Masoud MahdianpariC-CORE and Memorial University of Newfoundland · Electrical Engineering
Masoud Mahdianpari
PhD
Cross-Appointed Professor
About
164
Publications
120,169
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Introduction
Masoud Mahdianpari is currently a Remote Sensing Technical Lead with C-CORE and a Cross-Appointed Professor with the Department of Electrical and Computer Engineering, Memorial University of Newfoundland. His research interests include remote sensing and image analysis, with a special focus on PolSAR image processing, multisensor data classification, machine learning, geo big data, and deep learning.
Additional affiliations
June 2019 - present
September 2015 - May 2019
September 2014 - September 2015
Cartographic Center
Position
- Instructor
Description
- Course: Digital Image Processing
Education
September 2015 - May 2019
September 2010 - September 2013
September 2006 - September 2010
Publications
Publications (164)
Abstract Wetlands are important ecosystems around the world, although they are degraded due both to anthropogenic and natural process. Newfoundland is among the richest Canadian province in terms of different wetland classes. Herbaceous wetlands cover extensive areas of the Avalon Peninsula, which are the habitat of a number of animal and plant spe...
Wetlands provide a wide variety of environmental services globally and detailed wetland inventory maps are always necessary to determine the conservation strategies and effectively monitor these productive ecosystems. During the last two decades, satellite remote sensing data have been extensively used for wetland mapping and monitoring worldwide....
Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution a...
Despite recent research into the Interferometric Synthetic Aperture Radar (InSAR) technique for wetland mapping worldwide, its capability has not yet been thoroughly investigated for Canadian wetland ecosystems. Accordingly, this study statistically analysed interferometric coherence and SAR backscatter variation in a study area located on the Aval...
Synthetic aperture radar (SAR) compact polarimetry (CP) systems are of great interest for large area monitoring because of their ability to acquire data in a wider swath compared to full polarimetry (FP) systems and a significant improvement in information content compared to single or dual polarimetry (DP) sensors. In this study, we compared the p...
Advancements in comprehending soil erosion alleviation, relevant to both natural terrains and urban settings, have experienced notable growth in knowledge and products. Nevertheless, the increasing influence of climate change-driven forces, extreme weather events, and human-caused actions have resulted in reduced attainment of the desired results i...
Methane (CH4) is one of the most significant greenhouse gases responsible for about one-third of climate warming since preindustrial times, originating from various sources. Landfills are responsible for a large percentage of CH4 emissions, and population growth can boost these emissions. Therefore, it is vital to automate the process of CH4 monito...
Potholes and other road surface damages pose significant risks to vehicles and traffic safety. The current methods of in situ visual inspection for potholes or cracks are inefficient, costly, and hazardous. Therefore, there is a pressing need to develop automated systems for assessing road surface conditions, aiming to efficiently and accurately re...
Accurate and efficient classification of wetlands, as one of the most valuable ecological resources, using satellite remote sensing data is essential for effective environmental monitoring and sustainable land management. Deep learning models have recently shown significant promise for identifying wetland land cover; however, they are mostly constr...
Wetland mapping is a critical component of environmental monitoring, requiring advanced techniques to accurately represent the complex land cover patterns and subtle class differences innate in these ecosystems. This study aims to address these challenges by proposing CVTNet, a novel deep learning (DL) model that integrates convolutional neural net...
Wildfires significantly threaten ecosystems and human lives, necessitating effective prediction models for the management of this destructive phenomenon. This study integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) modules to develop a novel deep learning model called CNN-BiLSTM for near-real-time wildf...
Monitoring methane emissions is crucial in mitigating climate change as it has a relatively short atmospheric lifetime of about 12 years and a significant radiative forcing impact. To measure the impact of methane-controlling policies and techniques, a deep understanding of methane emissions is of great importance. Remote sensing offers scalable ap...
To improve PolSAR imagery classification accuracy and address the current limitations of scarce training data in SAR image classification, we develop a 3D Generative Adversarial Network (3D GAN). Moreover, the possibility of generating 3D synthetic PolSAR data in two Benchmark datasets of San Francisco and Flevoland is evaluated. Achieved results i...
Wetlands are amongst Earth’s most dynamic and complex ecological resources, serving productive and biodiverse ecosystems. Enhancing the quality of wetland mapping through Earth observation (EO) data is essential for improving effective management and conservation practices. However, the achievement of reliable and accurate wetland mapping faces cha...
Coastal wetlands encompass diverse ecosystems such as tidal marshes, mangroves, and seagrasses, which harbor substantial amounts of carbon (C) within their vegetation and soils. Despite their relatively small global extent, these wetlands exhibit carbon sequestration rates on par with those observed in terrestrial forests. The application of remote...
The iceberg draft prediction is vital to mitigate the collision risk of deep keel icebergs with the seafloor-founded infrastructures, including the subsea pipelines, wellheads, hydrocarbon loading equipment, and communication cables crossing the Arctic and subarctic areas since the drifting icebergs may gouge the ocean floor and the physical and op...
The Arctic area is one of the best destinations for the development of oil and gas loading equipment. However, the recent
development of oil and gas facilities, including the submarine pipelines and wellheads crossing the Arctic area, has elevated
the need for more attention to iceberg draft (under-water height of icebergs) estimation during an ice...
Optical remote sensing of water quality poses challenges in small oligotrophic lakes due to the diminishing contribution of constituents to the water-leaving radiance as water clarity increases. For monitoring water clarity over such lakes, this study utilizes machine learning models and data from citizen science to develop effective models for ret...
With the rapid advancements in SAR systems aiming for operational capabilities, crop characterization using Compact-Polarimetric (CP) Synthetic Aperture Radar (CP-SAR) data has gained considerable attention. This study thoroughly assesses the potential usefulness of C-band SAR data in CP mode using the RADARSAT Constellation Mission (RCM) for crop...
Forest ecosystems have been persistently affected by wildfires, leading to significant damage worldwide. The severity and frequency of wildfires have escalated in recent years, necessitating more effective prediction models. This study presents an application of convolutional neural networks (CNNs) for wildfire spread prediction, focusing on the us...
Rapid impacts from both natural and anthropogenic sources on wetland ecosystems underscore the need for updating wetland inventories. Extensive up-to-date field samples are required for calibrating methods (e.g., machine learning) and validating results (e.g., maps). The purpose of this study is to design a dataset generation approach that extracts...
In the present study, the iceberg drafts and iceberg-seabed interaction process were simulated using the random forest regression (RFR)algorithm for the first time. Initially, utilizing the parameters governing the iceberg drafts and the iceberg-seabed interaction process in the sandy seabed, a set of RFR models were developed. To train and test th...
Nearly one-fifth of the Earth’s undiscovered hydrocarbons are reserved in the Arctic area whereas, the recent offshore oil and gas loading equipment, e.g., subsea pipelines, wellheads, and communication cables, developed in the Arctic waters has led to a considerable awareness of the iceberg draft prediction. The iceberg tip would gouge the ocean f...
Precise estimation of the iceberg draft may significantly reduce the collision risk of deep keel icebergs with the offshore facilities comprising the submarine pipelines, wellheads, communication cables, and hydrocarbon loading equipment crossing the Arctic shallow waters. As such, in this study, the iceberg drafts were simulated using a self-adapt...
Detailed wetland inventories and information about the spatial arrangement and the extent of wetland types across the Earth’s surface are crucially important for resource assessment and sustainable management. In addition, it is crucial to update these inventories due to the highly dynamic characteristics of the wetlands. Remote sensing technologie...
Recent offshore oil and gas loading facilities developed in the Arctic area have led to a considerable awareness of the iceberg draft approximation, where deep keel icebergs may gouge the ocean floor, and these submarine infrastructures would be damaged in the shallower waters. Developing reliable solutions to estimate the iceberg draft requires a...
The classifying of hyperspectral images (HSI) is a difficult task given the high dimensionality of the space, the huge number of spectral bands, and the small number of labeled data. As such, we offer a unique hyperspectral image classification methodology to address these issues based on sophisticated Multi-Layer Perceptron (MLP) algorithms. In th...
Mapping potential wetlands provides a promising approach to get such information rapidly, and thus is of great significance to understanding ecosystem sustainability and support wetland conservation and restoration. This study proposed a new processing pipeline to map potential wetlands in the Yangtze River Basin, the largest basin in China, by com...
An increasing availability of remote sensing data in the era of geo big-data makes producing well-represented, reliable training data to be more challenging and requires an excessive amount of human labor. In addition, the rapid increase in graphics processing unit (GPU) processing power has enabled the development of advanced deep learning (DL) al...
Effective monitoring of wetlands plays a pivotal role in comprehending and managing these ecologically vital ecosystems. This study assesses the potential of C-band Synthetic Aperture Radar (SAR) imagery in compact polarization (CP) mode, utilizing the RADARSAT Constellation Mission (RCM), for wetland characterization. We introduce the compact-pola...
Polarimetric synthetic aperture radar (PolSAR) images contain useful information, which can lead to extensive land cover interpretation and a variety of output products. In contrast to optical imagery, there are several challenges in extracting beneficial features from PolSAR data. Deep learning (DL) methods can provide solutions to address PolSAR...
Wetlands have long been recognized among the most critical ecosystems globally, yet their numbers quickly diminish due to human activities and climate change. Thus, large-scale wetland monitoring is essential to provide efficient spatial and temporal insights for resource management and conservation plans. However, the main challenge is the lack of...
Despite their importance to ecosystem services, wetlands are threatened by pollution and development. Over the last few decades, a growing number of wetland studies employed remote sensing (RS) to scientifically monitor the status of wetlands and support their sustainability. Considering the rapid evolution of wetland studies and significant progre...
Climate change and population growth risk the world’s food supply. Annual crop yield production is one of the most crucial components of the global food supply. Moreover, the COVID-19 pandemic has stressed global food security, production, and supply chains. Using biomass estimation as a reliable yield indicator, space-based monitoring of crops can...
Climate change-driven forces and anthropogenic interventions have led to considerable changes in coastal zones and shoreline positions, resulting in coastal erosion or sedimentation. Shoreline change detection through cost-effective methods and easy-access data plays a key role in coastal management, where other effective parameters such as land-us...
Many ecosystems, particularly wetlands, are significantly degraded or lost as a result of climate change and anthropogenic activities. Simultaneously, developments in machine learning, particularly deep learning methods, have greatly improved wetland mapping, which is a critical step in ecosystem monitoring. Yet, present deep and very deep models n...
Iceberg-seabed interaction that threatens subsea pipelines and structures is a challenging and costly engineering design aspect of Arctic offshore infrastructures. In this study, the sub-gouge soil deformation in the sand along with the keel reaction forces was simulated using Random Forest (RF) as a strong machine learning (ML) model and compared...
Convolutional Neural Networks (CNNs) have shown promising results in classifying complex remote sensing scenery, particularly in the classification of wetlands. State-of-the-art Natural Language Processing (NLP) algorithms, on the other hand, are transformers. In this paper, we illustrate the effectiveness of the cutting-edge Swin Transformer for t...
Recently, deep learning algorithms, specifically convolutional neural networks (CNNs), have played an important role in remote sensing image classification, including wetland mapping. However, one limitation of deep CNN for classification is its requirement for a great number of training samples. This limitation is particularly enhanced when the cl...
Wetlands are important ecosystems delivering goods and services for a number of important functions, including providing key habitats to many plants and animals, maintaining water quality and quantity (e.g., to control floods), offering food and recreational opportunities for humans, and acting as a carbon sink. They are also important components o...
Forest aboveground biomass (AGB) provides valuable information about the carbon cycle, carbon sink monitoring, and understanding of climate change factors. Remote sensing data coupled with machine learning models have been increasingly used for forest AGB estimation over local and regional extents. Landsat series provide a 50-year data archive, whi...
In Arctic offshore regions, the oil and gas hydrocarbons are transferred to the onshore basins through the subsea pipelines. However, the operational integrity of the subsea pipeline may be at risk of collision with traveling icebergs, which gouge the seabed in the Arctic shallow waters. Even though these sea bottom-founded structures are buried at...
Sea ice profoundly influences ocean circulation, the polar environment, biology, climate, and commercial activities. The rapidly changing sea ice environment and increased human activities in polar regions drive the demand for sea ice monitoring. Spaceborne synthetic aperture radar (SAR) has been widely adopted for sea ice sensing due to its all-we...
Providing an accurate above-ground biomass (AGB) map is of paramount importance for carbon stock and climate change monitoring. The main objective of this study is to compare the performance of pixel-based and object-based approaches for AGB estimation of temperate forests in north-eastern of New York State. Second, the capabilities of optical, SAR...
Forest above-ground biomass (AGB) estimation provides valuable information about the carbon cycle. Thus, the overall goal of this paper is to present an approach to enhance the accuracy of the AGB estimation. The main objectives are to: 1) investigate the performance of remote sensing data sources, including airborne light detection and ranging (Li...
Monitoring freshwater quality is a global concern because of
increasing harmful algal blooms (HABs). Therefore, it is
important to detect HABs especially in small lakes as they
hold great socioeconomic value. This study estimates the
potential of using Sentinel-2 for estimating chlorophyll-a
value in small inland lakes. In particular, this study us...
Shallow convolutional neural networks (CNNs) have successfully been used to classify polarimetric synthetic aperture radar (PolSAR) imagery. However, one drawback of the existing deep CNN-based techniques is that the input PolSAR training data are often insufficient due to their need for a significant number of training data compared to shallow CNN...
Wetland is one of the most productive resources on earth, and it provides vital habitats for several unique species of flora and fauna. Over the last decade, mapping and monitoring wetlands by utilizing deep learning (DL) models and remote sensing data gained popularity due to the importance of wetland preservation. In general, DL-based methods hav...
Seasonal variations result in hydrophytes and undrained hydric soil changes in wetland areas, which lead to a dynamic environment that makes wetland classification challenging. This study aims to explore the applicability of multi-seasonal Gray-Level Co-Occurrence Matrix (GLCM) texture-derived features for object-based wetland classification over l...
In wetland mapping, a lot of uncertainty is related to the task of selecting an appropriate classification approach. Although the individual models are available and well-established in the literature for the classification task, the combination approaches have become popular recently. Hence, selecting an appropriate method is challenging, whether...
The above-ground biomass (AGB) estimation monitoring provides a powerful tool for the assessment of carbon emission and sequestration. Using remote sensing technique is an environmentally friendly way of biomass estimation. Thus, this paper investigated optical (i.e. Landsat 8 OLI and Sentinel-2), synthetic aperture radar (SAR) (global phased array...
Carbon sequestration coupled with flood mitigation and other functions of wetlands, such as water filtration, coastal protection, biodiversity, and providing recreational spots, make wetland mapping and monitoring important for different countries. Google Earth Engine (GEE) cloud computing platform is becoming a very important tool for lots of envi...
Ice gouging is one of the major menaces to the subsea pipelines crossing the Arctic (e.g., Beaufort Sea) or the non-Arctic (e.g., Caspian Sea) shallow waters. Burial of the sea-bottom-founded infrastructures is regarded as a feasible method for protection of the subsea assets against the ice gouging threat. These pipelines are commonly embedded und...