bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reinventing Operation Through Visual Asset Management System & Digitalization
Authors: Masrom Mohd Nazri Bin , Rizal Afiq Bin Juazer , Kee Tham Siew , Mustafa Saiful Nizam Bin 
Published: July 2022

Abstract
As a data-driven organization, PETRONAS increases the demand for remote or autonomous operations on assets management. Visual Asset Management System (VAMS) is based on digital twin technology which digitalize asset inventory records and integrates multiple data sources that integrate engineering drawings, geo-tagged videos and 360° photographs with high intensity of spatial information in a cloud-based database. Intuitive dashboard showing the asset inventories allow asset management team to trace each inventory efficiently in a virtual reality model. A Proof-of-Concept (POC) of the VAMS was carried out on two newly installed offshore platforms. The findings showed that the centralized digital platform is effective for not only asset management but also production and maintenance operations. The POC results also demonstrated that VAMS minimize possible Health, Safety, Security and Environmental (HSSE) risks and exposure whereby site visit to the facility, safety induction, and HSSE walkabouts can be performed effectively in a safe virtual reality environment. This paper describes the POC and the implementation of VAMS for the two newly commissioned offshore facilities. It also discussed the technical challenges during the implementation, the perceive value creation, and the potentials and opportunities of the technology that we can leverage on to other fields or applications.
Coastal land-use mapping along Johore Straits using Sentinel 1-SAR data with maximizing parameterization of machine learning classification
Authors: Chen Shin Yi , Hashim Mazlan , Idris Nurul Hazrina 
Published: July 2022

Abstract
In tropical regions, cloud cover accounts for a major obstacle to detect the coastal land-use while adopting remote sensing technology. The advent of the latest Sentinel-1 C-band synthetic aperture radar (SAR) satellite provides advantages to collect data in all-weathered conditions. In this study, Sentinel-1 images are processed using Lee filter and GLCM-Mean texture analysis in order to enhance the classification results. Several sets of parameters have been tested and these resulted in the optimum overall accuracy by Neural Network with 79.00% in 2015 and 68.29% in 2019. In contrast, Support Vector Machine classifiers obtained overall accuracies of 77.44% and 71.26% in 2015 and 2019 respectively. The results were accessed and it is found that Support Vector Machine outperforms the Neural Network classifier in discriminating data with high heterogeneity properties. Besides, Support Vector Machine has more consistent results in parameter testing compared to Neural Network.
Estimating land surface temperature to study land use land cover influence on the temperature in a city with a dry arid climate
Authors: Albakri Naseeb Asaad , Al-Ruzouq Rami , Shanableh Abdallah , Zeiada Waleed , Hamdan Hamdan 
Published: July 2022

Abstract
Turning arid regions or forests into urban areas (Urbanization) is affecting the global temperature. This effect is intensifying due to uncontrolled urban growth. To understand the influence of land use land cover (LULC) on land surface temperature (LST) in dry arid climate cities like Sharjah, UAE, 2 Landsat 8 images were used to retrieve the LST, one in summer and one in winter by using the radiation correction equation in google earth engine. Supervised classification of a high-resolution image from worldview-3 will produce the LULC map. In addition, hourly ground temperature data (GT) is retrieved from 6 field stations to validate LST and find the correlation if it exists. The LST images were then compared to the different LULC, to find relationships between the two. This study showed that barren soil experienced the highest LST when compared to vegetation and urban areas, and vegetation areas experienced the lowest temperatures compared to all other LULC, finally, urbanization that was combined with green lands can have a positive impact on the temperature in cities with dry arid climates. This study proves that understanding the effects of urbanization on temperature accurately will aid greatly in the future planning of cities.
Interferometric synthetic aperture radar coherence constraints in heavily vegetated tropics
Authors: Chindo Musa Muhammad , Hashim Mazlan , Rasib Abdul Wahid 
Published: July 2022

Abstract
Success of Interferometry Synthetic Aperture Radar (InSAR) technique for deriving digital elevation model (DEM) in the heavily vegetated humid tropic using Sentinel-1 SAR has been controversial, owing to the difficulty in achieving good coherence. Yet achievable coherence with Sentinel-1 SAR over the heavily vegetated humid tropic has rarely been reported. Consequently, this paper evaluates coherence achievable with Sentinel-1 SAR over the heavily vegetated humid tropic. Twelve Sentinel-1 SAR over part of Johor selected based on perpendicular and temporal baselines considerations, were used as pairs of six reference and secondary images respectively and processed using Sentinel Application Platform (SNAP) to derive coherence maps and analysed accordingly. The result shows that regardless of image pairs baseline characteristics, coherence of above 0.6 over the heavily vegetated humid tropic with Sentinel-1 SAR can only be achieved in barely 10% of the study area. This result shall serve as eye-opener to geoscience community, especially SAR enthusiast looking forward to leveraging on SAR clouds penetrating ability; and Sentinel-1 SAR open access and short revisit circle to apply Sentinel-1 SAR in deriving DEM over heavily vegetated humid tropical environment. However, future studies shall focus on assessing consistency of good coherence patches and their usability in acquiring elevation.
Covid-19 pandemic induced spatio-temporal changes in human activity in the United Arab Emirates using night-time light data
Authors: El Traboulsi Y , Al-Ruzouq R , Hamad K , Shanableh A , Khalil M , Gibril M 
Published: July 2022

Abstract
The COVID-19 pandemic outbreak in early 2020 impacted people’s lives and the working environment and led to a drastic change in human activities. This has opened a window to analyze the people’s response to the safety measures implemented to contain the virus, which can be reflected and monitored using Night-time light (NTL) images. This study aims to demonstrate the use of NTL images captured by satellites to detect changes in human activity in the UAE before and during the pandemic. The study period will include the pandemic year and the previous years (2017-2019) will be used as a control. Raw NTL data was pre-processed to obtain cloud-free radiance images through which the monthly average radiance was calculated. The monthly average radiance is categorized into three classes: residential, commercial, and roads. The radiance levels during the lockdown were compared against the months prior to the restriction imposed. The results revealed that the roads category showed the highest decrease in radiance levels due to the enforcement of the safety measures, followed by the commercial category, whereas the least reduction was observed for the residential category. The results show how NTL radiance could be used in monitoring the changes in human activities.
Spatial Information for Disaster Risk Management: The Constraints, Limitations and Opportunities from JUPEM Perspective
Authors: Hassan H , Abdul Rahman S A F S , Ahmad F 
Published: July 2022

Abstract
The age of spatial information has undoubtedly unlocked the potential in visualising, investigating, and forecasting natural disasters, especially for policymakers. From this viewpoint, the implementation of spatial data has drastically transformed the traditions through which human civilisations embrace natural disaster management policies to reduce loss of life and economic losses. Unfortunately, each natural disaster is harmed varied and has different impacts locally. The various environmental and climate factors for mega-landform diversity countries like Malaysia play a crucial role in causing natural disasters and limiting the agency’s ability to obtain geomorphological spatial information during and after the incident. Therefore, this paper will analyse the constraint faced by Department of Survey and Mapping Malaysia (JUPEM) in providing spatial data for stakeholders based on a case study of natural disasters in Gunung Jerai, Kedah. All six (6) identified constraints, including time, climate, system, data, equipment, and human constraints, were examined. The opportunities to enhance the existing JUPEMs’ policies for natural disasters were highlighted from the JUPEM perspective. The prospects for empowering Subject Matter Expert (SMEs), equipment maintenance and improvement, annual disaster preparation, and enhancement of JUPEMs’ Peraturan Tetap Operasi (PTO) for disaster management were discussed in this paper.
Asset Management in Malaysia: Current Status
Authors: Nasir Amalin A’ishah Mohd , Azri Suhaibah , Ujang Uznir 
Published: July 2022

Abstract
Asset management is a rapidly growing practice in response to changes in urban development and becoming one of the important practices in Malaysia. As a result, the Malaysian government has raised awareness of asset management to manage and maintain government assets. Thus, the Malaysian government has established MySPATA, a fixed asset management system used by all government ministries to help agencies standardize, manage, and monitor fixed assets. The four asset management documents used in MySPATA are the Government Asset Management Policy (DPAK), Government Comprehensive Asset Management Manual (MPAM), Government Immovable Asset Management Procedures (TPATA), and Immovable Asset Management System. Asset management is concerned mainly with operating assets in Malaysia, including their management, monitoring, maintenance, and disposal. Government agencies are charged with supervising governments and enabling the delivery of public services. Thus, asset management policies and processes are critical for delivering and assisting in effective asset management. Asset management must be effective and efficient to ensure that the assets provided can be handled and utilized for the intended purpose. This study examines Malaysia’s current asset management practices and identifies the advantages and disadvantages of the current approach.
Critical success factors for geospatial data sharing in disaster management
Authors: Valachamy Mageshwari , Sahibuddin Shamsul , Ahmad Nur Azurati , Bakar Nur Azaliah Abu 
Published: July 2022

Abstract
Globally, the rise of disasters has caused billions of dollars lost each year. These include the loss of properties, life and has created a negative impact on socioeconomic level of a country. Currently, geospatial datasets are becoming crucial for situational awareness and management of disasters. The timely and accurate information on disastrous occurrences must be collected, maintained, and managed for efficient management of emergency. These geospatial datasets are from different data provider agencies. Thus, there is a need to focus on the geospatial data sharing that would benefits the authorities in decision making. This initiative entails high commitment and collaboration from the data provider agencies, which can be achieved through the sharing of geospatial datasets approach. This study aims to identify the critical success factors of geospatial data sharing in the context of natural disaster. A preliminary review, focus group discussion and interviews were conducted to get insights of the subject being studied. The findings revealed that there are thirteen (13) critical success factors for geospatial data sharing in disaster management. Technology, Organisation, Social, Environment, Ecology and Economy are the dimensions identified and mapped accordingly to the thirteen critical success factors.
State-of-the-Art Review of Assessing Water Quality from Space
Authors: Prapti D R , Shahar N A N , Shariff A R B M 
Published: July 2022

Abstract
Water acts as the medium which helps supply seafood and freshwater food for human consumption and drinking water. It is thus imperative that such a precious resource should be well monitored to safeguard human health and survival. Conventionally water quality can be monitored through manual laboratory-based methods which are time-consuming. Ground-based sensors are helping in expediting this task, though it involves the use of multiple sensors at various locations and requires regular maintenance and replacement. Satellite technology provides a good alternative method as it can scan large areas at a relatively low cost. Measurements of parameters such as sea surface temperature, total suspended solids (turbidity), primary food production (chlorophyll A), abnormal movement of aquatic animal, disease occurrence, water oxygen deficiency, growth monitoring of aquatic life and many others have been successfully reported in the literature. With the advent of micro-satellites that can transmit higher resolution images with a finer spectral resolution, greater advancements can be made for the detection of a greater number of critical water quality parameters. This paper reviewed the existing status of the water quality monitoring from space technology and covered published research done in various parts of the globe, including the problems, solutions, algorithm used, the advantages of the study, research region, water bodies, water quality variable, and satellites data.
Assessing the Suitability of Affordable Housing Based on Demand Criteria
Authors: Zamri Zur’ain , Tarmidi Zakri , Maimun Nurul Hana Adi , Hassan Noordyana , Nasir Ahmad Nabil Md , Sidek Akhmal , Che’Ya Nik Norasma 
Published: July 2022

Abstract
Affordable housing has become essential to provide housing with affordable prices, but most of the locations of affordable housing are often not suitable with the people’s demand. To assess the suitability of affordable housing, several initiatives has been developed, to match the demand and location of the affordable housing, but most the models lacks of spatial element, other model focusing on the goal related to site suitability to cities, and people’s preferences of housing. This study aims to integrating spatial information and analysis to assessing the suitability level of affordable housing in Malaysia. This assessment model consists of 3 main indicators; suitability house according to neighbourhood context, demographic factor and commute distance. The method use in this study is using multi-Criteria Analysis, using weighted scoring techniques. The results show that most of the affordable housing score more than 60% average, with the highest score are 84% and the lowest score are 57.9%. this shows that the suitability level of affordable housing in the study area is good. These indicators can be used for further investigation of other affordable housing, and also in finding the suitable site for affordable housing in the future.
Discriminant Analysis for Predicting Post-Earthquake Municipal Road Recovery
Authors: Wu Jieling , Saito Mitsugu , Endo Noriaki 
Published: July 2022

Abstract
In the event of a widespread natural disaster, the speed of recovery is greatly affected by the priority given to road recovery, not only in terms of assessing the extent of the damage but also in terms of providing rapid assistance to the affected areas and carrying out recovery activities. The purpose of this study is to simulate the recovery of local roads in the Tokai region (Shizuoka, Aichi, and Mie prefectures) in Japan after the Nankai Trough earthquake, which is expected to occur in the future. From the previous analysis of driving data in the Tohoku region (Fukushima, Miyagi, and Iwate prefectures) in Japan during the first six months of the 2011 Tohoku earthquake, we discovered regional characteristics and other factors that affect road recovery. In this study, we continued to analyze and quantify the influencing factors in each pattern of road recovery and applied discriminant analysis to construct a predictive road recovery model. The model showed an accuracy rate of 72.4%. In addition, we used this model to predict municipal road recovery in the Tokai region after the Nankai Trough earthquake in the future and mapped it on a geographic information system (GIS).
An analysis of multidimensional poverty in Nigeria using statistical and geospatial modelling: A case study of Jigawa state
Authors: Gambo Jibrin , Shafri Helmi Zulhaidi Mohd , Yusuf Yusuf Ahmed 
Published: July 2022

Abstract
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 their life, and a lack of social networking inside state and societal organisations. The goal of this research is to conduct a geospatial analysis of Nigeria’s multidimensional poverty and to identify the geospatial distribution pattern of poverty in Jigawa State as a case study. As a result, comprehend the causes of Jigawa’s complicated poverty condition and to create the state’s first ever poverty map. Also, to look at the country’s poverty trends, with a particular emphasis on Jigawa. The National Bureau of Statistics provided the headcount poverty statistics for Nigerian states, which were downloaded and analysed using simple geospatial approach. The findings revealed a dynamic pattern in Nigerian state poverty from 1996 to 2019. The distribution of poverty in Jigawa state, as a case study, revealed that physical and natural environment resources play a critical role in the state’s high poverty rate. Natural drainage, fertile soil, flat topography, and vegetation pattern all aid other people’s poverty levels in different parts of the state. Future study will examine the linking between environmental, social, religious, and political factors and multidimensional poverty using a geospatial method to map the state’s poverty, which has consistently ranked at the bottom of the Nigeria States Poverty Index.
Seagrass Habitat Suitability Models using Multibeam Echosounder Data and Multiple Machine Learning Techniques
Authors: Muhamad M A H , Che Hasan R 
Published: July 2022

Abstract
Seagrass beds are important habitats in the marine environment by providing food and shelter to dugongs and sea turtles. Protection and conservation plans require detail spatial distribution of these habitats such as habitat suitability maps. In this study, machine learning techniques were tested by using Multibeam Echo Sounder System (MBES) and ground truth datasets to produce seagrass habitat suitability models at Redang Marine Park. Five bathymetric predictors and seven backscatter predictors from MBES data were used to representing topography features and sediment types in the study area. Three machine learning algorithms; Maximum Entropy (MaxEnt), Random Forests (RF), and Support Vector Machine (SVM) were tested. The results revealed that MaxEnt and RF models achieved the highest accuracy (93% and 91%, respectively) with SVM produced the lowest (67%). Depth was identified as the most significant predictor for all three models. The contributions of backscatter predictors were more central for SVM model. High accuracy models showed that suitable habitat for seagrass is distributed around shallow water areas (<20 m) and between fringing reef habitats. The findings highlight that acoustic data and machine learning are capable to predict how seagrass beds are spatially distributed which provide important information for managing marine resources.
A Review of Current Development of Altimetry Technique for Tidal and Water Level Measurement Practices and Its Relevance to Energy Industry Applications
Authors: Nugroho Kristiawan Tri , Din Ami Hassan Md 
Published: July 2022

Abstract
With massive geospatial coverage and adequate time series of sea surface height, spatio-temporal multi-mission satellite altimetry tidal modelling emerges as a profound potential solution for increasing accuracy and minimising variation across multiple offshore applications. Therefore, this article attempts to review the current implementation of satellite altimetry in the applicable area of studies relevant to conventional oil and gas applications toward sustainable energy applications. The implication of current spatio-temporal enhancement of tidal measurement by satellite altimetry at the coastal area and the offshore zone is discussed mainly to elaborate on current achievement as well as to gauge potential future optimisation for offshore applications in the energy industry. Spatio-temporal enhancement in conventional oil and gas field applications improves the integration of various offshore construction applications. The impact of this application is more significant as engineering construction adopts stringent and higher vertical data accuracy acceptance criteria. More comprehensive spatial information coverage of tidal regime, co-tidal range, the offshore co-tidal pattern should be more accessible by more intensive spatio-temporal enhancement attempts in various studies and implementations. This leads to higher reliability and integrity of offshore vertical references derivation.
Application of geographical information system (GIS) using artificial neural networks (ANN) for landslide study in Langat Basin, Selangor
Authors: Selamat Siti Norsakinah , Majid Nuriah Abd , Taha Mohd Raihan , Osman Ashraf 
Published: July 2022

Abstract
The landslide was recognized as the most common geologic hazard around the world. The assessment of the relationship landslide conditioning factors is a critical step in managing landslide hazards and risks. Several models have been made to develop the landslide model in recent years. The Artificial Neural Networks (ANN) model was used in this study to develop a landslide model and to identify the most important landslide conditioning factors. Eight conditioning factors, including elevation, slope, aspect, curvature, lithology, soil series, Topographic Wetness Index (TWI), and rainfall, were selected and analyzed using the Geographical Information System (GIS) approach. The multilayer perceptron module and one hidden layer method extracted weighted conditioning factors. The landslide model was validated using the area under the curve (AUC) method. This model validation showed a success rate for training and testing is 0.876, respectively. This study found curvature is the most crucial factor affecting landslide occurrence in the Langat Basin with a 0.213 weight index, followed by rainfall (0.143) and elevation (0.141). Finally, the landslide model can be used as an indicator to identify the most important landslide conditioning factors and assess the relationship between these factors and landslide occurrences.
Suitability modelling: an application to forest road development planning in Peninsular Malaysia
Authors: Kamarudin Norizah 
Published: July 2022

Abstract
The development of modelling applications with Geographic Information System (GIS) has been widely used for planning and analysis. It is a vital technology that can be integrated with multi-criteria evaluation decision support system for problems solving process. This paper presents the model development for suitability forest road planning for the forestry department of Peninsular Malaysia’s application. Multiple significance preferences factors concerned with suitable forest road allocation were selected for the model development, such as slope constraint, elevation factor, the buffer zone of the river, and distance from the existing road. In conclusion, this model is developed to help forest managers sustainably manage the forest, particularly in commencing the forest harvesting operation, which has been widely reported as the main cause of negative environmental impact.