Filippo Catani

University of Padova | UNIPD · Department of Geosciences

1] Catchment modeling in areas dominated by active geomorphologic processes, such as soil erosion and landsliding, is often hampered by the lack of reliable methods for the spatial estimation of soil depth. In a catchment, soil thickness h can vary as a function of many different and interplaying factors, such as underlying lithology, climate, grad...

Despite the large number of recent advances and developments in landslide susceptibility mapping (LSM) there is still a lack of studies focusing on specific aspects of LSM model sensitivity. For example, the influence of factors such as the survey scale of the landslide conditioning variables (LCVs), the resolution of the mapping unit (MUR) and the...

The magnitude of mass movements, which may be expressed by their dimension in terms of area or volume, is an important component of intensity together with velocity. In the case of slow-moving deep-seated landslides, the expected magnitude is the prevalent parameter for defining intensity when assessed as a spatially distributed variable in a given...

Strong earthquakes, especially on mountain slopes, can generate large amounts of unconsolidated deposits, prone to remobilization by aftershocks and rainstorms. Assessing the hazard they pose and what drives their movement in the years following the mainshock has not yet been attempted, primarily because multi-temporal landslide inventories are lac...

The recent development of mobile surveying platforms and crowdsourced geoinformation has produced a huge amount of non-validated data that are now available for research and application. In the field of risk analysis, with particular reference to landslide hazard, images generated by autonomous platforms (such as UAVs, ground-based acquisition syst...

The analysis of three-dimensional point cloud data is becoming one of the most used approaches to assess instabilities processes affecting rock slopes. With the increased collection of point cloud data, there is an increasing demand for rapid computational point cloud segmentation techniques to format data for rock fall risk analysis. However, ther...

Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth observation (EO) data, several gaps and uncertainties remain with developing models to be operational at...

Accurate early warning systems for landslides are a reliable risk-reduction strategy that may significantly reduce fatalities and economic losses. Several machine learning methods have been examined for this purpose, underlying deep learning (DL) models’ remarkable prediction capabilities. The long short-term memory (LSTM) and gated recurrent unit...

Quantifying landslide volumes in earthquake affected areas is critical to understand the orogenic processes and their surface effects at different spatio-temporal scales. Here, we build an accurate scaling relationship to estimate the volume of shallow soil landslides based on 1 m pre- and post-event LiDAR elevation models. On compiling an inventor...

Multiple landslide events happen frequently across the world. They have the potential to wreak significant harm to both human life and infrastructure. Although a substantial amount of research has been conducted to address the speedy mapping of landslides using optical Earth Observation (EO) data, significant gaps and uncertainties remain when enga...

Accurate landslide early warning systems are a trustworthy risk-reduction method that may greatly minimize human and economic losses. Several machine learning algorithms have been investigated for this goal, underlying the impressive potential in prediction capability of Deep Learning (DL) models. Despite this, the only DL models evaluated so far a...

Mapping landslides in space has gained a lot of attention over the past decade with good results. Current methods are primarily used to generate event inventories, but multi-temporal (MT) inventories are rare, even with manual landslide mapping. Here, we present an innovative deep learning strategy employing transfer learning. This allows our Atten...

Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address the mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational...

Following extreme climate events, a timely and detailed landslide mapping is necessary to determine which areas have been most affected and to support civil protection in rescue operations. Moreover, the monitoring of slope instabilities can lead to an appropriate hazard and risk assessment of the affected areas and to an effective design of remedi...

Landslide inventories are quintessential for landslide susceptibility mapping, hazard modeling, and risk management. Experts and organizations all across the world have preferred manual visual interpretation of satellite and aerial imagery for decades. However, there are other issues with manual inventory, such as the subjective process of manually...

The numerical simulation and slope stability prediction are the focus of slope disaster research. Recently, machine learning models are commonly used in the slope stability prediction. However, these machine learning models have some problems, such as poor nonlinear performance, local optimum and incomplete factors feature extraction. These issues...

Due to the similarity of conditioning factors, the aggregation feature of landslides and the multi-temporal landslide inventory, the spatial and temporal effects of landslides need to be considered in landslide susceptibility prediction (LSP). The ignorance of this issue will result in some biases and time-invariance in landslide susceptibility. He...

Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretat...

We present a regionally distributed slope stability modelling for shallow landslides considering the effect of plant roots. Our modelling is based on the physically-based distributed slope stability model, HIRESSS (HIgh REsolution Slope Stability Simulator). Thanks to the parallel structure, our code is well suited to perform fast assessment of slo...

The selection of non-landslide samples has a great impact on the machine learning modelling for landslide susceptibility prediction (LSP). This study presents a novel framework for studying the uncertainty of non-landslide samples selection on the LSP results through the slope unit-based machine learning models. In this framework, the non-landslide...

Quantifying landslide volumes in earthquake affected areas is critical to understand the orogenic processes and their surface effects at different spatio-temporal scales. Here, we build an accurate scaling relationship to estimate the volume of soil landslides based on 1 m pre-and post-event LiDAR elevation models. On compiling an inventory of 1719...

Several studies on empirical rainfall thresholds for landslide occurrence depend on the measurements of nearest rain gauges to the landslides, without taking in consideration the morphological and hydrological settings of the areas. Therefore, we introduce the DEWS (Distance, Elevation, Watershed, and Slope unit) QGIS software tool, for selecting r...

Mapping of landslides over space has seen an increasing attention and good results in the last decade. While current methods are chiefly applied to generate event-inventories, whereas multi-temporal (MT) inventories are rare, even using manual landslide mapping. Here, we present an innovative deep learning strategy which employs transfer learning t...

This study aims to reveal the impacts of three important uncertainty issues in landslide susceptibility prediction (LSP), namely the spatial resolution, proportion of model training and testing datasets and selection of machine learning models. Taking Yanchang County of China as example, the landslide inventory and 12 important conditioning factors...

We present a regionally distributed slope stability modelling for shallow landslides considering the effect of plant roots. Our modelling is based on the physically-based distributed slope stability model, HIRESSS (HIgh REsolution Slope Stability Simulator). Thanks to the parallel structure, our code is well suited to perform fast assessment of slo...

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide event...

Landslides are the most frequent and diffuse natural hazards in Italy causing the greatest number of fatalities and damage to urban areas. The integration of natural hazard information and social media data could improve warning systems to enhance the awareness of disaster managers and citizens about emergency events. The news about landslide event...

In this letter, we propose a Deep Learning (DL) based approach which exploits multispectral Sentinel-2 open-source data and a small-size inventory to map artisanal and small-scale mines (ASM). The study area is in central northern Burkina Faso (Africa) and is characterized by a semi-desert environment that makes mapping challenging. In sub-Saharan...

Multiple landslide events occur often across the world which have the potential to cause significant harm to both human life and property. Although a substantial amount of research has been conducted to address mapping of landslides using Earth Observation (EO) data, several gaps and uncertainties remain when developing models to be operational at...

Landslides are affected not only by their own environmental factors, but also by the neighborhood environmental factors and the landslide clustering effect, which are represented as the neighborhood characteristics of modelling spatial datasets in landslide susceptibility prediction (LSP). This study aims to innovatively explore the neighborhood ch...

To perform landslide susceptibility prediction (LSP), it is important to select appropriate mapping unit and landslide-related conditioning factors. The efficient and automatic multi-scale segmentation (MSS) method proposed by the authors promotes the application of slope units. However, LSP modeling based on these slope units has not been performe...

Repeated temporal mapping of landslides is essential for investigating changes in landslide movements, legacy effects of the landslide triggering events, and susceptibility changes in the area. However, in order to perform such investigations, multi-temporal (MT) inventories of landslides are required. The traditional approach of visual interpretat...

The uncertainty of non-landslide sample selection has a crucial influence on the landslide susceptibility prediction (LSP), which has not been thoroughly studied. In this study, a novel framework based on slope unit-based machine learning models is proposed to solve this issue. First, slope units are extracted by the multi-scale segmentation method...

Multiple landslide events are one of the most critical natural hazards. Landslide occurrences have become more frequent in recent decades because of rapid urbanization and climate change, causing widespread failures throughout the world. Extreme landslide events can cause severe damages to both human lives and infrastructures. Hence, there is a gro...

Early warning for complex landslides is a difficult task since their evolution could depend on the combination of various predisposing and triggering geological (e.g. rock type, water circulation) and climatic factors (e.g. rainfall, snowmelt). Depending on the type of phenomenon, the temporal evolution of a landslide can be monitored in several wa...

Landslide inventories are essential for landslide susceptibility mapping, hazard modelling, and further risk mitigation management. For decades, experts and organisations worldwide have preferred manual visual interpretation of satellite and aerial images. However, there are various problems associated with manual inventories, such as manual extrac...

In sub-Saharan Africa, artisanal and small-scale mining (ASM) represents a source of subsistence for a significant number of individuals. While 40 million people officially work in ASM across 80 countries, more than 150 million rely indirectly upon ASM. However, because ASM is often illegal, and uncontrolled, the materials employed in the excavatio...

Landslide susceptibility maps are often not validated after significant landslide events. In this work, we analyse the impact of the Vaia windstorm on landslide activity in Belluno province (Veneto Region, NE, Italy). The storm hit the area on October 27-30, 2018, causing 8,679 ha of damaged forests and widespread landslides. As shown in the case o...

Frequent and extreme meteorological events can lead to an increase in landslide hazard. A multi-temporal inventory plays an essential role in monitoring slope processes over time and forecasting future evolution. In recent years, the province of Belluno (Veneto Region, NE Italy) was affected by two relevant and intense meteorological phenomena that...

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important, as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (frequency ratio and evidence belief function) and two machine learning (ML) models (random forest and XGBoost; eXtreme Gradient Boosting) f...

Landslide hazard mapping is essential for disaster reduction and mitigation. The hazard map produced by the spatiotemporal probability analysis is usually static with false-negative and false-positive errors due to limited data resolution. Here we propose a new method to obtain dynamic landslide hazard maps over the Wushan section of the Three Gorg...

Landslides represent a serious worldwide hazard, especially in Italy, where exposure to hydrogeological risk is very high; for this reason, a landslide quantitative risk assessment (QRA) is crucial for risk management and for planning mitigation measures. In this study, we present and describe a novel methodological approach of QRA for slow-moving...

One of the main constraints in assessing shallow landslide hazards through physically based models is the need to characterize the geotechnical parameters of the involved materials. Indeed, the quantity and quality of input data are closely related to the reliability of the results of every model used, therefore data acquisition is a critical and t...

Multiple landslide events are common around the globe. They can cause severe damage to both human lives and infrastructures. Although a huge quantity of research has been shaped to address rapid mapping of landslides by optical Earth Observation (EO) data, various gaps and uncertainties are still present when dealing with cloud obscuration and 24/7...

The Three Gorges Hydropower Station is the largest hydropower station worldwide with the impoundment of the 660-km long reservoir. More than 500 landslides have been triggered by the reservoir water level fluctuation since the first impoundment in 2003. The classification of the reservoir affected landslide (seepage-driven and buoyancy-driven lands...

Event-based landslide inventories are essential sources to broaden our understanding of the causal relationship between triggering events and the occurring landslides. Moreover, detailed inventories are crucial for the succeeding phases of landslide risk studies like susceptibility and hazard assessment. The openly available inventories differ in t...

A promised potential of spaceborne interferometric synthetic aperture radars (InSAR) is a capability for regularly monitoring ground deformation with millimeter accuracy, for timely forecasting of impending natural hazards such as landslides. The main limitation in InSAR being actually capable of unleashing this potential for hazard prediction is t...

The patterns and controls of the transient enhanced landsliding that follows strong earthquakes remain elusive. Geostatistical models can provide clues on the underlying processes by identifying relationships with a number of physical variables. These models do not typically consider thermal information, even though temperature is known to affect t...

Nowadays, several systems to set up landslide inventories exist although they rarely rely on automated or real-time updates. Mass media can provide reliable info about natural hazard events with a relatively high temporal and spatial resolution. The news publication about a natural disaster inside newspaper or crowd-sourcing platforms allows a fast...

In this letter, we use deep-learning convolutional neural networks (CNNs) to compare the landslide mapping and classification performances of optical images (from Sentinel-2) and SAR images (from Sentinel-1). The training, validation and test zones used to independently evaluate the performance of the CNN1on different datasets are located in the ea...

Several territorial landslide early warning systems in different parts of the world are based on empirical rainfall thresholds for landslide triggering. The calculation of such thresholds, using rainfall measurements gathered from rain gauges, has been examined frequently, especially considering uncertainties, modeling complexity, spatial assumptio...

In the domain of landslide risk science, landslide susceptibility mapping (LSM) is very important as it helps spatially identify potential landslide-prone regions. This study used a statistical ensemble model (Frequency Ratio and Evidence Belief Function) and two machine learning (ML) models (Random Forest and XG-Boost) for LSM in the Belluno provi...

The concept of climate change has grown in recent decades, influencing the scientific community to conduct research on meteorological parameters and their variabilities. Research on global warming, as well as on its possible economic and environmental consequences, has spread over the last 20 years. Diffused changes in trends have been stated by se...

In the attempt of mitigating landslide risks, the capability of quantitatively assessing hazard, that is the probability of occurrence of a possibly damaging event in time and space, is fundamental. In this chapter, we will briefly review the main operational methods for the prediction and the forecasting of the time of occurrence of mass movements...