Table of contents

1. Introduction

Greetings from the editorial team of GGT2022. Papers in this proceeding were presented in the International Conference on Geomatics and Geospatial Technology 2022 or GGT2022. This year conference jointly organised by Universiti Teknologi MARA (UiTM) and Royal Institution of Surveyor Malaysia (RISM) was held virtually as many other international conferences in the era of the pandemic.

Universiti Teknologi MARA, Shah Alam, Malaysia was selected as the base for this conference where we had used Zoom as a platform for conducting this conference.

List of Committee, Virtual Conference Dates, Location of Organisers, Platform, Information on Keynote Sessions, Information on Parallel Sessions, Conference's Participants, are available in this pdf.

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing.

• Type of peer review: Double Anonymous

• Conference submission management system: Morressier

• Number of submissions received: 47

• Number of submissions sent for review: 46

• Number of submissions accepted: 30

• Acceptance Rate (Submissions Accepted / Submissions Received × 100): 63.8

• Average number of reviews per paper: 2

• Total number of reviewers involved: 41

• Contact person for queries:

Name: Nabilah Naharudin

Email: nabilahnaharudin1290@uitm.edu.my

Affiliation: Universiti Teknologi MARA

To develop a gravimetric geoid, a Global Geopotential Model (GGM) is required to minimise the truncation error arising from using the Stokes integral with a limited number of gravity data points. The choice of a best-fitting GGM determines the accuracy of a gravimetric geoid solution. Selecting a suitable GGM is a rigorous process, requiring both internal and external evaluation of all GGMs available at the International Center for Globa Earth Models (ICGEM). Moreover, GGMs perform differently depending on the wavelength, and it is difficult to obtain a GGM that performs best across the full harmonic spectrum. In this study, a combined GGM is developed from a selection of the most recent and high-resolution GGMs covering Peninsular Malaysia. The selected models are first synthesized harmonically to obtain geoid undulations at collocated GNSS-levelled points, and free air anomalies at randomly sampled points across the study area. These quantities are compared with the observed geoid undulations and point gravity anomalies interpolated from a grid of free air anomalies. The best performing GGMs are then used to produce a combined GGM, by selecting the spherical harmonic coefficients with the best characteristics for every degree. The signal and error spectra of the new GGM are compared with the selected geopotential models. The combined GGM produced a higher cumulative signal to noise ratio (SNR) of 4402.669 compared to all the selected GGMs, with XGM2016 and Eigen-6C following suit with SNR of 4139.561 and 4092.462, respectively. Besides, the new combined GGM performed better across the whole harmonic spectrum than all selected GGMs. The use of combined GGMs in geoid modelling, instead of a single GGM may be more desirable because they can improve the quality of results.

Stochastic modelling (SM) plays an essential role in least-squares adjustment (LSA), especially for geodetic network data processing. Estimated variances derived from SM are vital factors in determining the reliability of the computed parameter vectors and ensuring the sensitivity of adjustment outcomes toward outliers. As there are multi-source of datasets consisting various of data quality, there is still room for improvement when positional accuracy becomes the main priority. Concerning the accuracy argument, legacy datasets that were exploited in establishing the National Digital Cadastral Database (NDCDB) were obtained from multi-classes of measurement (i.e., first, second and third classes). Taking into account this condition, this research has investigated the capability of stochastic modelling to preserve the positional accuracy of land records that comprehends from multi-classes data quality. To achieve that, the algorithm of Least Squares Variance Component Estimator (LSVCE) was employed in estimating realistic variances. First and second classes measurement were yielded from three (3) certified plans (CPs) which are CP93887, CP80333, and CP33758. Comparison between the adjusted results computed from the combined and separated variance according to data classes have demonstrated that combined variance can detect outliers while separated variance can give realistic adjustment results. From these outcomes, the experiments verified that a hybrid solution is needed for both data classes in order to preserve positional accuracy. In conclusion, to ensure the accuracy of survey data in the future, a proper variance component is needed to improve the coordinated cadastral database.

Sea level rise has currently become a major issue for climate change. It has globally drawn attention because as time passes, global sea levels will continue to rise at an accelerating rate in the 21st century. It will cause a serious impact on environmental problems such as coastal inundation, salt intrusion, coastal erosion, and other phenomena. These scenarios lead to earth problems in which land and oceans continue shifting due to climate change, posing a threat to the very existence of all living beings in the coming years. As a result, climate monitoring is critical for tracking the change. Therefore, this paper reviews the physical factors that contribute to sea level rise. The main contributors for sea level rises, such as ice melting from land into the ocean, thermal expansion, a slowing of the Gulf Stream, and land sinkage, are being discussed. This paper also emphasises the studies of regional sea level, and sea level rate changes. Finally, this review will be discussed in order to clarify the causes of sea level rise issues for human society.

The coordinated cadastral database in Malaysia is known as National Digital Cadastral Database (NDCDB) with an expected accuracy of ±10cm in rural and ±5cm in urban area. Till date, there are approximately 7.8 million land parcels and 22 million boundary markers in the NDCDB for the whole of Peninsular Malaysia and Federal Territory of Labuan covering total area of 132,183 km². Since 2010, NDCDB block adjustment has been carried out continuously without giving prime concern to eliminate gross errors in the adjustment's input data. This approach aims to propose a methodology to improve the positional accuracy of the existing NDCDB through utilisation of the current eKadaster application. A comprehensive investigation in the office and field processes has been carried out to prove the efficiency of the methodology introduced. This investigation was focused on the East Coast Rail Link (ECRL) right of way (ROW) survey from Dungun to Besut where displacement of 1 to 6 meters relative to the NDCDB coordinates, as shown in the Land Acquisition (LA) Plan, have been identified. Areas involved are coded as Block T10701, T1100101 and T1100102 which are located in Lubuk Kawah and Pelagat Sub-districts, in the state of Terengganu. Positional accuracy of the NDCDB after adjustment was further verified by comparing the coordinates of randomly picked ground proofing points in the field using Real-Time Kinematic (RTK) observation. This will determine the Root Mean Square Error (RMSE) of the respective NDCDB Block based on actual observations and adjusted coordinate values. With that, it can be concluded that the proposed approach is reasonably practical and capable to improve and strengthen the positional accuracy of existing coordinated cadastral database used in Malaysia.

Ground Penetrating Radar (GPR) is one of a new instrument in the field survey that has emerge along with the advancement of the world technology. The non-destructive technique (NDT) that was introduced along with the GPR instrument is able to be used in assessing the condition of pavement structure and condition. This data can be used to assess the underground features and thus detect any anomalies and buried features such as cable pipe. The current technique used to assess the pavement structure is very destructive and cost a lot of time to be conducted. This paper is aim to evaluate the performance of GPR in assessing the pavement layer with regards to JKR standard specification. The GPR will be equipped with two different types of frequencies which are 250 MHz and 700 MHz to determine the most suitable frequencies for pavement assessment. The obtain result will then be compared with the specification from JKR in order to evaluate whether the GPR is suitable or not. Based on the result, 700 MHz or higher frequencies are more suitable for pavement layer assessment as it can produce detailed and higher resolution of radargram. The comparison results also determined that the GPR can be used for pavement layer assessment as the measured thickness is within the JKR specification.

This study uses Unmanned Aerial Vehicle (UAV) as a platform to inspect and monitor a building. It focuses on the inspection of defects in buildings, especially cracks. The aim of this study is to investigate cracks on a building by using UAV photogrammetry. The building was used as an object for this study because there are demands from the building maintenance team to assess cracks on the building in an effective way, where it can help to provide reliable crack information. The selected building for this study was an old apartment building with cracks, making it appropriate for this study. There were several steps and procedure used to capture the image of cracks on the building. From the image captured by using UAV, measurement of the cracks can be identified by using a software. There were also different approaches for the output. First is the 3D model of the building and second is the measurement of cracks. The 3D model of the building was created by using Agisoft Photoscan software for the purpose of getting the overview of building dimension. The measurement of cracks was processed by using PhotoModeler software. The accuracy achieved for this study was ±0.8cm. The result showed that UAV photogrammetry can help the current surveying work, especially on building maintenance. Besides, it can capture the crack's images from certain altitude. The authors used UAV for smart building monitoring assessment, which was less considered in the past.

Digitizing architectural heritage information has become an important task during these past years. However, only a few studies have addressed the digitization of historic building models for conservation and restoration purposes. As historical building conservation began to decline, many historical buildings are being abandoned and converted into modern facilities in response to our changing needs. The conservation attempt can be made simple by utilizing the Building Information Modeling (BIM), and High Definition Survey (HDS) approaches. These technologies are considered brand new in managing important information related to 3D models, providing us with semantic information and accurate information in both position and elevation. HDS data is constructed in the form of point clouds generated by the collaboration of survey equipment such as GNSS, Drones, Terrestrial Laser Scanning, and high-performance computers. Point cloud data is registered, filtered, and georeferenced before using BIM modeling. This study resulted from registering point cloud data with RMSE 3mm and customary semantic information. The BIM model based on accurate point cloud data is expected to be a solution and a reference in carrying out the conservation and reconstruction of historical buildings in the future.

Reservoir monitoring is important in maintaining water retention and controlling volume changes as well as sedimentation rates. Reservoir monitoring usually uses conventional means such as recording the pole leveling height at a certain time, using large ships with the sounding method and determining the position and depth by utilizing total stations with intersection method measurements. However, such conventional methods require a lot of manpower, a significant period of time, a lot of equipment, and, more often than not, yielding in results that cannot be used to depict the real condition of the corresponding reservoir. This paper uses bathymetric and aerial photographic data to construct land and water topography, the state of the reservoir, and a 3D model of the reservoir, which later can be used as the basis for volume and sedimentation analyses. An effective way of merging the aforementioned data is by utilizing point cloud data generated from bathymetric surveys and UAVs. The point cloud data was then used as the basic material for creating DEM, land, and water contours. The bathymetric data quality test results meet the SNI 7647:2010 standard tolerance with a 1.96*standard deviation of 0.191. It passes the SNI 8202:2015 photo quality test with CE90/LE90 values of 0.325 and 0.285, respectively. Merging bathymetric and aerial photographic data in the regular reservoir monitoring or shallow waters is proven to be a more efficient, effective, and optimum method compared to the existing conventional means.

Burgeoning off-the-selves Digital Single Lens Reflector (DSLR) cameras have been gaining attentions as a fast and affordable tool for conducting deformation monitoring of man-made engineering structures. When a sub millimetre of accuracy is sought, deliberate concerns of their usage must be considered since lingering systematic errors in the imaging process plaque such non metric cameras. This paper discusses a close range photogrammetric method to conduct structure deformation monitoring of the bridge using the digital DSLR camera. The bridge is located in Malang Municipality, East Java province, Indonesia. There are more than 100 images of the bridge's concrete pillars were photographed using convergent photogrammetric network at distance variations between 5m to 30m long on each epoch. Then, the coordinates of around 550 captured retro-reflective markers attached on the pillars facade are calculated using self-calibrating bundle adjustment method. The coordinate differences of the markers from the two consecutive epochs are detected with a magnitude between 0.03 mm to 6 mm with a sub-millimetre precision measurement level. However, by using global congruency testing and a localization of deformation testing, it is confirmed that the bridge pillar's structures are remain stable between those epochs.

Enhancing border management policies appears to be a crucial aspect of the state border nations' proposal for interstate border governance. Understanding the action principle that guides an effective decision-making process among the institutions involved is necessary to improve a policy. The Institutional Analysis and Development Framework (IAD), proposed by Elinor Ostrom, an American political scientist specializing in institutional behaviour, is the most applicable framework for reforming guidelines. This article aims to raise awareness of the significance of incorporating the Institutional Analysis and Development Framework concept into the Joint Border Committee's (JBC) effective practice of interstate border determination in Peninsular Malaysia. Although incorporating social science knowledge into border management cycles remains limited, it appears to have increased over the past decade. The findings are analyzed to determine the suitability of integrating Institutional Analysis & Development (IAD) into Peninsular Malaysia's JBC practice to predict institutional behaviour and relationships related to the outcomes

This paper discusses the lessons learnt from the SmartKADASTER Phase II city model development project, specifically on the reconstruction of the LoD 1 CityGML models. The LoD 1 models were reconstructed using automated height extrusions, either by creating categorised point clouds or by employing a raster-based equation such as CHM=DSM-DTM. The methods for reconstructing the LoD1 are further elaborated in this study. However, due to the particular nature of Malaysian buildings and inaccurate point cloud classifications, automated height extrusion alone was found to be insufficient to achieve the typical recommended average rooftop height as the LoD1 height reference. Additionally, it was determined that the recommended height reference is also unsuitable for cadastre-based analysis and other beyond cadastre purposes in Malaysia. As a result, this paper will discuss the selection of the LoD1 height reference and suggest the approach to ensure accurate height extrusion of the LoD1 model can be met. Finally, it is hoped that this work will contribute to the body of knowledge by appropriately referencing their 3D models for analysis purposes and raising readers' awareness of the SmartKADASTER application system.

Open-source data is open for anyone and everyone for access, modification, reuse, and sharing for particular application. Open-source data is compiled from various sources using public user collaboration. Open street map is one of open-source vector provider. The suitability study of the use of open-source data for the production of topographic maps of various scales is important as a new approach. The importance of the feasibility study of the use of open-source data can help improve the efficiency of the production of mapping products. Many methods of producing topographic maps of various scales use various state -of -the -art technologies for fast and efficient map production. The objective of the study is to check the planimetric accuracy and feature geometry of open-source vector datasets. Conventional methodologies for the production of multi -scale topographic maps are time consuming and involve high costs. Therefore, open-source data is an alternative source for the production of topographic maps of various scales. This data source shows the potential use for generation of multiple data layers such as roads, points, places, waterways, railways, natural, buildings and land use. The planimetric accuracy of open-source vector data is ranging from 2-5 m. The overlay analysis between reference dataset and open-source data show the similarity geometry for 1:50,000 map scale. This method shows a high level of suitability for the efficient updating of topographic data and the production of topographic maps for 1:50,000 map scale.

Wave is formed from the movement of air caused by pressure variations that make airflow move from high pressure toward places of low pressure. Understanding the wave patterns is challenging since it is highly changeable in space as they travel in variety of directions and heights. Wave are also changing over time especially during the monsoon seasons. Hence, to extract significant information from this highly changeable behaviour of wave this study has utilized a multi-dimensional clustering technique called co-clustering. This technique is able to cluster spatio-temporal data with similar behavior into spatial and temporal components simultaneously. To reveal the spatial and temporal patterns, an algorithm called Bregman Block Average co-clustering with I-divergence (BBAC_I) has been implemented for extracting wave patterns. In order to discover the wave behaviour, the extracted wave patterns were visualized in the form of heatmap that contain information of co-clusters; spatial clusters and temporal clusters dimensions. Then, both spatial and temporal clusters from the heatmap were transformed into geographical maps to depict the variation of wave patterns based on their individual dimension. From these maps, we could observe the distribution of 8 different group of clusters that representing the spatial wave patterns. Furthermore, 5 individual maps have been produced to depict the temporal wave patterns across the study area. Finally, the obtained maps were interpreted in the form of wave height which were found to be within 0.4 to 1.4 m heights. The wave height information can be used for identifying their potential for ocean energy harvesting along the coastal area. In generally, the generated spatio-temporal wave patterns from this study could aid Malaysian marine agencies to provide strategic planning for proposing future ocean energy in Malaysian coastal area.

As the need for elevation data grows, it is more vital than ever for users to match the data degree of dependability, precision, and spatial resolution to their specific uses to produce a useful and cost-effective product. This article will describe several sources of elevation data, ranging from space-based to aerial-based techniques, and classify the data according to its respective quality and accuracy. The elevation data sources can be classified into two namely localised or can also be referred to as regional, and global coverage. Among the example of localised sources of elevation data are Light Detection and Ranging (LiDAR) and Interferometry Synthetic Aperture Radar (InSAR). The global sources of elevation data are Shuttle Radar Topography Mission (SRTM), Advanced Spaceborne Thermal Emission and Reflection Radiometer-Global Digital Elevation Model (ASTER), Advanced Land Observing Satellite (ALOSW3D), Global Multi-Resolution Terrain Elevation Data 2010 (GMTED2010), TerraSAR-X add on for daily Digital Elevation Measurement (TanDEM-X), The Ice, Cloud, and land Elevation Satellite-2 (ICESat-2), Radar Satellite (RADARSAT) Constellation Mission (RCM) and Satellite-Derived Bathymetry (SDB). The characteristics of each elevation data source were discussed in terms of its launch date, period of observation, spatial resolution, horizontal and vertical datum, and coverage. Its reliability was described in detail for future topographic applications.

Walkability is defined as the level of comfort of an environment can be provided to people so they can walk to their destination. Walkability also supports community health, safety, liveability and reduce car dependence. Walkability is vital due to urban growth and the increased number of vehicle used. Walkable city promotes the residents to walk more in their neighbourhood. In addition to that, a walkable city promotes an active transportation in the era that seems to be very much car reliance. This study aims to measure the Walkability Index in the city of Pasir Gudang while addressing a question; i) How can the Walkability Index be measured? Hence, a new index was developed to address the question. The Walkability Index is calculated for every neighbourhood in Pasir Gudang by using the 3D criteria, Dwelling Density, Land Use Diversity, and Intersection Density. The index was classified into five categories from the lowest to the highest index.

Public bus service is an essential transport in major cities. This service makes it easier for passengers to move from one place to another place. The aim of this research is to allocate new potential bus stop locations in Shah Alam. The purpose is to use the AHP calculation method to find the average priorities weightage value of the criteria and sub criteria used in this research. The applying of GIS to process the data calculated from the AHP will identify which criteria is preferred over each other to locate new bus stop locations. The methodology in this research is divided into four phases which is preliminary work, data acquisition, data processing, and data analysis. Software used in this research for data processing is ArcGIS Pro. How strategic is the locations of the bus stop according to the population? The rising of urbanization has led to increasing in population density; hence the location, functionality, safety, and visual appearance of bus stop are crucial. Therefore, this research is focused on allocating of new potential bus stop locations that has been identified through the GIS geoprocessing tools.

Crop yield can determine the health of a tree based on the nutrient content applied by the farmers. Soil nutrients are essential for plant production. This research aims to study the relationship between the yields of Harumanis mango and the nutrient content in soil and leaves. Three (3) primary objectives are highlighted, namely: (1) to study the distribution of nutrients content in the soil; (2) to determine the relationship between the yields of Harumanis mango and nutrient content in soil and leaves; and (3) to identify the significant factors affecting the yields of Harumanis mango. The study area is in the UiTM Perlis branch. Overall, 25 samples have been collected with information on the coordinate position, crop yield and nutrient content; nitrogen (N), phosphorus (P), potassium (K) and calcium (Ca). The Kriging method interpolated the distribution of nutrient content in the soil. Pearson correlation and multiple linear regression analysis were used to study the relationship between yield and nutrient content in soil and leaves to determine which nutrients influenced the yield of Harumanis mango. The result shows only K and Ca in the leaves significantly contribute to the yield of Harumanis mango. The multiple linear regression model for the predicted yield of Harumanis mango is 347.974 – 51.426 (N% in leaves) – 550.225 (K% in leaves) + 86.672 (Ca% in leaves).

The purpose of this paper is to evaluate the performance of the spline interpolation method in predicting and mapping the concentration of Total Suspended Solids (TSS) in the surface water of Pulau Tuba, Kedah. Thirty sampling points were set up and geolocated using the Geographic Positioning System (GPS). Gravimetric analyses were used to determine the TSS level. Fifty percent of the total sampling points were randomly chosen for developing spatial models using regularised and tension spline methods. The research found that the tension spline methods outperform the regularised spline method. The Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Error Percentage (MAPE) were reported at 351.641, 18.752, 15.81, and 21.51%, respectively. This study's findings are critical in the domains of spatial statistics and interpolation for creating a precise map of water properties.

The phenomenon of coastal erosion is a natural disaster that often occurs every year on the East Coast of Peninsular Malaysia. Coastal erosion can be identified through changes in coastlines found in coastal areas. Beaches in the state of Terengganu often face the problem of big waves and strong winds during the monsoon season. Coastal erosion can be identified through changes in coastlines found in coastal areas. The Ruin Monsoon phenomenon which causes tides and violent waves as high as almost four meters has eroded the land little by little over the past few decades. This study aims to determine the coastline change from 2016 until 2020 by using geospatial technology at Pantai Kuala Nerus, Terengganu. Furthermore, this study has three objectives to achieve. The first objective is to identify the coastline by using multispectral satellite imagery. The second objective is to analyze the rate of erosion and accretion along Pantai Kuala Nerus. The third objective is to determine the category of Pantai Kuala Nerus coastline according to the Malaysia National Coast Erosion Study (NCES) guidelines. The study concludes almost all erosion area in Pantai Kuala Nerus is included in critical and high erosion rate.

Utility is considered as one of the most important systems in all countries. A structured network is defined as a network comprising specific components and subsystems. Leakage of water pipes is a common problem around the world. It is understood that water is a valuable resource to all living things. The aim of this study is to estimate the durability of water pipe based on natural environment factor in Selangor, Malaysia. There are several proposed objectives listed such as to identify the parameters that affect the durability of the pipe, to develop GIS modelling to predict the durability of water pipelines and to generate durability of pipeline prediction maps to the study area. The criteria such as temperature, soil moisture, soil type, acidity, resistivity, landuse and slope were selected from the expert opinion and also from the previous study. The method used is Analytical Hierarchy Process (AHP) and GIS. AHP technique were used to calculate the weightage for each of the criteria. In this study, map of the mildsteel waterpipe durability estimation can be produced using ArcGIS.

Rice is the primary source of nutrition food of more than half of the world's population, and it is hugely important in the global economic growth, food security, water use, and climate change. The need for satellite systems to monitor rice crops and assist in rice crop management is gaining in popularity. The European Space Agency's (ESA) launched Sentinel-2 A + B twin platform's which enhanced the temporal, spatial, and spectral resolution, opening the way for their widely use in crop monitoring. Aside from the technical features of the Sentinel-2 A and B constellation, the easily accessible type of information they generate as well as the appropriate support software have been significant improvements for rice crop monitoring. In this study, the spectral reflectance has been analysed to find how far their potential in determining rice growth phases. The highest spectrum in reflectance was observed in the near infrared (NIR) region (842 nm). Because of the structure of mesophyll cells tissues and the inner backscatter of air spaces, moisture content, and air–water abstraction layers within the leaves, the reflectance in the NIR region seems to be much larger than in the visible band. The multi-temporal vegetation index namely Normalized Difference Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Normalized Difference Moisture Index (NDMI) have derived from ten Sentinel-2 images cover the entire rice season. These indices have been tested to determine the rice growth phases over the rice season. The spatial distribution of each tested indices is displayed in the map output. The maps are then analysed and compared to determine the potential of each index in determining rice growth phases. It was discovered in this study that there was a quadratic correlation between all of the tested indices and rice age. The Normalized Difference Vegetation Index (NDVI) is the most accurate vegetation index for estimating rice growth phases, followed by SAVI and NDMI.

The energy demand has risen dramatically in recent years as a result of population growth and the fast expansion of the global economy. Due to rising carbon dioxide (CO2) emissions and increasing energy needs, Malaysia has recently intensified its efforts to encourage the development of renewable energy supplies. Despite the abundance of solar resources, the PV growth in underdeveloped countries such as Malaysia is moving slower. Malaysia has the advantage in solar energy generation since it is geographically close to the equator and has a significant solar generation potential due to its hot and sunny weather all year. Therefore the question here is, where is the most suitable place to build more solar power plants? What methods will we be using to find the most suitable place to build the solar power plant? In picking a place for such development, several factors must be examined, such as how effective the PV power station site is and how to decrease the overall cost of the project by minimising proximity to existing infrastructures while improving solar panel power production. A geographic information system (GIS) is a tool that can help to solve this challenge. In this case, we integrate economic, environmental, and technical variables such as solar radiation intensity, local physical terrain, environment, climate, and placement criteria such as distance from roads and rivers. As additional input factors, other geographical information data were used (solar radiation, digital elevation models (DEM), land cover, and temperature). Further analysis using the Analytic Hierarchy Process (AHP) based Multi-Criteria Decision Making (MCDM) was then applied to this study to get the suitable location based on the importance of the criteria. In order to build a cost-effective and high-performing solar project, a complete solar site assessment is required. The results of the study should give a decision support system model and map on determining the optimal locations of solar power plants in Melaka.

The growth of urbanization in Klang District was considered to be fast and has increased the concern of policy makers and town planners. This paper assess the changes of urban development in Klang District using Support Vector Machine (SVM) classification by different kernel for the purpose of studying the built up area changes within the year 2017 to 2021. At the initial stage of image processing, Land Use Land Cover (LULC) has been classified based on the use of SVM by different kernel (RBF, Polynomial, Linear, and Sigmoid) which was then reclassify into the built up and non built up after the most accurate kernel has been identified, thus the study was focused on the growth of urbanization. As results, the highest accuracy is RBF Kernel which the LULC that has been classified were 88% in 2017 and 90% in 2021. The RBF Kernel was then used for the classification of built up area and also for the analysis of urban growth. It can be seen that there have been changes for every land use, particularly urban growth by 9.39% (5451.77 Ha). Hence, the pattern of urban sprawl would assist planners and policymakers in planning and managing a better city.

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), random forests (RF) and deep neural network (DNN). A total 13218 sampling points (80% of the total sampling points used as training samples and 20% applied as testing samples) were randomly selected in the study area which were then classified into six land cover features; water, bare soil, forest, immature oil palm (the age of 2-8 year), mature oil palm (age >8 year) and built-up area. These data were validated by using spectral reflectance, Google Earth Pro and ground checking. The accuracy assessment was conducted by a confusion matrix method. The results showed that classification of land features using DNN with batch size 32 and epoch 100 has the highest accuracy which is 99.35% for overall accuracy and 98.49% kappa accuracy. This study demonstrated various machine learning algorithms that may be used to detect and classify the maturity of oil palm trees, which is vital to record in tree inventories for effective plantation management.

Oil palm is one of the cultivation that synonyms in Malaysia. Malaysia become one of the biggest palm oil producer globally after Indonesia. In order to achieve successful yield per year, oil palm need constant effort and labor to monitor them accordingly. Manual method in monitoring the palm oil consumes large amount of time and energy. Palm oil comes from the fleshy fruit of oil palms. Unrefined palm oil is sometimes referred to as red palm oil because of its reddish-orange color. Remote sensing technique utilizes usage of satellite imageries to analyzes healthiness and canopy features of palm oil plantation. There are several advantage in determining palm oil condition through multispectral and texture analysis in ERDAS Imagine and Envi. Utilizing Landsat-8 imagery, monitoring palm oil cultivation and yield can be effectively implemented in Malaysia. In this study, we will use three vegetation indices which are Normalized Differential Vegetation Index (NDVI), Soil Adjusted Vegetation Index (SAVI), and Ratio Vegetation Index (RVI). This study will demonstrate that selected satellite-derived vegetation indices can be used to estimate oil palm yields with reliable accuracy. In this work, the ability of selected vegetation indices, derived from a single-date archived high resolution satellite imagery, to estimate oil palm yields at the management block scale was demonstrated. This technique applied to determine the condition of the palm oil tree. Using remote sensing technique, the value of the vegetation indices will be determined and analyzed. Result from this process, palm oil condition can be evaluated. This study provides an important benchmark for applying remote sensing technology in the management of plantation-scale oil palm. Oil palm yield estimation based on empirical models, as described in this work, can be computerized using a simple spreadsheet interface so as to facilitate optimal agronomic intervention, particularly with regard to crop harvesting, crop stress alleviation and input application. However, it's important to note that palm oil should not be confused with palm kernel oil. While both originate from the same plant, palm kernel oil is extracted from the seed of the fruit. It provides different health benefits.

Cases of children died in vehicle have been increased each year. Such incident sometimes may happen incidentally especially when children are seated at the rear seats and the problem occurs due to lacking of existing system in detecting children image in a car. Consequently, this study aims to detect the existence of "in-car-abandoned children" using deep learning algorithm. A set of children images model will be classified into two (2) classes; children and no-children via Convolutional Neural Network (CNN) classifier by integrating with programming language, namely TensorFlow. Interestingly, the proposed method can automatically learn pattern features and reduce the incompleteness caused by artificial design features. As a result, a model of sensor that can detect the whole children's body in various poses with automatic tagging to the children's image is designed. Accordingly, this study can assist to improve current vehicle systems and create awareness among parents regarding the importance of children's safety.

In agriculture management and cultivation, many researchers tend to introduce and implement new methods or techniques to improve the sectors in order to sustain a good production from the sectors. The oil palm plantation is one of the sectors that have received an improvement in development in many aspects. Thus, this paper reviews in detail the recent expansion of oil palm management and sustainability through the latest application technologies specifically in Remote Sensing (RS) and Geographical Information System (GIS) knowledge which covered land classification and crop changes, disease detection and pest control, age estimation for oil palm, above-ground biomass (AGB) and carbon estimation, tree counting for oil palm assessment and land suitability with soil nutrients. In the end, it concluded the most significant GIS and RS tools for oil palm management come from the implementation of Machine Learning (ML) and Deep Learning (DL) knowledge in it which can be improved over time through recent technologies and variation analysis to enhance the results.

Oil palm is one of the important sources of vegetable oil that is mostly consumed by the Malaysian citizen. Because the demand of vegetable oil is high in Malaysia, the expansion of oil palm tree plantation has been increasing rapidly. Remote sensing application in monitoring and detecting oil palm trees has become a very useful tool to help minimize the human energy to monitor in large plantations. The aim of this study is to evaluate the accuracy of oil palm tree detection using deep learning and support vector machine (SVM) approaches using high-resolution remote sensing images. Deep learning is one of the base frameworks for oil palm tree detection using high-resolution remote sensing images. Deep learning also is a counting tool provided in newest technology software such as ArcGIS Pro, where the tools use the pattern recognition concept as a template in detecting objects in a high-resolution image. In machine learning, support vector machines are supervised learning models with associated machine learning algorithms that analyse data for classification analysis. Based on this study, 91% of the oil palm trees detected using deep learning approach gained higher accuracy than SVM classifier with 86%. The accuracy of oil palm tree detection using deep learning is higher than the accuracy of support vector machine classifier. Based on the findings, deep learning approach create better object interpretation than SVM. The oil palm tree detection using both classification approaches had also been displayed by using a spatial map distribution for easier analysed and observation.

The pH of a soil is a measure of its acidity or alkalinity. The pH of the soil is important in agricultural activities because it has an impact on crop yield. Remote sensing, Geographic Information Systems (GIS), and digital soil maps are becoming more appreciated in soil science studies. The research thus focuses on creating a predicted soil pH map of the study area using Landsat-8 satellite imagery and GIS. The single and combination of spectral bands were used to generate three models using simple linear regression. The results suggested that the approach is not sensitive enough for prediction of soil pH in the study area. R² value obtained are 0.049 from Model 1, 0.016 from Model 3, and 0.0003 for Model 2. All the models indicate that the soil pH is in the acid situation but the full range of observed pH is not matched by any predicted model. In the validation process, Model 1 has an RMSE value of 0.397, whereas both Model 2 and 3 have RMSE value of 0.405. To obtain a more promising pH result, it is suggested to use indices such as vegetable indices (VI), salinity index (SI), and a combination of band ratios.