The results demonstrated that the stacking ensemble model outperforms traditional models. The XBGþSVC model achieves the highest AUC value (0.9468), which is followed by the GBDTþSVC (0.9316), RFþSVC (0.9162), XGB (0.9393), GBDT (0.9009), and RF (0.8693). These findings indicate that stacking machine learning approaches hold promise for landslide susceptibility mapping. [ABSTRACT FROM AUTHOR]

The periodic storage and release of water in reservoirs have the potential to accelerate deformation or trigger ancient landslides. Interferometric synthetic aperture radar (InSAR) technology, renowned for its wide coverage and high precision, has emerged as an effective technique for the identification and monitoring of landslides. This study utilized spaceborne InSAR technology to identify and interpret landslides in the Liujiangxia reservoir area in China, leading to the detection of a total of 20 landslides. Moreover, among these 20 landslides, the landslide with substantial deformation (LJX02) was chosen, and the wavelet quantitative analysis technique was employed to investigate the correlation and time lag between various elevation positions of this landslide, the reservoir water level, and rainfall. The study successfully demonstrated that the deformation of the landslide lagged behind the reservoir water level by 152.10-182.50 days (from low to high elevation) and was delayed by 45.60-68.44 days compared to rainfall (from high to low elevation). [ABSTRACT FROM AUTHOR]

After the earthquake in Wenchuan in 2008, landslides have been happening frequently, causing a buildup of sediment in slopes and gullies. This creates a potential for mudslides and flash floods. It is important to create a model to assess the likelihood of landslides in Wenchuan County for disaster prevention. This study focuses on Wenchuan County, using 6180 co-seismic landslide sites as data. The relevant survey data is combined, and slope, normalized difference vegetation Index (NDVI), peak seismic acceleration (PGA), stream power index (SPI), topographic wetness index (TWI), distance from the road, distance from the fault, distance from the water system, relief intensity, aspect, curvature, land use type, and lithologic characters are chosen. The significance of the co-seismic landslide key influence factors is discussed. The study area's disaster susceptibility was assessed using the informative hierarchical analysis method model (I-AHP) and fully connected neural network (FCNN) model; the evaluation's findings mostly agreed with the survey's actual findings. The study's findings demonstrated that: (1) the FCNN model's AUC value was 0.910, while the I-AHP model's AUC value was 0.768, and the ROC curve and AUC value analysis indicated that the FCNN model outperformed the I-AHP model. (2) The three most significant influencing factors are lithology, topographic relief, and distance from roads; they account for 29.5%, 11%, and 10% of the total, respectively. Thus, landslide hazard research should be done in the high terrain undulating areas surrounding the road, and rock monitoring should be intensified in post-earthquake landslide-prone areas like Wenchuan County. (3) Given that Yingxiu Town is the epicentre of the region and that landslide disasters are highly likely to occur there, technical support for landslide disaster prediction, forecasting, safe prevention, and control in the area needs to be strengthened. [ABSTRACT FROM AUTHOR]

Landslide inventory incompleteness (LII) may significantly affect the model performance in landslide susceptibility mapping (LSM). However, traditional methods, including heuristic, statistical and deterministic models, cannot address LII issue. In this work, we introduce a novel hybrid LEO-MAHP model, blending landslide frequency, empirical adjustments, optimization functions, and multi-participated analytic hierarchy process to address it by taking Badong County as the study area. This hybrid model mitigates the drawbacks of data-heavy statistical approaches and subjective heuristic models by incorporating LII into weight determination. The findings show that the LEO-MAHP model demonstrates superior performance (AUROC = 0.809 and 0.805) over conventional statistical (AUROC = 0.714 and 0.770) and heuristic models (AUROC = 0.738 and 0.741) across different LII levels. We further discuss alternative LII solutions, proposing an updated landslide management strategy that accounts for climate change and human activities. Our findings underscore the necessity of evaluating LII before applying statistical or machine learning methods in LSM. List of Abbreviations: AHP: Analytic hierarchy process; AUROC: Area under receiver operating characteristic curve; CR: Consistency ratio; CI: Consistency index; CF: Conditional factor; DEM: Digital elevation model; FN: False negative; FP: False positive; FPR: False positive rate; InSAR: Interferometric Synthetic Aperture Radar; IV: Information value; LD: Landslide density; LII: Landslide inventory incompleteness; LS: Landside susceptibility; LSI: Landslide susceptibility index; LSM: Landslide susceptibility mapping; MAHP: Multi-participated analytic hierarchy process; MC: Model construction; MCDA: Multi-criteria decision analysis; MV: Model verification; MPP: Model prediction performance; ORS: Optical remote sensing; P&C: Prevention and control; PGA: peak ground acceleration; ROC: Receiver operating characteristic; RI: Random index; TN: True negative; TP: True positive; TPR: True positive rate. [ABSTRACT FROM AUTHOR]

We present and experimentally demonstrate a seismic ambient noise monitoring optical fibre sensor for early warning detection of sinkholes. The developed optical fibre sensor is designed for warning alert of subsidence and cover collapse sinkholes. The progressive process of sinkhole development causes structural change in the subterranean surface. The impact of this change and its influence on the subsurface acoustic modes was detected in the form of variations in the spectral content of the ambient noise signals monitored in the subsurface. Structural surface integrity was monitored through frequency response as the void increased. Vibrational states relating to unsteady structural conditions were identified. Significant instability events were captured giving timely warnings before collapse. The polarisation based single mode fibre sensor and monitoring method is proposed for implementation in a phase sensitive distributed acoustic sensor setup. Peak frequencies in the micro-seismic noise band of 0.1 Hz to 1.0Hz were observed through cavity development and growth. Extended peak frequency shifts and bandwidth in the band >1Hz were recorded, indicating weakness and imminence of collapse. Early warning detection by the structural field model was achieved prior to the sudden subsurface failure which results in collapse sinkholes. By monitoring variations in the vibrating frequency modes when a subsurface cavity develops within the structure, trigger events and collapse precursor conditions are identified. We have successfully demonstrated an early response warning annunciator by using an algorithm to analyse combinational characteristics of the spectral components of the detected signals. The fibre sensor reduces the risk and socio-economic impact of infrastructural damage due to sudden collapse of sinkholes and has extended potential of monitoring earthquakes and landslides. [ABSTRACT FROM AUTHOR]

The north-eastern border of CA represents the accretionary system developed during its continuous collision with Apulian block. The tardive oblique tectonic component is highlighted by pervasive NW-SE crustal transpressive left-lateral strike slip faults developed from the Upper Miocene to Quaternary tectonic stages. The seismotectonic and landslides Main Map represents the update of the main faults, the style of their arrangement and kinematics, the seismotectonic features and landslide area distribution with risk implications. Seismotectonics allows us to recognise a vertical crustal zonation with the distribution of earthquakes and their focal mechanism solution. The prevalent modality of ruptures depicts a mainly transcurrent and compressive solution in mid-lower crust portions respectively. This data, together with the historical seismicity analysis of the area, lead to consider the NW-SE strike-slip faults improved on the Main Map, as recent and tectonically active. The areal distribution of landslide and susceptibility of the area is due to the interaction between seismotectonic peculiarity, lithological typologies and fault zone architectures. Landslides and flood phenomena of this sector involve many municipalities, major and minor infrastructures and economic and social activities. The landslides and the faults have been identified, mapped and classified, originally at detail scale and, then, represented at 1:50,000 scale in the Main Map, included as supplementary material. The geo-structural and geomorphological data were analysed in a geographic information system allowing data management and implementation. The work presents an updated knowledge framework of risk conditions of the study zone, available to plan and reduce the fundamental elements that determine the landslide and seismic risks in this region. [ABSTRACT FROM AUTHOR]

Periodically monitoring landslides is a key factor for supporting the realisation of hazard warning systems and risk reduction in the corresponding neighbourhood areas. Although satellite remote sensing solutions can be considered for low spatial resolution monitoring, this approach is still inappropriate for high spatial resolution investigations. Ground-based Radar Interferometry is also a widely used technique that allows for working at a proper spatial resolution, but it can often be an overbudget solution for most applications. Instead, photogrammetric surveys based on Unmanned Aerial System (UAS) imagery appear as a very interesting approach in terms of both spatial resolution and flexibility in temporally repeating the survey. Motivated by this observation, this work investigates the use of multi-temporal UAS surveys for landslide monitoring. To be more precise, Digital Image Correlation (DIC) has been applied to orthomosaics generated from different UAS photogrammetry surveys to compute the area's deformation map. Compared with a reference GNSS survey, the results obtained using NHAZCA IRIS software and an in-house DIC approach show a deformation estimation accuracy of approximately 0.1 m, a reasonable accuracy for landslides moving at moderate velocity. [ABSTRACT FROM AUTHOR]

In Italy, landslides are one of the main geological hazards. Sometimes urban areas are affected by deep-seated gravitational slope deformations (DGSDs) that can lead to a potentially catastrophic failure and trigger a secondary collateral landslide. In these cases, monitoring is important for early warning and risk reduction. In this study, we analyzed DGSDs, in particular, lateral spreads, and landslides in the inhabited areas of Ulassai, in eastern-central Sardinia. Starting from high-resolution geostructural and geomorphological surveys of lateral spread, integrated with high-resolution digital elevation models acquired by UAV (Unmanned Aerial Vehicle), we created geological 3D models of the slope deformations. To better understand the kinematics and temporal evolution of unstable slope deformation, a monitoring system, consisting of Space-borne Interferometric Persistent Scatter Synthetic Aperture Radar (PS-InSAR), GNSS antennas, tiltmeters, and extensometers, was performed. PS-InSAR analysis confirmed by GNSS periodic measurement identified downslope movement of up to 10 mm per year in the lateral spread. Continuous acquisition by extensometers and tiltmeter recorder displacement in large block inclinations and opening and closing of fractures since 2021. Integrated data analysis will be essential to define the threshold for a future 24/7 early-warning system. [ABSTRACT FROM AUTHOR]

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Landslides are one of the most common geological hazards worldwide, especially in Sichuan Province (Southwest China). The current study's main purposes are to explore the potential applications of convolutional neural networks (CNN) hybrid ensemble metaheuristic optimization algorithms, namely beluga whale optimization (BWO) and coati optimization algorithm (COA), for landslide susceptibility mapping in Sichuan Province (China). For this aim, fourteen landslide conditioning factors were compiled in a spatial database. The effectiveness of the conditioning factors in the development of the landslide predictive model was quantified using the linear support vector machine model. The receiver operating characteristic (ROC) curve (AUC), the root mean square error, and six statistical indices were used to test and compare the three resultant models. For the training dataset, the AUC values of the CNN-COA, CNN-BWO and CNN models were 0.946, 0.937 and 0.855, respectively. In terms of the validation dataset, the CNN-COA model exhibited a higher AUC value of 0.919, while the AUC values of the CNN-BWO and CNN models were 0.906 and 0.805, respectively. The results indicate that the CNN-COA model, followed by the CNN-BWO model, and the CNN model, offers the best overall performance for landslide susceptibility analysis. [ABSTRACT FROM AUTHOR]

The Karakoram Highway (KKH) is a pivotal gateway within the framework of the China-Pakistan Economic Corridor. Nevertheless, its distinct and intricate geographical characteristics make it susceptible to recurrent landslides. As an essential tool, landslide susceptibility mapping (LSM) is significant in managing and alleviating landslides near the KKH. Moreover, landslide conditioning factors (LCFs) are crucial determinants influencing the outcomes of LSM. However, existing methods primarily rely on machine learning algorithms, which do not adequately account for the intricate spatial characteristics and patterns between LCFs and landslide occurrences. In response, this study introduces the feature fusion transformer (FFTR) module, constructed based on the foundations of the transformer framework, to fuse the spatial information features of all LCFs. Subsequently, the abstract high-level spatial features obtained are fed into diverse machine learning classifiers, including random forest (RF), extreme gradient boosting (XGBoost), gradient boosting decision trees (GBDT), categorical boosting (CatBoost), and extremely randomized trees (ET), to generate landslide susceptibility maps. Displacement velocity calculated by Persistent Scatterer Interferometric Synthetic Aperture Radar (PSInSAR) is incorporated in the LSM. The results demonstrate that FFTR-RF achieves premium performance in the area under the curve (AUC) (94%), accuracy (87.31%), precision (87.21%), recall (88.02%), and F1-score (87.61%). Incorporating displacement velocity into LSM results predicted by models enhances the comprehensiveness of LSM. These methods will furnish early warning systems for landslide disasters along the KKH, thus aiding recommendations for mitigating landslides' social and economic losses. [ABSTRACT FROM AUTHOR]

This research aimed to assess landslide susceptibility in the Badakhshan province of Afghanistan, an area highly susceptible to landslides due to its complex topography and geological conditions. Three distinct machine learning (ML) models, namely the Generalized Linear Model (GLM), Maximum Entropy (ME), and Random Forest (RF), were employed to identify the key contributing factors to landslide occurrences in the study region. The dataset used in this study consisted of landslide conditioning factors and a landslide inventory map. The conditioning factors encompassed lithology, soil type, plane curvature, profile curvature, elevation, slope, aspect, precipitation, land use/land cover (LULC), distance to fault, river, road, Normalized Difference Vegetation Index (NDVI), Topographic Wetness Index (TWI), Terrain Ruggedness Index (TRI), and Standardized Precipitation Index (SPI). The landslide inventory map contained 177 landslide locations and 65 non-landslide points obtained from Google Earth. Each machine learning (ML) model was trained and implemented independently using 70% of the training data, with the results validated against the remaining 30% of the landslide inventory dataset. Ensemble results from GLM, ME, and RF were obtained using the median approach. All three models exhibited consistent performance and identified similar landslide-prone areas. Among the various factors studied, proximity to rivers emerged as the most influential factor contributing to landslides, followed by the distance to roads and slope gradient. The study revealed that the districts of Argo and Yaftali Sufla, near Faizabad, were identified as particularly susceptible to landslides, especially in the vicinity of large valleys. Out of the total study area of 3086.4 km2, ∼2162 km2 were deemed relatively safe from landslides, while 149 km2 (representing 4.8% of the study region) were identified as highly susceptible to landslides. Area Under the Receiver Operating Characteristic Curve (AUC) and Root Mean Square Error (RMSE) statistics were used to evaluate the performance of the machine learning (ML) algorithms. The RF and ME models demonstrated the highest performance levels. This research contributes to our understanding of landslide susceptibility in Badakhshan province and can aid in implementing effective landslide risk management strategies in the region. [ABSTRACT FROM AUTHOR]

Landslides in high-order position areas pose a serious threat to residents located below such areas. Therefore, research on the evolution process and underlying dynamic mechanisms is crucial. The majority of relevant studies are based on landslides that have already occurred; however, the investigation of potential landslides is of higher value. In this study, a progressive potential landslide identification method is proposed with the application of SBAS-InSAR (Small Baseline Subset Interferometric Synthetic Aperture Radar) and subsequently combined with Google Earth, GF-1 (Gaofen-1), GF-2 (Gaofen-2), ZY-3 (Ziyuan-3) and UAV (Unmanned Aerial Vehicle) imagery, and DEM (Digital Elevation Model) for further validation. The proposed method is employed to determine the potential landslide in Tuanjiecun by integrating the multi-period data to simultaneously analyze the evolution and mechanism of the potential landslide. The long short-term memory method is then adopted to predict the evolution trend based on accumulative deformation from SBAS-InSAR. The results suggest that the deformation of Tuanjiecun potential landslide will increase to −238.57 mm. This article provides a new approach for disaster prevention and mitigation by determining a potential landslide using composite remote sensing and predicting the development of the potential landslide on indirect contact monitoring. [ABSTRACT FROM AUTHOR]

Landslide inventory maps are the first and main information level in studies of reactivation of previous landslides and susceptibility, hazard and risk maps of future landslides. Due to its geographical location and tectonic setting, the Dominican Republic is exposed to several geological risks, among which landslides stand out. In this study, we present a landslide inventory elaborated in three selected zones with particular geographical, lithological, tectonic and climatic characteristics: Puerto Plata, Santiago de los Caballeros and Jarabacoa. A total of 6085 landslides have been recorded (56% in S, 29% in P and 15% in J), which have been classified according to their geometry, propagation mechanism, affected material, thickness and activity. The detected factors that control the distribution and type of gravitational deposits are lithology and geologic structure, as well as rainfall and active faulting along the Camú, Septentrional and Bonao-La Guácara fault zones. [ABSTRACT FROM AUTHOR]

The study presents a landslide susceptibility assessment on a large scale in the City of Zagreb (Croatia). The susceptibility analysis was performed using the Weight of Evidence model in the pilot area (21 km² ) and applying the obtained weight values for each class of conditioning factors in the study area (130 km² ). The input data were LiDAR-based landslide inventory and six conditioning factors derived from 5 m LiDAR DTM, 5 m SfM DEM, and geological and land-use maps. The validation of the susceptibility assessment for the study area was evaluated with a ROC curve, which showed a high prediction rate (AUC = 84.4%), similar to the prediction rate for the pilot area (AUC = 86.9%). Based on the results, it can be concluded that the proposed method for large-scale landslide susceptibility assessment, where susceptibility conditions are defined in smaller pilot areas, can be applied to larger research areas with similar geomorphological and geological conditions. [ABSTRACT FROM AUTHOR]

A 1:1.45Million geologic map of NW Terra Cimmeria and SE Nepenthes Mensae (-5°31’–5°38’ N, 119°20’–129°55’ E) is presented here, displaying one of the highland-lowland transitional zones of Mars with greatest contrast and diversity. The Terra Cimmeria-Nepenthes Mensae Transitional Zone comprises two complex and poorly studied geologic domains bounded by the dichotomy escarpment. The map produced in this work displays nineteen geologic units and numerous geomorphic features. Detailed mapping, morphologic and morphometric analyses and Poisson model-derived ages have permitted to constrain the stratigraphy of this transitional zone and reconstruct more accurately its geological history from the Early Noachian to recent times, suggesting a significant period of fluvial activity within the Early Amazonian. The map provides insight into the processes involved in the origin and development of this transitional zone and offers a reference geologic framework for future studies in the region. [ABSTRACT FROM AUTHOR]

Timely and rational prediction of coseismic landslides is crucial for the design and development of key infrastructure capable to protect human lives in seismically active regions. This research introduces the novel Hybrid Coseismic Landslide Forecasting model (COLAFOS) that takes into consideration three parameters namely: The Average Slope of the Active Areas, the Slope Aspect and the types of Geological forms. The developed model was tested on two datasets from the island of Lefkada Greece, for years 2003 and 2015. COLAFOS is a hybrid model, employing the Fuzzy c-Means clustering, the Ensemble Adaptive Boosting (ENS_AdaBoost) and the Ensemble Subspace k-Nearest Neighbour (ENSUB k-NN) algorithms. The introduced model managed to correctly classify the coseismic landslides according to their severity, with a success rate of 70.07% and 72.88% for 2003 and 2015, respectively. The algorithm has shown very good performance for the classes of major severity, reaching an accuracy up to 92%. Accuracy, Sensitivity, Specificity, Precision and F-1 Score, were used to evaluate the performance of the model. Given the fact that this is a multi-class classification problem, 'One Versus All' Strategy was used in the evaluation process. Although the datasets were relatively unbalanced, the evaluation indices sealed the efficiency of the model. [ABSTRACT FROM AUTHOR]

Slope units represent surface slopes by means of polygons delimited by drainage and divide lines obtained on a digital topography. Objective slope unit delineation for a given digital elevation model is still an open issue and, often, a limitation that may dictate the use of a more traditional pixel-based approach for spatial analysis. Availability of slope unit maps facilitates many kinds of studies and allows scholars to focus on specific scientific issues rather than on preparing sound mapping units from scratch for their research. Here, we present a slope unit map of a large portion of the Himalayas. The map is prepared following a widely tested, parameter-free optimization algorithm. The area encompassed by the map is relevant to studies of the well-known 2015 Gorkha earthquake and monsoons, which makes it relevant to a vast portion of the scientific community working in natural hazards including, but not limited to, landslide scientists and practitioners. The map contains 112,674 polygons with average area of 0.38 km² and is published in vector form. The map is accompanied by a selection of data including morphometric and thematic quantities. In addition to describing the rationale behind the delineation of the polygonal map and selected data, we describe an application devoted to unsupervised terrain classification. We applied a k-means clustering procedure with two strategies: one at (coarser) basin scale and one at (finer) slope unit scale. We show similarities and differences between the two classification strategies, highlighting the role of the slope unit subdivision in the two cases. [ABSTRACT FROM AUTHOR]

In this research work, a constitutive mathematical review has been carried out to study the application of smoothed-particle hydrodynamics (SPH) in the modeling of geophysical flows like landslide, debris flows and stability failure problems across the world with particular focus on the landslide-associated geohazards happening in the southern region of Luxembourg. A particular interest is drawn to this region due to the research position taken by Stefan Van Baars suggesting the sufficiency of using friction angle of the geophysical flow-prone southern region to model landslide problems. This position invariably suggests that to model geophysical flows in any other region faced with this problem, friction angle data should have been enough. Meanwhile the present study has presented that climate factors and soil behavior variables beyond friction angle are required to solve geophysical flow problems and proposed the mathematical SPH technique as being the most efficient method to model geophysical flow problems due to its versatility and flexibility. Also, the (SPH) method can be used to simulate slope movements with large deformation and requires comprehensive profiling of the environmental and field data of the studied region. Generally, the present research has shown from previous efforts made to solve geophysical flow problems by using SPH, that groundwater flow, pore pressure changes and precipitation-induced infiltration are major factors causing these geohazards if unchecked. [ABSTRACT FROM AUTHOR]

Landslides are severe geographical activities that result in large quantities of rock and debris flowing down hill-slopes, leading to thousands of casualties and billions of dollars in infrastructure damage every year worldwide. For detecting landslides, on-site sensor systems are widely applied for data collection and many existing statistical process control methods can be adopted for modeling and monitoring. However, the conventional methods may perform poorly or even inapplicable when the sensors have different set-up times and end times, especially when the system includes newly deployed sensors with limited data collected. To make effective use of such new sensors immediately after deployment, we propose a novel multi-sensor based charting scheme for dynamic landslide modeling and monitoring by using transfer learning. A regularized parameter-based transfer learning approach integrated with the ordered LASSO is first proposed to effectively transfer information from old sensors with sufficient historical data to new ones with limited data. The approach considers the similarities not only between the autoregressive coefficients of different sensors, but also between the temporal correlation patterns. A control chart is then proposed for monitoring the newly deployed sensors sequentially based on the generalized likelihood ratio. Extensive simulation results and a real data example of landslide monitoring demonstrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Landslides inventories are crucial documents for extrapolating the spatial distribution of landslides that occurred in the past. However, the production of landslide hazards maps also needs deepening the knowledge on the temporal distribution of landslide activity. In this regard, historical maps and archive documents represent important sources of data and information on territory evolution through time and especially on landslides occurrence. Nevertheless, these kinds of data sources are rarely considered as they often must be interpreted integrating different disciplines. This work shows how historical and geomorphological data on landslides occurrence in an area of the Northern Apennines can be integrated to produce a synthesis map. This synthesis map put together analogic and digital information allowing to delineate the landscape evolution, in terms of spatial and especially temporal changes occurred in the study area representing a tool that could be useful for the landslide hazard assessment in territory management. [ABSTRACT FROM AUTHOR]

A comprehensive geomorphological and structural mapping of the largest landslides in the central-eastern Southern Alps (NE Italy) is presented. Their occurrence in this Alpine sector is discussed in relation with structural geological features (bedding and faults), which are largely conditioned by the Giudicarie and Schio-Vicenza regional fault systems. Tectonic rock damaging here coexists with high relief stored energy and active deformation, which is testified by present seismicity and cumulative earthquake moment release. Based on updated rock failure ages, slope activity is concentrated at 5-3 and 1 ka, as primarily enhanced by climatic and seismic controls. The analysis of predisposing and driving factors poses the question of present and future landslide hazards for these densely inhabited Alpine valleys. [ABSTRACT FROM AUTHOR]

Deep-seated gravitational slope deformation (DsGSD) represents a relevant geological-geomorphological feature of the landscape and has drawn scientific attention for the past several decades. During a detailed fieldwork in the Ligurian Alps, we identified areas (larger than ca 5 km2), characterized by very low percentage of outcrops, with rock blocks on the watershed and many morpho-tectonic evidences. Through the integration of geological-geomorphological mapping, structural data, photo interpretation, GIS-based analyses, we identified a number of typical DsGSDs. The example here described is one major complex-sagging-type DsGSD that is not present in national land planning and/or management databases but that is likely at the origin of the large-scale landslides which affected the villages of the area. This DsGSD is also located within the Beigua UNESCO Global Geopark, in an area of unique natural and historical value. This study once again highlights the importance of ground truth observations as the basis for further investigations. [ABSTRACT FROM AUTHOR]

Urban and small catchment landslides and floods are common types of hazards caused by intense rainfall. The detailed geomorphological mapping and analysis of the superficial hydrographic network are fundamental tools to assess the geo-hydrologically critical areas. In this study, the Feltrino Stream and Lanciano area (Adriatic coastal-hills) were investigated through a basin-scale and urban-scale geomorphological analysis – which incorporated temperature-rainfall and morphometric analyses, and the acquisition of geomorphological and hazard data, integrated with geomorphological fieldwork and mapping. The creation of a geodatabase and the data overlay led to assess the landslide–flood critical areas through geomorphology-based matrices. This study aimed to define the distribution of landslide–flood historical/recent events and related critical areas for the realization of an urban EWS, composed of a network of nine gauges, integrating the existing regional monitoring network. It defined a local alert system for landslide–flood and could support real-time communication for civil protection purposes. [ABSTRACT FROM AUTHOR]