Kushanav Bhuyan

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• Doctor of Philosophy
• Research Fellow at University of Padua

Mapping landslide-depleted source areas is pivotal for refining predictive models and volume estimations, yet these critical regions are often conflated with the landslide runouts, leading to sub-optimal assessments. The source areas are typically the regions where the actual failure occurs, providing crucial information on the initiation mechanism...

Landslide susceptibility maps serve as the basis for hazard and risk assessment, as well as risk-informed land use planning at various spatial scales. Researchers create these maps aiming to fulfil a variety of purposes, including infrastructure planning and restrictive land use zoning. These applications require accurate and specific information t...

On April 2nd, 2024, a Mw 7.4 earthquake struck Taiwan’s eastern coast, triggering numerous landslides and severely impacting infrastructure. To create the preliminary inventory of earthquake-induced landslides in Eastern Taiwan (3,300 km2) we deployed automated landslide detection methods by combining Earth Observation (EO) data with Artificial Int...

Efficient response to large and widespread multiple landslide events (MLEs) demands rapid and effective landslide detection. Despite extensive efforts using optical remotely sensed imagery, limitations in global, day & night, and all-weather operational capabilities remain. To address these gaps, we introduce an approach that harnesses Deep Neural...

The death toll and monetary damages from landslides continue to rise despite advancements in predictive modeling. These models’ performances are limited as landslide databases used in developing them often miss crucial information, e.g., underlying movement types. This study introduces a method of discerning landslide movements, such as slides, flo...

Rapid response to multiple landslide events (MLEs) demands accurate, all-weather, day-and-night detection capabilities. Optical remote sensing has advanced landslide detection but remains limited under adverse weather and lighting conditions. Synthetic Aperture Radar (SAR), resilient to these constraints, remains underexplored for automated landsli...

Mapping landslide-depleted source areas is pivotal for refining predictive models and volume estimations, yet these critical regions are often conflated with the landslide runouts, leading to sub-optimal assessments. The source areas are typically the regions where the actual failure occurs, providing crucial information on the initiation mechanism...

The risks associated with ground displacements, such as landslides, subsidence, sinkholes, and liquefaction, are on the rise due to climate change. To effectively address these issues, it is crucial to enhance existing methodologies for fundamental tasks like data collection, monitoring, modeling, and prediction. Although remote sensing and Earth o...

Purpose: Landslide is a well-known natural hazard, responsible each year for a high number of casualties and economic losses. Scientific community is taking huge efforts for landslide risk reduction and one of the most popular strategies is the setup of early warning systems (EWS). Local EWS can take advantage of monitoring data or physical modelin...

Purpose: The development of accurate early warning systems for landslides is critical for reducing the risk of fatalities and economic losses. Machine learning techniques, specifically Deep Learning (DL) models, have demonstrated exceptional predictive capabilities for this purpose. Although comparisons of DL models for landslide prediction have ma...

Purpose: Landslide inventories are essential for landslide susceptibility mapping, hazard modelling, and risk mitigation management. However, manual inventories have limitations such as subjective delineation of landslide borders due to the applied methodology, expert preferences, and available resources. To overcome these limitations, recent resea...

Purpose: Landslides are hazardous mass movements that can be induced by various factors such as lithology, morphology, soil properties, hydrology, and seismicity. To mitigate their impacts, we need to understand their failure mechanisms in diverse contexts and scenarios as this would allow us to improve existing predictive models that generally gro...

Despite significant advancements in predictive modeling, the death toll and monetary damages caused by landslides continue to escalate. A critical limitation in enhancing the predictive capability of these models is the 'incomplete' nature of landslide databases. Specifically, these databases often lack essential details such as the types of landsl...

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...

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...

Understanding the process of landslide failure is crucial for predicting and minimizing the consequences of landslides. Landslide failure can be caused by a variety of factors, including geology, topography, and soil conditions, while environmental triggers such as precipitation and earthquakes initiate the movement. We can better understand the ri...

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 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...

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...

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...

The mapping and characterisation of building footprints is a challenging task due to inaccessibility and incompleteness of the required data, thus hindering the estimation of loss caused by natural and anthropogenic hazards. Major advancements have been made in the collaborative mapping of buildings with platforms like OpenStreetMap, however, many...

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...

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...

Multi-temporal landslide inventories are crucial for understanding the changing dynamics and states of activity of landslide masses. However, mapping landslides over space and time is challenging as it requires lots of time and resources to delineate landslide bodies for affected areas. With the current advances in artificial intelligence models an...

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...

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...

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...

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 Himalayan rivers are vulnerable to devastating flooding caused by landslides and outbreak of glacial lakes. On 7 February 2021, a deadly disaster occurred near the Rishi Ganga Hydropower Plant in the Rishi Ganga River, killing more than 100 people. During the event, a large volume of debris and broken glacial fragments flooded the Rishi Ganga R...

The Himalayan regions are vulnerable to all kinds of natural hazards. On 7 February 2021, a deadly disaster occurred near the Tapovan, in Uttarakhand, Himalayas. During the event, large volume of debris along with broken glacial fragments flooded the Rishi Ganga River and washed away the nearby hydropower plants (Rishi Ganga and Tapovan), which was...

Accurate elements-at-risk data (EaR) are one of the most important components to estimate the loss to both natural and anthropogenic hazards, particularly because of the potential increased exposure to these hazards, due to rapid urbanisation and poorly planned development strategies in hazardous regions. Therefore, it is important to not only map...

The high-resolution multi-temporal PlanetScope image of 7 February 2021 clearly shows the fall of a large part of the Nanda Ghunti glacier (Uttarakhand) down in the base of the valley from a height of about 2000 m. The recorded seismic signals at the local seismic networks, close to the Joshimath station, show the occurrence of the fall of the firs...

The Himalayan rivers are glacier-fed and are vulnerable to devastating flash floods caused by damming of landslides and outbreak of glacial lakes. On 7 February 2021, around 10:30 am IST, a huge block of glacier mass broke from the Nanda Ghunti glacier. It is evident from the multi-temporal satellite imageries from Planet Scope that snow dust depos...