Biswajeet Pradhan

University of Technology Sydney | UTS · Director, Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering & IT

Soil erosion by wind poses a significant threat to various regions across the globe, such as drylands in the Middle East and Iran. Wind erosion hazard maps can assist in identifying the regions of highest wind erosion risk and are a valuable tool for the mitigation of its destructive consequences. This study aims to map wind erosion hazards by deve...

Public safety is a massive concern for governments and civilians alike. Measures taken to ensure public safety range from urban and safe city planning at the government level to self-defence programs for the public. Among these measures, baggage checking and surveillance systems are already in place. The motivation for this paper is to enhance thes...

The early diagnosis of skin cancer has significantly improved with the use of computer-aided techniques and deep learning (DL) models. However, existing methods often struggle with issues of interpretability and adaptability, which are crucial for clinical application. To address these limitations, we employed a Multi-Task Learning (MTL) approach t...

As the sensor technology and reliability of the obstacle detection techniques advances automated driving is going to become the most pivotal technology that will be the birth of the next revolution in mobility industry. The impact of autonomous systems will no longer be limited to automobile industry, but to the mankind. Controversies, discussions,...

The human body can undergo changes in blood cell structure and characteristics when affected by contaminants. Examining the tiny images of blood cells helps identify potential infections or irregularities within the body, aiming to detect diseases. Accurately segmenting these cells significantly enhances disease detection, making it more precise an...

Skin lesions are a severe disease and the most predominant type of cancer worldwide. It is becoming more prevalent in modern society, with rising cases every year. The World Health Organization (WHO) claims melanoma is from the most severe forms of skin cancer, affecting well over 100,000 people worldwide each year. While early-stage lesions are fr...

Droughts are one of the most disastrous natural hazards, primarily due to their persistence and spatial distribution. Drought prediction is one of the key challenges for effective drought management and to do so, studies often involve the use of station-based data which are effective only in regions with high-gauge density. Therefore, there is grow...

Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope sat...

Robust soil ecosystems are crucial for sustaining life on our planet and enhancing agricultural output. Soil erosion poses a significant threat to the health of these ecosystems. Gully erosion is capable of breaking down the soil ecosystem and degrades the quality of water in rivers and wetlands. This study aims to map and predict gully erosion sus...

Landslide is a common hazard in Tamil Nadu’s Nilgiri district of. While much work on landslide susceptibility have been done worldwide, understanding society’s vulnerability to landslides, considering house structure and socio-economic conditions, remains lacking. This research presents landslide vulnerability mapping using advanced computing deep...

Drought monitoring is a critical environmental challenge, particularly in regions where irrigated agricultural intensification and urban expansion pressure water resources. This study assesses the impact of these activities on drought dynamics in Morocco’s Oum Er-Rbia (OER) watershed from 2002 to 2022, using the newly developed Watershed Integrated...

This study presents a dataset consisting of 268 retinal images from 179 individuals, including 133 left-eye and 135 right-eye images, collected from Natasha Eye Care and Research Institute in Pune, Maharashtra, India. The images were captured using a nonmydriatic Optical Coherence Tomography Angiography (OCTA) device, specifically the Optovue Avant...

This study presents a dataset consisting of 268 retinal images from 179 individuals, including 133 left-eye and 135 right-eye images, collected from Natasha Eye Care and Research Institute in Pune, Maharashtra, India. The images were captured using a nonmydriatic Optical Coherence Tomography Angiography (OCTA) device, specifically the Optovue Avant...

This paper introduces two novel class-specific fuzzy clustering algorithms: Mean-based Supervised Clustering (MSC) and Density-based Mean Supervised Clustering (DMSC). These algorithms are designed to construct the hidden layer of the Fuzzy Hypersphere Neural Network (FHNN) classifier, which is structured on the framework of the Radial Basis Functi...

Detecting psychological disorders, particularly depression, is a complex and critical task within the realm of mental health assessment. This research explores a novel approach to improve the identification of psychological distresses, such as depression, by addressing the subjectivity, complexity, and biasness inherent in traditional diagnostic te...

The prevalence of diabetic retinopathy (DR) among the geriatric population poses significant challenges for early detection and management. Optical Coherence Tomography Angiography (OCTA) combined with Deep Learning presents a promising avenue for improving diagnostic accuracy in this vulnerable demographic. In this method, we propose an innovative...

Large language models (LLMs) have transformed open-domain abstractive summarization, delivering coherent and precise summaries. However, their adaptability to user knowledge levels is largely unexplored. This study investigates LLMs’ efficacy in tailoring summaries to user familiarity. We assess various LLM architectures across different familiarit...

Vehicular traffic significantly contributes to economic growth but generates frictional noise that impacts urban environments negatively. Road traffic is a primary noise source, causing annoyance and interference. Traditional regression models predict two-dimensional (2D) noise maps, but this study explores the impact and visualization of noise usi...

This study explores the fusion of artificial intelligence (AI) and machine learning (ML) methods within anti–money laundering (AML) frameworks using data from the US Treasury’s Financial Crimes Enforcement Network (FinCEN). ML and deep learning (DL) algorithms—such as random forest classifier, elastic net regressor, least absolute shrinkage and sel...

This study aims to compare deep learning explainability (DLE) with explainable artificial intelligence and causal artificial intelligence (Causal AI) for fraud detection, emphasizing their distinct methodologies and potential to address critical challenges, particularly in finance. An empirical evaluation was conducted using the Bank Account Fraud...

The study focuses on intelligent driving, emphasizing the importance of recognizing nearby vehicles and estimating their positions using visual input from a single image. It employs transfer learning techniques, integrating deep convolutional networks’ features into a modified CenterNet model for six-degrees-of-freedom (6DoF) vehicle position estim...

This paper introduces an airborne object dataset comprising 22,516 images categorizing four classes of airborne objects: airplanes, helicopters, drones, and birds. The dataset was compiled from YouTube-8 M, Anti-UAV, and Ahmed Mohsen's dataset hosted on Roboflow. Videos were sourced from the first two platforms and converted into individual frames,...

Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult. Financial institutions use rule-based systems to detect suspicious money laundering transactions. However, it suffers from large false positives (FPs) that lead to operational efforts or mis...

The rockburst phenomenon in excavation endeavours reveals a multitude of complexities and obstacles that significantly impact both the technical and financial dimensions of project execution. Investigating critical rockburst factors in underground excavations is of considerable importance for addressing pivotal safety issues and operational complex...

Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, e...

In recent times, landslides have become a major concern in the southeast part of Bangladesh. This study aims to develop comprehensive landslide risk mapping by applying the analytical hierarchy process (AHP) and geospatial techniques in Ukhiya and Teknaf Upazilas, a highly populated Rohingya Refugee Settlement area. To assess the landslide risk, we...

Sign language is the primary form of communication for individuals with auditory impairment. In Bangladesh, Bangla Sign Language (BdSL) is widely used among the hearing-impaired population. However, due to the general public’s limited awareness of sign language, communicating with them using BdSL can be challenging. Consequently, there is a growing...

The edited book entitled 'Technology-Based Solutions for Sustainable Groundwater Management: Challenges, Models, and Strategies' explores innovative spatial tools and models to address the critical challenges of groundwater management, offering strategies for sustainable resource use across diverse ecological and societal contexts. This book addres...

This study aimed to compare the effectiveness of FAO-UNEP and MEDALUS models in evaluating desertification risk in the south of Nishabur, located in the northeastern part of Iran, covering an area of approximately 118,658 ha. To create the base maps for this study, satellite data of ETM+, field data, and maps from various regional administrative of...

Forest loss significantly contributes to climate change. As a result, scientists aim to understand its causes and evaluate forest loss classification maps. While Vision Transformers (ViTs) have demonstrated superior performance compared to convolutional neural networks (CNNs) in computer vision applications, they face challenges in recognizing and...

Recent advancements in deep learning (DL) have demonstrated potential for interpreting ground-penetrating radar (GPR) data, which is crucial for near surface geophysical investigations. However, the complexity of DL models and the challenge of interpreting their decision-making processes remain significant obstacles. This study addresses these chal...

This study aims to determine the crucial variables for predicting agricultural drought in various climates of Iran by employing feature selection methods. To achieve this, two databases were used, one consisting of ground-based measurements and the other containing six reanalysis products for temperature (T), root zone soil moisture (SM), potential...

Bangladesh is extremely vulnerable to sea-level rise and other climate-induced extreme events, such as flooding, storm surge, and salinity intrusion. The southwestern coastal region of Bangladesh is particularly vulnerable to salinity intrusion caused by cyclone induced storm surges and coastal floods. Salinity intrusion endanger land productivity...

Machine learning (ML) models have experienced remarkable growth in their application for multimodal data analysis over the past decade [1]. The diverse applications of ML models span domains such as medical image [2-4] and signal processing [5,6], remote sensing for earth observation and monitoring [7-9], the detection of daily human activities [10...

The prediction of suspended sediment load (SSL) within riverine systems is critical to understanding the watershed’s hydrology. Therefore, the novelty of our research is developing an interpretable (explainable) model based on deep learning (DL) and Shapley Additive ExPlanations (SHAP) interpretation technique for prediction of SSL in the riverine...

A severe threat to natural resources and human livelihood is groundwater scarcity. Therefore, mapping groundwater potentiality (GWP) is necessary for future resource management. In this article, a framework for conducting ensemble modeling is introduced. This framework is used to map GWP at the national level under the scenario of climatic variabil...

The increase in water demand and the scarcity of fresh water in arid regions have contributed to the depletion of groundwater. Artificial Groundwater Recharge (AGR) is an advanced strategy that contributes to combating water shortage issues. Limited efforts have been exerted to evaluate and demarcate AGR potential zones in the United Arab Emirates...

📢CALL FOR BOOK CHAPTERS (Springer): Machine Learning and AI Technology for Agricultural Applications Dear Colleagues. It is our pleasure to invite you to submit a chapter for inclusion in the book Machine Learning and AI Technology for Agricultural Applications: from Microscale Analysis to Regional Mapping published by Springer. This book will giv...

In leukemia diagnosis, automating the process of decision-making can reduce the impact of individual pathologists' expertise. While deep learning models have demonstrated promise in disease diagnosis, combining them can yield superior results. This research introduces an ensemble model that merges two pre-trained deep learning models, namely, VGG-1...

The increasing severity, duration, and frequency of destructive floods can be attributed to shifts in climate, infrastructure, land use, and population demographics. Obtaining precise and timely data about the extent of floodwaters is crucial for effective emergency preparedness and mitigation efforts. Deep convolutional neural networks (CNNs) have...

The application of Artificial Intelligence in various fields has witnessed tremendous progress in the recent years. The field of geosciences and natural hazard modelling has also benefitted immensely from the introduction of novel algorithms, the availability of large quantities of data, and the increase in computational capacity. The enhancement i...

The leaf area index (LAI) provides valuable input for modeling climate and ecosystem processes. However, ground-based observations are necessitated across various phenophases from dense tropical forests for a better understanding in terms of their contribution to carbon fixation. In this study, Digital Hemispherical Photography (DHP) was used for L...

Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western United States, particularly in Colorado, leading to extreme subsurface contamination issue. Given Colorado's arid climate and dependence on groundwater, an accurate assessment of AMD-induced contamination is deemed crucial. While in past, machine learning (ML)-...

Purpose The purpose of this study is to review the role of knowledge management (KM) in disaster management and crisis. Disaster causes many detrimental impacts on human lives through loss of life and damage to properties. KM has been shown to dampen the impact of the disaster on the utilization of knowledge among agencies involved and the local co...

The commonly used precipitation-based drought indices typically rely on probability distribution functions that can be suitable when the data exhibit minimal discrepancies. However, in arid and semi-arid regions, the precipitation data often display significant discrepancies due to highly irregular rainfall patterns. Consequently, imposing any prob...

Grass pollen is a globally prevalent allergen, known to trigger allergic reactions such as hay fever and asthma. Australia, in particular, exhibits one of the highest rates of asthma and hay fever prevalence and morbidity. In this study, we characterized the seasonal and inter-annual variations in grass pollen sources surrounding a pollen trap in S...

Water is a vital resource supporting a broad spectrum of ecosystems and human activities. The quality of river water has declined in recent years due to the discharge of hazardous materials and toxins. Deep learning and machine learning have gained significant attention for analysing time-series data. However, these methods often suffer from high c...

Vegetation constitutes the primary component of terrestrial ecosystems and plays a crucial role in examining global climate change and its impacts. Assessing vegetation dynamics over significant periods of time can provide critical information on changes in vegetation cover and regional climate. Satellite-based remote sensing products offer a uniqu...

Assessing groundwater potential for sustainable resource management is critically important. In addressing this concern, this study aims to advance the field by developing an innovative approach for Groundwater potential zone (GWPZ) mapping using advanced techniques, such as FuzzyAHP, FuzzyDEMATEL, and Logistic regression (LR) models. GWPZ was carr...

The Sustainable Development Goals (SDGs) and soil-related activities are closely intertwined. Healthy soil ecosystems promote the survival of life on Earth and uplift agricultural productivity. One of the issues that is putting healthy soil ecosystems at risk is soil erosion. The Central Highlands of Sri Lanka were chosen because it is a humid, tro...

Liveness face detection is essential for modern biometric systems, ensuring that input data is genuine and not derived from a false image or video. Liveness face detection in today’s biometric systems will ensure that input comes from a real, live person rather than a manipulated image or video. The novelty of this study lies in combining deep lear...

This paper focuses on exploring the potential of Climate resilient agriculture (CRA) for river basin-scale management. Our analysis is based on long-term historical and future climate and hydrological datasets within a GIS environment, focusing on the Ajoy River basin in West Bengal, Eastern India. The standardized anomaly index (SAI) and slope of...

Infection of leukemia in humans causes many complications in its later stages. It impairs bone marrow’s ability to produce blood. Morphological diagnosis of human blood cells is a well-known and well-proven technique for diagnosis in this case. The binary classification is employed to distinguish between normal and leukemiainfected cells. In additi...

As massive underground projects have become popular in dense urban cities, a problem has arisen: which model predicts the best for Tunnel Boring Machine (TBM) performance in these tunneling projects? However, performance level of TBMs in complex geological conditions is still a great challenge for practitioners and researchers. On the other hand, a...

Lung cancer is the leading cause of cancer death, and early diagnosis is associated with a positive prognosis. Chest X-ray (CXR) provides an inexpensive imaging mode for lung cancer diagnosis. Computer vision algorithms have previously been proposed to assist human radiologists in this task; however, leading studies use down-sampled images and comp...

Chat Generative Pre-trained Transformer (ChatGPT), developed by OpenAI, is a prominent AI model capable of understanding and generating human-like text based on input. Since terms and concepts of spatial information are contextual, the applications of ChatGPT on spatial information disciplines can be biased by the perceptions and perspectives of Ch...

Acid mine drainage (AMD) represents a global environmental crisis, rendering land and groundwater unsuitable for human use. The Western United States (WUS) has grappled with this environmental challenge for an extended period. Among the WUS regions, Colorado, known for its arid conditions, sustains oases and heavily relies on groundwater due to lim...

Tectonic and erosional processes that contribute to the gradual process of topographic uplift and erosion have shaped the Himalayan terrain. We used geomorphic proxies and previous published rates of erosion and exhumation across the Teesta River catchment to model the presence of transient segments. This helped us understand how the regional lands...

Paddy cultivation in Malaysia plays a crucial role in food production, with a focus on improving crop quality and quantity. With current national self-sufficiency levels ranging between 67 and 70%, the Malaysian government intends to produce higher-quality crops and boost agricultural production. However, the prominent paddy-producing state of Keda...

Acid Mine Drainage (AMD) is a persistent environmental issue in the Western United States (WUS), causing subsurface contamination that makes land and groundwater unfit for human use. Advances in computer processing and geophysical data have enabled Machine Learning (ML)-based inversion algorithms to automatically reconstruct ground electrical prope...

Road network extraction is a significant challenge in remote sensing (RS). Automated techniques for interpreting RS imagery offer a cost-effective solution for obtaining road network data quickly, surpassing traditional visual interpretation methods. However, the diverse characteristics of road networks, such as varying lengths, widths, materials,...

The primary goal of this research is to see how effective cloud-based computing services such as Google Earth Engine (GEE) platform are at classifying multitemporal satellite images and producing high-quality land cover maps for the target year of 2020, with the possibility of using it on a larger-scale area such as metropolitan Melbourne as a test...

Accurate vegetation analysis is crucial amid accelerating global changes and human activities. Achieving precise characterization with multi-temporal Sentinel-2 data is challenging. In this article, we present a comprehensive analysis of 2021's seasonal vegetation cover in Greater Sydney using Google Earth Engine (GEE) to process Sentinel-2 data. U...

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Gully erosion possess a serious hazard to critical resources such as soil, water, and vegetation cover within watersheds. Therefore, spatial maps of gully erosion hazards can be instrumental in mitigating its negative consequences. Among the various methods used to explore and map gully erosion, advanced learning techniques, especially deep learnin...

Debris flows are geomorphological processes that affect the landscape evolution process of any region. In this study, an integrated methodology is proposed to identify the chance of further debris flows and quantify the similarities between debris flow locations, materials and rheology, using field and laboratory investigations and remote sensing d...