ORCID Profile
0000-0002-8669-874X
Current Organisation
Universiti Putra Malaysia
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Publisher: Springer Science and Business Media LLC
Date: 08-01-2010
Publisher: Hindawi Limited
Date: 05-08-2018
DOI: 10.1155/2018/7212307
Abstract: This paper reports on a building detection approach based on deep learning (DL) using the fusion of Light Detection and Ranging (LiDAR) data and orthophotos. The proposed method utilized object-based analysis to create objects, a feature-level fusion, an autoencoder-based dimensionality reduction to transform low-level features into compressed features, and a convolutional neural network (CNN) to transform compressed features into high-level features, which were used to classify objects into buildings and background. The proposed architecture was optimized for the grid search method, and its sensitivity to hyperparameters was analyzed and discussed. The proposed model was evaluated on two datasets selected from an urban area with different building types. Results show that the dimensionality reduction by the autoencoder approach from 21 features to 10 features can improve detection accuracy from 86.06% to 86.19% in the working area and from 77.92% to 78.26% in the testing area. The sensitivity analysis also shows that the selection of the hyperparameter values of the model significantly affects detection accuracy. The best hyperparameters of the model are 128 filters in the CNN model, the Adamax optimizer, 10 units in the fully connected layer of the CNN model, a batch size of 8, and a dropout of 0.2. These hyperparameters are critical to improving the generalization capacity of the model. Furthermore, comparison experiments with the support vector machine (SVM) show that the proposed model with or without dimensionality reduction outperforms the SVM models in the working area. However, the SVM model achieves better accuracy in the testing area than the proposed model without dimensionality reduction. This study generally shows that the use of an autoencoder in DL models can improve the accuracy of building recognition in fused LiDAR–orthophoto data.
Publisher: Informa UK Limited
Date: 14-12-2015
Publisher: IEEE
Date: 12-2015
Publisher: IEEE
Date: 07-2008
Publisher: Informa UK Limited
Date: 17-04-2014
Publisher: Informa UK Limited
Date: 20-02-2022
Publisher: IEEE
Date: 12-2012
Publisher: Informa UK Limited
Date: 2015
Publisher: IEEE
Date: 04-2013
Publisher: Informa UK Limited
Date: 2016
Publisher: Springer Science and Business Media LLC
Date: 12-06-2015
Publisher: Informa UK Limited
Date: 30-06-2011
Publisher: Informa UK Limited
Date: 19-03-2013
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: SPIE-Intl Soc Optical Eng
Date: 05-2009
DOI: 10.1117/1.3155804
Publisher: IEEE
Date: 12-2008
Publisher: Science Alert
Date: 15-03-2009
Publisher: Hindawi Limited
Date: 13-02-2017
DOI: 10.1155/2017/6750346
Abstract: Nowadays, a Global Navigation Satellite System (GNSS) unit is embedded in nearly every smartphone. This unit allows a smartphone to detect the user’s location and motion, and it makes functions, such as navigation, tracking, and compass applications, available to the user. Therefore, the GNSS unit has become one of the most important features in modern smartphones. However, because most smartphones incorporate relatively low-cost GNSS chips, their localization accuracy varies depending on the number of accessible GNSS satellites, and it is highly dependent on environmental factors that cause interference such as forests and buildings. This research evaluated the performance of the GNSS units inside two different models of smartphones in determining pedestrian locations in different environments. The results indicate that the overall performances of the two devices were related directly to the environment, type of smartphone/GNSS chipset, and the application used to collect the information.
Publisher: Trans Tech Publications, Ltd.
Date: 06-2014
DOI: 10.4028/WWW.SCIENTIFIC.NET/AMM.567.705
Abstract: Soil moisture (MC) is considered as the most significant boundary conditions controlling most of the hydrological cycle’s processes especially over humid areas. However, MC is very critical parameter to measure because of its variability in both space and time. The fluctuation of MC along the soil depth in turn, makes it so difficult to assess from optical satellite techniques. The study aims to produce a rectified satellite’s surface temperature (Ts) in order to enhance the spatial estimation of MC. The study also aims to produce MC estimates from three variable depths of the soil using optical images from NOAA 17 in order to examine the potential of satellite techniques in assessing the MC along the soil depths. The universal triangle (UT) algorithm was used for MC assessment based on Ts, vegetation Indices (VI) and field measurements of MC which were conducted at variable depths. The study area was ided into three classes according to the nature of surface cover. The resultant MC extracted from the UT method with rectified Ts, produced accuracies of MC ranging from 0.65 to 0.89 when validated with in-situ measured MC at depths 5cm and 10 cm respectively.
Publisher: Elsevier BV
Date: 06-2015
Publisher: MDPI AG
Date: 29-01-2023
Abstract: 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 variance of date palm trees, the variations in the spatial resolutions of the data, and the differences in image contexts and backgrounds, accurate mapping of date palm trees from very-high spatial resolution (VHSR) images can be challenging. This study aimed to investigate the reliability and the efficiency of various deep vision transformers in extracting date palm trees from multiscale and multisource VHSR images. Numerous vision transformers, including the Segformer, the Segmenter, the UperNet-Swin transformer, and the dense prediction transformer, with various levels of model complexity, were evaluated. The models were developed and evaluated using a set of comprehensive UAV-based and aerial images. The generalizability and the transferability of the deep vision transformers were evaluated and compared with various convolutional neural network-based (CNN) semantic segmentation models (including DeepLabV3+, PSPNet, FCN-ResNet-50, and DANet). The results of the examined deep vision transformers were generally comparable to several CNN-based models. The investigated deep vision transformers achieved satisfactory results in mapping date palm trees from the UAV images, with an mIoU ranging from 85% to 86.3% and an mF-score ranging from 91.62% to 92.44%. Among the evaluated models, the Segformer generated the highest segmentation results on the UAV-based and the multiscale testing datasets. The Segformer model, followed by the UperNet-Swin transformer, outperformed all of the evaluated CNN-based models in the multiscale testing dataset and in the additional unseen UAV testing dataset. In addition to delivering remarkable results in mapping date palm trees from versatile VHSR images, the Segformer model was among those with a small number of parameters and relatively low computing costs. Collectively, deep vision transformers could be used efficiently in developing and updating inventories of date palms and other tree species.
Publisher: SPIE
Date: 19-11-2012
DOI: 10.1117/12.979631
Publisher: Springer Science and Business Media LLC
Date: 08-05-2015
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: Springer Science and Business Media LLC
Date: 06-2014
Publisher: Elsevier BV
Date: 06-2015
Publisher: Informa UK Limited
Date: 23-04-2010
Publisher: Springer Science and Business Media LLC
Date: 30-01-2021
Publisher: Informa UK Limited
Date: 02-08-2011
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: IEEE
Date: 05-2014
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: SPIE-Intl Soc Optical Eng
Date: 10-2009
DOI: 10.1117/1.3257626
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: SPIE-Intl Soc Optical Eng
Date: 28-05-2015
Publisher: Informa UK Limited
Date: 23-11-2009
Publisher: Science Publications
Date: 12-2013
Publisher: Informa UK Limited
Date: 25-02-2014
Publisher: Springer Science and Business Media LLC
Date: 02-08-2013
Publisher: Science Alert
Date: 15-12-2008
Publisher: IEEE
Date: 12-2008
Publisher: IEEE
Date: 03-2009
Publisher: Springer Science and Business Media LLC
Date: 25-09-2010
Publisher: Journal of Urban and Environmental Engineering
Date: 04-07-2014
DOI: 10.4090/JUEE.2014.V8N1.011027
Abstract: This study aims at identifying the suitable lands for urban development in Bandar Abbas city based on its real world use regarding specific criteria and sub-criteria. The city of Bandar Abbas is considered as the most important commercial and economic city of Iran. It is also considered as one of the major cities of Iran which has played a pivotal role in the country's development and progress in recent years especially after the end of Iran-Iraq war owing to its embracing the country's main commercial ports. This process has caused the immigration rate into the city to rise significantly over the past 20 years. Thus, the development of the city is meanwhile considered as a high priority. Bandar Abbas city does not have a rich capacity for growth and development due to its special geographical situation being located in coastal border. Among the limitations placed in the city's development way, natural limitations (heights and sea shore) in the northern and southern parts of the city and structural limitations (military centers) in the east and west sides of the city may be referred. Therefore, identifying the suitable lands for urban development within Bandar Abbas city limits is becoming an essential priority. Therefore, different quantitative and qualitative criteria have been studied in order to select and identify these lands. The structures of qualitative criteria for most parts involve ambiguities and vagueness. This leads us to use Fuzzy logic in this study as a natural method for determining the solutions for problems of Multi-criteria decision making (MCDM). In the current research, a combination of MCDM methods has been presented for analysis. To assignee weights of the criteria Fuzzy AHP (analytic hierarchy process) is used for land selection and Fuzzy TOPSIS (method for order priority by similarity to ideal solution) is utilized to choose the alternative that is the most appropriate through these criteria weights. The sensitivity analysis of the results is included in the research.
Publisher: Informa UK Limited
Date: 11-11-2022
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: Thomas Telford Ltd.
Date: 09-2012
Abstract: Groundwater vulnerability mapping is an important key to improving planning and decision-making processes in order to prevent groundwater contamination. This study generates groundwater vulnerability maps for the Izeh Plain in Iran using a modified Drastic model, geographical information system and remote sensing data. Sensitivity analysis was used to assign weight by showing the importance of each parameter in the Drastic model. The highest weight (5) was assigned to the aquifer media factor of the model, which had the highest mean value (4·53), and the lowest weight was assigned to the topography factor. The resulting maps revealed that the groundwater is highly vulnerable in the southwest Izeh Plain.
Publisher: IOP Publishing
Date: 23-06-2014
Publisher: Informa UK Limited
Date: 02-01-2015
Publisher: Hindawi Limited
Date: 2014
DOI: 10.1155/2014/690872
Abstract: The process of land use change and urban sprawl has been considered as a prominent characteristic of urban development. This study aims to investigate urban growth process in Bandar Abbas city, Iran, focusing on urban sprawl and land use change during 1956–2012. To calculate urban sprawl and land use changes, aerial photos and satellite images are utilized in different time spans. The results demonstrate that urban region area has changed from 403.77 to 4959.59 hectares between 1956 and 2012. Moreover, the population has increased more than 30 times in last six decades. The major part of population growth is related to migration from other parts the country to Bandar Abbas city. Considering the speed of urban sprawl growth rate, the scale and the role of the city have changed from medium and regional to large scale and transregional. Due to natural and structural limitations, more than 80% of barren lands, stone cliffs, beach zone, and agricultural lands are occupied by built-up areas. Our results revealed that the irregular expansion of Bandar Abbas city must be controlled so that sustainable development could be achieved.
Publisher: IEEE
Date: 06-2012
Publisher: SPIE-Intl Soc Optical Eng
Date: 07-04-2016
Publisher: Elsevier BV
Date: 02-2014
Publisher: Springer Science and Business Media LLC
Date: 09-10-2020
Publisher: Springer Science and Business Media LLC
Date: 28-12-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2016
Publisher: Informa UK Limited
Date: 13-11-2014
Publisher: Springer Science and Business Media LLC
Date: 25-11-2015
DOI: 10.1007/S10661-014-4156-0
Abstract: Wetlands are regarded as one of the most important ecosystems on Earth due to various ecosystem services provided by them such as habitats for bio ersity, water purification, sequestration, and flood attenuation. The Al Hawizeh wetland in the Iran-Iraq border was selected as a study area to evaluate the changes. Maximum likelihood classification was used on the remote sensing data acquired during the period of 1985 to 2013. In this paper, five types of land use/land cover (LULC) were identified and mapped and accuracy assessment was performed. The overall accuracy and kappa coefficient for years 1985, 1998, 2002, and 2013 were 93% and 0.9, 92% and 0.89, 91% and 0.9, and 92% and 0.9, respectively. The classified images were examined with post-classification comparison (PCC) algorithm, and the LULC alterations were assessed. The results of the PCC analysis revealed that there is a drastic change in the area and size of the studied region during the period of investigation. The wetland lost ~73% of its surface area from 1985 to 2002. Meanwhile, post-2002, the wetland underwent a restoration, as a result of which, the area increased slightly and experienced an ~29% growth. Moreover, a large change was noticed at the same period in the wetland that altered ~62% into bare soil in 2002. The areal coverage of wetland of 3386 km(2) in 1985 was reduced to 925 km(2) by 2002 and restored to 1906 km(2) by the year 2013. Human activities particularly engineering projects were identified as the main reason behind the wetland degradation and LULC alterations. And, lastly, in this study, some mitigation measures and recommendations regarding the reclamation of the wetland are discussed. Based on these mitigate measures, the discharge to the wetland must be kept according to the water requirement of the wetland. Moreover, some anthropogenic activities have to be stopped in and around the wetland to protect the ecology of the wetland.
Publisher: Informa UK Limited
Date: 03-05-2022
Publisher: Informa UK Limited
Date: 29-03-2011
Publisher: Informa UK Limited
Date: 30-06-2015
Publisher: Informa UK Limited
Date: 22-12-2011
Publisher: IEEE
Date: 11-2014
Publisher: Springer Science and Business Media LLC
Date: 19-11-2013
Publisher: Journal of Urban and Environmental Engineering
Date: 30-06-2011
Publisher: MDPI AG
Date: 07-05-2014
DOI: 10.3390/S140508259
Publisher: Elsevier BV
Date: 08-2013
Publisher: Informa UK Limited
Date: 13-02-2014
Publisher: IEEE
Date: 12-2007
Publisher: Science Publications
Date: 2009
Publisher: IEEE
Date: 2008
Publisher: MDPI AG
Date: 20-02-2023
DOI: 10.3390/IJGI12020076
Abstract: 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 use optimisation approaches to make a sustainable decision regarding the suitable site encompasses complex processes. Thus, this study aims to innovate a novel technique that can predict urban sprawl for a long time and can be simply integrated with optimisation land use techniques to make suitable decisions. Three main processes were applied in this study: (1) a supervised classification process using random forest (RF), (2) prediction of urban growth using a hybrid method combining an artificial neural network and cellular automata and (3) the development of a novel machine learning (ML) model to predict urban growth boundaries (UGBs). The ML model included linear regression, RF, K-nearest neighbour and AdaBoost. The performance of the novel ML model was effective, according to the validation metrics that were measured by the four ML algorithms. The results show that the Nasiriyah City expansion (the study area) is haphazard and unplanned, resulting in disastrous effects on urban and natural systems. The urban area ratio was increased by about 10%, i.e., from 2.5% in the year 1992 to 12.2% in 2022. In addition, the city will be expanded by 34%, 25% and 19% by the years 2032, 2042 and 2052, respectively. Therefore, this novel technique is recommended for integration with optimisation land use techniques to determine the sites that would be covered by the future city expansion.
Publisher: Hindawi Limited
Date: 2018
DOI: 10.1155/2018/2536327
Abstract: The current study proposes a new method for oil palm age estimation and counting from Worldview-3 satellite image and light detection and range (LiDAR) airborne imagery. A support vector machine algorithm (SVM) of object-based image analysis (OBIA) was implemented for oil palm counting. The sensitivity analysis was conducted on four SVM kernel types with associated segmentation parameters to obtain the optimal crown coverage delineation. Extracting tree’s crown was integrated with height model and multiregression methods to accurately estimate the age of trees. The multiregression model with multikernel sizes was examined to achieve the most optimized model for age estimation. Applied models were trained and examined over five different oil palm plantations. The results of oil palm counting had an overall accuracy of 98.80%, while the overall accuracy of age estimation showed 84.91%, over all blocks. The relationship between tree’s height and age was significant which supports the polynomial regression function (PRF) model with a 3 × 3 kernel size for under 10–12-year-old oil palm trees, while exponential regression function (ERF) is more fitted for older trees (i.e., 22 years old). Overall, recent remote sensing dataset and machine learning techniques are useful in monitoring and detecting oil palm plantation to maximize productivity.
Publisher: IEEE
Date: 04-2011
Publisher: IEEE
Date: 10-2013
Publisher: Copernicus GmbH
Date: 30-03-2015
Abstract: Abstract. Modelling and classification difficulties are fundamental issues in natural hazard assessment. A geographic information system (GIS) is a domain that requires users to use various tools to perform different types of spatial modelling. Bivariate statistical analysis (BSA) assists in hazard modelling. To perform this analysis, several calculations are required and the user has to transfer data from one format to another. Most researchers perform these calculations manually by using Microsoft Excel or other programs. This process is time-consuming and carries a degree of uncertainty. The lack of proper tools to implement BSA in a GIS environment prompted this study. In this paper, a user-friendly tool, bivariate statistical modeler (BSM), for BSA technique is proposed. Three popular BSA techniques, such as frequency ratio, weight-of-evidence (WoE), and evidential belief function (EBF) models, are applied in the newly proposed ArcMAP tool. This tool is programmed in Python and created by a simple graphical user interface (GUI), which facilitates the improvement of model performance. The proposed tool implements BSA automatically, thus allowing numerous variables to be examined. To validate the capability and accuracy of this program, a pilot test area in Malaysia is selected and all three models are tested by using the proposed program. Area under curve (AUC) is used to measure the success rate and prediction rate. Results demonstrate that the proposed program executes BSA with reasonable accuracy. The proposed BSA tool can be used in numerous applications, such as natural hazard, mineral potential, hydrological, and other engineering and environmental applications.
Publisher: IEEE
Date: 05-2008
Publisher: Walter de Gruyter GmbH
Date: 11-06-2015
Abstract: The aim of this study is to test and compare twoprobabilistic based models (frequency ratio and weightsof-evidence) with regard to regional gold potential mappingat Kelantan, Malaysia. Until now these models havenot been used for the purpose of mapping gold potentialareas in Malaysia. This study analyzed the spatial relationshipbetween gold deposits and geological factors such aslithology, faults, geochemical and geophysical data in geographicalinformation system (GIS) software. About eight(8) gold deposits and five (5) related factors are identifiedand quantified for their spatial relationships. Then, all factorswere combined to generate a predictive gold potentialmap. The predictive maps were then validated by comparingthem with known gold deposits using receiver operatingcharacteristics (ROC) and “area under the curve”(AUC) graphs. The results of validation showed accuraciesof 80% for the frequency ratio and 74% for the weightsof-evidence model, respectively. The results demonstratedthe usefulness of frequency ratio and weights-of-evidencemodeling techniques in mineral exploration work to discoverunknown gold deposits in Kelantan, Malaysia.
Publisher: Emerald
Date: 23-02-2010
DOI: 10.1108/09653561011022144
Abstract: The purpose of this research is to produce the landslide susceptibility map of Fraser's Hill and its surroundings in Pahang (Malaysia), utilizing remote sensing data and Geographic Information System (GIS) as a way to monitor sustainable highland development. Ancillary data are collected, processed, and constructed into a spatial database in a GIS platform to produce the satellite image. The factors chosen that influence landslide occurrence are land cover, vegetation index (NDVI), precipitation, and geology. Landslide‐hazardous areas are analyzed and mapped using the landslide‐occurrence factors through the heuristic approach Analytic Hierarchy Process (AHP). It is demonstrated that the integration of remote sensing data and GIS database is of assistance in managing land‐use planning of sustainable development. The verification with the existing landslides record shows a noteworthy accuracy. The list of data/maps reflects a considerable understanding of the basic cartographic information that is needed to effectively deal with the landslide problem. This approach indicates a potential long‐term application of remote sensing and GIS in managing sustainable highland development by monitoring the hazard‐susceptibility area. The value of the work is in its integration and utilization of remote sensing and GIS to provide sustainable development which can be developed to aid landslide warning systems.
Publisher: Springer Science and Business Media LLC
Date: 09-07-2013
Publisher: MDPI AG
Date: 25-02-2023
Abstract: 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) models were developed and compared for dengue prediction in Malaysia: LSTM, stacked LSTM (S-LSTM), LSTM with temporal attention (TA-LSTM), S-LSTM with temporal attention (STA-LSTM), LSTM with spatial attention (SA-LSTM), and S-LSTM with spatial attention (SSA-LSTM). The models were trained and evaluated on a dataset of monthly dengue cases in Malaysia from 2010 to 2016, with the task of predicting the number of dengue cases based on various climate, topographic, demographic, and land-use variables. The SSA-LSTM model, which used both stacked LSTM layers and spatial attention, performed the best, with an average root mean squared error (RMSE) of 3.17 across all lookback periods. When compared to three benchmark models (SVM, DT, ANN), the SSA-LSTM model had a significantly lower average RMSE. The SSA-LSTM model also performed well in different states in Malaysia, with RMSE values ranging from 2.91 to 4.55. When comparing temporal and spatial attention models, the spatial models generally performed better at predicting dengue cases. The SSA-LSTM model was also found to perform well at different prediction horizons, with the lowest RMSE at 4- and 5-month lookback periods. Overall, the results suggest that the SSA-LSTM model is effective at predicting dengue cases in Malaysia.
Publisher: Institute of Research and Community Services Diponegoro University (LPPM UNDIP)
Date: 25-04-2018
DOI: 10.14710/GEOPLANNING.5.1.17-34
Abstract: Natural and anthropogenic activities surrounding a Protected Area (PA) may cause its natural area to change in terms of Land Use-Land Cover (LULC). Thus, there is need of environmental change monitoring within and around PA because of its significant values to ecosystem at conservation scales. Effects and influences of local community within and around PA turn into the major problems for natural resource and conservations management as well as environmental impact assessment. Ascertaining the complex interface in relations to changes and its driving factors over period of time within and around PA is significant in order to predict future LULC changes, build alternative scenarios and serve as tools for decision making. The main objective of this work was to evaluate temporal change detection and prediction of LULC as well as the trends of changes from 1989 to 2016 within and around Krau Wildlife Reserve (KWR). The cloud issues were mitigated by producing cloud free image and object-based image analysis (OBIA) was adopted after a comparison with pixel-based analysis for overall accuracy and kappa statistics. The comparison of classified maps had produced a satisfactory results of overall accuracies of 91%, 86% and 90% for 1989, 2004 and 2016 respectively. The natural/dense forest between periods of 1989-2016 was decreased whereas built-up and agricultural/sparse forest were increased. The simulation model of Land Change Modeler (LCM) was utilized with digital elevation model (DEM) and past LULC maps to project future LULC pattern using Markov chain. The predicted map trend showed an increase of dense forest converted to agricultural/sparse forest in the north-western, and urban/built-up in east-southern part of KWR. The study is important for the conservation of habitat species and monitoring the current status of the KWR
Publisher: Elsevier BV
Date: 10-2018
Publisher: Science Publications
Date: 06-2009
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Helmi Zulhaidi Mohd Shafri.