Helmi ShafriUniversiti Putra Malaysia | UPM · Department of Civil Engineering
Helmi Shafri
BSc (RMIT, Australia), PhD (Nottingham, UK)
Sleeping
About
290
Publications
221,457
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
6,449
Citations
Introduction
Major research interest - Urban remote sensing using hyperspectral data and high resolution satellite systems, wavelets applications in remote sensing, remote sensing algorithm assessments, agro/vegetation-geoinformatics via remote sensing techniques.
Additional affiliations
April 2011 - present
Publications
Publications (290)
Impervious surface discrimination and mapping are important in urban and environmental studies. Confusion in discriminating urban materials using multispectral systems has led to the use of hyperspectral remote sensing data as an effective way to improve urban analysis. However, the high dimensionality of these data needs to be reduced to extract s...
Image classification of roofing types, road pavements, and natural features can assist land-cover maps in further examining the effects of such features on health, pollution, and the microclimate in urban settings. Airborne hyperspectral sensors with high spectral and spatial resolutions can be employed for detailed characterization of urban areas....
Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (...
The processing of remotely sensed data includes compression, noise reduction, classification, feature extraction, change detection and any improvement associated with the problems at hand. In the literature, wavelet methods have been widely used for analysing remote sensing images and signals. The second-generation of wavelets, which is designed ba...
Existing vegetation indices and red-edge techniques have been widely used for the assessment of vegetation status and vegetation health from remote-sensing instruments. This study proposed and applied optimized Airborne Imaging Spectrometer for Applications (AISA) airborne hyperspectral indices in assessing and mapping stressed oil palm trees. Six...
To obtain seasonable and precise crop yield information with fine resolution is very important for ensuring the food security. However, the quantity and quality of available images and the selection of prediction variables often limit the performance of yield prediction. In our study, the synthesized images of Landsat and MODIS were used to provide...
In the past, the monitoring of crops in the agriculture sector was done manually. However, this approach is inconvenient as it consumes time, energy, and money. Various vegetation indices obtained through remote sensing data are utilized to monitor vegetation development. One main factor affecting the oil palm's production and health is its age. Th...
Extracting building footprints from extensive very-high spatial resolution (VHSR) remote sensing data is crucial for diverse applications, including surveying, urban studies, population estimation, identification of informal settlements, and disaster management. Although convolutional neural networks (CNNs) are commonly utilized for this purpose, t...
The spatial assessment criteria system for hybridizing renewable energy sources, such as hybrid solar-wind farms, is critical in selecting ideal installation sites that maximize benefits, reduce costs, protect the environment, and serve the community. However, a systematic approach to designing indicator systems is rarely used in relevant site sele...
The dual use of wind and solar energy holds great promise for low-cost and high-performance green infrastructure. However, for such hybrid systems to operate successfully, comprehensive and simultaneous dimensional planning is required, a goal that single-perspective assessment approaches fail to attain. This paper proposes a novel SpatioTemporal D...
Integrating agricultural production with the identification and use of groundwater resources has been shown to reduce multidimensional poverty in semi-arid regions. Poverty reduction and socioeconomic growth depend on sustainable water supply, especially in developing countries with limited rainy seasons. Poverty eradication is a top priority among...
Currently, remote sensing has been used extensively in the agriculture industry for oil palm monitoring due to their large plantation area. Oil palm monitoring can be done by performing land cover classification using various classification methods and machine learning algorithms. This study was conducted to perform oil palm mapping using WorldView...
The feasibility of classifying soybean frogeye leaf spot (FLS) has been investigated with the advance of hyperspectral technology. Hyperspectral reflectance data of healthy and FLS disease soybeans were used. The first step was to smooth out the data by using a filtering technique namely Savitzky-Golay to eliminate the noise of the spectrum. In ord...
In this paper, a novel approach to develop groundflash density (GFD) maps based on six years of lightning flashdata from 2012 to 2017 in Peninsular Malaysia. By leveragingadvanced Geographical Information System (GIS) software anda detailed Peninsular Malaysia basemap, the GFD maps weredeveloped to gain a better understanding of lightningdistributi...
This research proposes a ‘temporal attention’ addition for long-short term memory (LSTM) models for dengue prediction. The number of monthly dengue cases was collected for each of five Malaysian states i.e. Selangor, Kelantan, Johor, Pulau Pinang, and Melaka from 2011 to 2016. Climatic, demographic, geographic and temporal attributes were used as c...
This research aims to predict dengue fever cases in Malaysia using machine learning techniques. A dataset consisting of weekly dengue cases at the state level in Malaysia from 2010 to 2016 was obtained from the Malaysia Open Data website and includes variables such as climate, geography, and demographics. Six different long short-term memory (LSTM)...
Land use and land cover changes driven by urban sprawl has accelerated the degradation
of ecosystem services in metropolitan settlements. However, most optimisation techniques do not
consider the dynamic effect of urban sprawl on the spatial criteria on which decisions are based.
In addition, integrating the current simulation approach with land us...
This research uses the Python language to model the Gaussian Plume equation in Quantum Geographic Information System (QGIS) to estimate the contaminants released from the cement plant. Spline interpolation and the maximum likelihood (ML) classification process are used to extract wind speeds and land cover classes. The primary and secondary directi...
The reliable and efficient large-scale mapping of date palm trees from remotely sensed data is crucial for developing palm tree inventories, continuous monitoring, vulnerability assessments, environmental control, and long-term management. Given the increasing availability of UAV images with limited spectral information, the high intra-class varian...
Researchers are interested in developing techniques to monitor, manage and predict
the risks of gases and particles emitted from cement factories, which have a direct and negative impact on human health. Deep learning (DL) is a critical component of data mining, which further involves statistics and prediction. In this study, we developed a deep le...
Vegetation health is an essential indicator in the global hydrologic cycle as it is interrelated with the hydrological components. In tropical areas where vegetation dominates, analysing their correlation at a regional scale helps forecast the hydrologic cycle and understand vegetation’s response to climate change. However, the interactions between...
Climate change, population growth and urban sprawl have put a strain on water supplies across the world, making it difficult to meet water demand, especially in city regions where more than half of the world's population now reside. Due to the complex urban fabric, conventional techniques should be developed to diagnose water shortage risk (WSR) by...
Waves are the driving force that shapes the coastline. Precise prediction of wave height and period is of great importance in many coastal studies. Accurate wave parameters are required to execute coastal activities such as merchant vessel routing, offshore drilling, coastal protection works, naval operations more efficiently and safely. This study...
The construction sector is one of Yemen’s most important economic pillars. Building information modelling (BIM) is a new information technology implementation that can create an intelligent digital design of buildings to support a variety of tasks and provides a wide range of benefits throughout the project life cycle. However, BIM is not widely em...
Crop yield estimates are affected by various factors including weather, nutrients and management practices. Predicting yields on a large scale in a timely and accurate manner by considering these factors is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme cli...
During the past decade, deep learning-based classification methods (e.g., convolutional neural networks—CNN) have demonstrated great success in a variety of vision tasks, including satellite image classification. Deep learning methods, on the other hand, do not preserve the precise edges of the targets of interest and do not extract geometric featu...
In recent years, deep learning-based image classification has become widespread, especially in remote sensing applications, due to its automatic and strong feature extraction capability. However, as deep learning methods operate on rectangular-shaped image patches, they cannot accurately extract objects’ boundaries, especially in complex urban sett...
Performance of building construction was pointed over the past years to low standards of information management, which depend on project complexity. BIM application in building projects is generally seen as a sophisticated environment in the Yemeni construction industry, leading to cost and time overrun, labor productivity and poor design. This stu...
In the fast-expanding construction industry worldwide, building information modelling (BIM) is a robust process. However, to date, developing countries are not very well adopting the techniques proven to help significantly produce effective management of construction projects. This study reviews numerous current studies conducted on the challenges...
Building information extraction utilizing remote sensing technology has vital applications in many domains, such as urban planning, cadastral mapping, geographic information censuses, and land-cover change analysis. In recent years, deep learning algorithms with strong feature construction ability have been widely used in automatic building extract...
Field data collection and geospatial map generation are critical aspects in different fields such as road asset management, urban planning, and geospatial applications. However, one of the primary impediments to data collection is the availability of spatial and attribute data. This issue is aggravated by the high cost of conventional data collecti...
Desertification is a major environmental issue all over the world, and Al-Khidhir district, Al-Muthanna, in the south of Iraq is no exception. In mapping, assessing, and predicting desertification, remote sensing and geospatial solutions (spatial analysis, machine learning) are crucial. During 1998–2018, this study employed satellite images from La...
Research on the underground filling mining of Cemented Paste Backfilling (CPB) slurry shows that it alleviates the underground subsidence and environmental problems caused by mining, making a strong contribution to the realization of green mining. To optimize performance, cost reductions, and the recycling of agricultural solid waste, some research...
Predicting yields on a bigger scale in a timely and accurate manner is essential for preventing climate risk and ensuring food security, particularly in the light of climate change and the escalation of extreme climatic events. Furthermore, crop yield estimates are affected by various factors including weather, nutrients and management practices. I...
An assessment of site suitability for wind and solar plants is a strategic step toward ensuring a low-cost, high-performing, and sustainable project. However, these issues are often handled on a local scale using traditional decision-making approaches that involve biased and non-generalizable weightings. This study presents a global wind and solar...
With the development of coal mining, the use of elbows has diversified the forms of underground backfill pipelines, which has inevitably complicated the transportation characteristics of filling slurry in the pipeline, thus affecting the entire backfilling system. The objective of this study is to numerically investigate the running state of cement...
Although the degradation of Iraqi water sources has been a persistent problem for decades, it has become a full-blown crisis since a few years ago. The water levels in both rivers, Euphrates and Tigris, have declined by 60%. This paper aims to detect and monitor the hydrological systems of Mesopotamia marsh in Dhi-Qar province, located in the south...
Nowadays, there are various techniques and methods used in land cover classification using remote sensing data especially in oil palm monitoring. This study discussed the oil palm mapping using satellite imagery (Sentinel-2) and classification of land cover features using machine learning algorithms such as linear support vector classifier (LSVC),...
Riverine plastic pollution has received worldwide attention due to numerous challenges associates with it. This study is premised on the need to reduce plastic leakage from land-based sources into the ocean. Geospatial technology was used to model plastic leakage in Sungai Pinang, Pulau Pinang. This study proposed a citizen-based approach because t...
Malaysia has a significant amount of biodiversity and has developed protected areas to conserve and sustain this tremendous degree of biodiversity. A protected area is known for its recognized natural, ecological or cultural values. However, the protected areas confront many obstacles, including poor or non-existent management plans. Remote sensing...
Despite recent advances in reducing poverty across the world, millions of people remain on the verge of or already living in severe multidimensional poverty especially in third world nations. The poor in Nigeria suffer from a feeling of voicelessness, helplessness, mistreatment, a complete lack of capacity to influence important choices that impact...
Cement is widely used for building construction and infrastructural purposes. In the last thirteen years, this productâs global annual production has increased by 78 percent, to 4,200 Mtpa. However, cement factories have been found to be harmful to air quality, plantations, and houses. Dust emission occurs through all the stages of cement product...
Globally, urbanisation has been the most significant factor causing land use and land cover changes due to accelerated population growth and limited governmental regulation. Urban communities worldwide, particularly in Iraq, are on the frontline for dealing with threats associated with environmental degradation, climate change and social inequality...
Though hyperspectral remote sensing images contain rich spatial and spectral information, they pose challenges in terms of feature extraction and mining. This paper describes the integration of a dimensionality reduction technique that employs spectral attention and Hybrid Spectral Networks (HybridSN) with spatial attention for hyperspectral image...
The implication of artificial intelligence (AI) is becoming popular in the field of Remote Sensing (RS) due to solving critical problems, like big data analysis, advanced classification algorithms, storage, etc. Cloud-based AI and remote sensing services allow users (with little technical background) to analyse imagery with high precision. This res...
Sustainable development on a global scale has been hindered by urbanization and water scarcity, but the greatest threat is from decision-makers ignoring these challenges, particularly in developing countries. In addition, urbanization is spreading at an alarming rate across the globe, affecting the environment and society in profound ways. This stu...
Accurate oil palm yield prediction is necessary to sustain oil palm production for food security and economic return. However, there are limited studies on comprehensive mapping and accurate oil palm yield prediction using advanced machine learning algorithms. Using multi-temporal remote sensing data, this paper proposed a new approach to predict o...
Considering the spatial–temporal variation of renewable energy (RE) resources, assessment of their complementarity is of great significance for decision-makers to increase the stability of power output and reduce the need for storage systems. In this regard, the current paper presents a roadmap to assess the temporal complementarity patterns betwee...
The detection of buildings in the city is essential in several geospatial domains and for decision-making regarding intelligence for city planning, tax collection, project management, revenue generation, and smart cities, among other areas. In the past, the classical approach used for building detection was by using the imagery and it entailed huma...
In recent decades, most of the natural and semi-natural ecosystems around the world have experienced excessive environmental loads due to human unsustainable activities that threaten their health and sustainability. Accordingly, the idea of ecosystem health has been proposed to monitor simultaneously the structure, function as well as the services...
Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and ass...
Carbon Dioxide (CO2) is one of the most significant factors that affecting the Air quality and the health of urban population. Air pollution is a serious challenge in all urban and semi-urban areas in Iraq. The air pollution in Baghdad is high owing to fact that due to tremendous increase in use of private, public and personal vehicles. Air Polluti...
finding a suitable site for landfill location is considered one of the main challenges that facing local governments and municipalities. This study presents an approach based on the implementation of the Fuzzy model and a traditional weighted-based approach to find an optimal site for a landfill. The proposed method was implemented based on five pa...
Recreational Sites are considered as a vital part of urban activities in most cities of the world.Generally,the concept of modernity and urbanism in contemporary urban centers is measured to some extent by the availability of recreational facilities for its residents. Recreational services contribute to providing a variety of investment opportuniti...
Large-scale mapping of date palm trees is vital for their consistent monitoring and sustainable management, considering their substantial commercial, environmental, and cultural value. This study presents an automatic approach for the large-scale mapping of date palm trees from very-high-spatial-resolution (VHSR) unmanned aerial vehicle (UAV) datas...
Automatic building extraction has been applied in many domains. It is also a challenging problem because of the complex scenes and multiscale. Deep learning algorithms, especially fully convolutional neural networks (FCNs), have shown robust feature extraction ability than traditional remote sensing data processing methods. However, hierarchical fe...
The widespread, severe negative impacts of human activities on Earth’s ecosystems over the past few decades have highlighted the importance of continuous and up-to-date monitoring of ecosystems health. On the other hand, it has been proven that the use of remote sensing technology in environmental studies can lead to accurate and reliable results w...
This study aims to evaluate the performance of state-of-the-art HybridSN deep learning algorithm versus standard machine learning (ML) and deep learning (DL) techniques using open-source Python libraries for producing hyperspectral land use and land cover (LULC) classification maps. Japanese Chikusei hyperspectral datasets captured by the airborne...
Land Use Land Cover (LULC) changes in the Banadir region are rapidly changing because of the increasing interaction of human activates with the environment as the population increases. However, there is no published evidence on this phenomenon. This study used multi-temporal Landsat images (1989, 2003, and 2018) to extract and evaluate LULC changes...
Information on rubber tree (Hevea brasiliensis) areas and stages of rubber tree growth is needed in making decisions to maximise land use and for efficient farm management. The use of conventional methods in collecting this information requires a long time, high costs, and constraints to access certain areas. Therefore, this study was conducted to...
The information on building features especially in the urban area is very important to support urban management and development. Nevertheless, the automated and transferable detection of building features is still challenging because of variations of the spatial and spectral characteristics to support urban building classification using remote sens...
Overpopulation growth and urban center crowd became a global phenomenon which mainly cause urban growth in both developed and developing countries cities including Nigeria. Understanding the extent and pattern of such growth required spatial information as well as earth observation data that will be analyse on a temporal dimension. Remote sensing d...
Assessment of rooftop rainwater harvesting (RRWH) quality and suitability requires detail and reliable information on roofs. Characterization of roof surface conditions affects the quality of harvested rainwater. Nevertheless, the implementation of the system requires improvement in terms of the roof detection techniques to ensure the roof of the b...
Geospatial Big Data is currently received overwhelming attention and are on highlight globally and Google Earth Engine (GEE) is currently the hot pot platform to cater big data processing for Remote Sensing and GIS. Currently few or no study regarding the usage of this platform to study land use/cover changes over years in Malaysia. The objective i...
Ponds locally called (Kududfi in Hausa) are either naturally or artificially created ditches which usually contained water and constitute significant elements of the settlement in Northern Nigeria which can be expanded beyond their natural depth. Many ponds in the urban centers of developing nations have inlets and outlets for transporting water fr...