DIGITAL TWIN FOR INDOOR DISASTER IN SMART CITY: A SYSTEMATIC REVIEW

Technology has advanced and progressed tremendously, and the term city is being elevated to a new level where the smart city has been introduced globally. Recent developments in the concept of smart city have led to a renewed interest in Digital Twin. Using precise Building Information Modelling (BIM) consolidated with big data and sensors, several attempts have been made to establish digital twin smart cities. In recent years, several researchers have sought to determine the capability of smart city and digital twin for various taxonomies such as development and urban planning purposes, built environment, manufacturing, environmental, disaster management, and healthcare. Despite being beneficial in many disciplines, especially in manufacturing, built environment, and urban planning, these existing studies have shown a lack of aspect in terms of emergency or disaster-related as opposed to the elements mentioned above. This is because the researcher has not treated emergencies or disasters in much detail. Therefore, an extensive review on smart city, digital twin, BIM and disaster management and technology that revolves around these terms were summarised. In general, 39 articles from prominent multidisciplinary databases were retrieved over the last two decades based on the suggested PRISMA workflow. These final articles were analysed and categorised into four themes based on the research content, gist, and keywords. Based on the review of 39 articles related to smart city, digital twin and BIM, a workflow for the smart city digital twin and the conceptual framework for indoor disaster management was proposed accordingly. The establishment of smart city digital twins solely for an indoor emergency can be beneficial to urbanites, and it could provide numerous benefits for enhanced situation assessment, decision making, coordination, and resource allocation.

A COMPARISON OF TURKEY-MALAYSIA LADM COUNTRY PROFILE FOR 3D CADASTRE PURPOSES

This paper summarises the comparison of Turkish and Malaysian cadastral registration systems based on the Land Administration Domain Model (LADM, ISO 2012) associated with 2D and 3D cadastral situations. Literature review shows that many countries propose their profile based on the LADM, such as The Netherlands, Australia/ Queensland, China, Greece and others. Turkey and Malaysia are some of the potential candidates for the LADM based country profile, as described in this paper. The study presents a detailed overview of the Turkish and Malaysian cadastral system, and LADM-based country profiles developed by the two countries are compared thanks to the common ontology offered by LADM.

IMPACT OF THE PORT STRUCTURE IN THE SPATIO-TEMPORAL EVOLUTION OF THE SEDIMENTARY AND BATHYMETRIC CHARACTERISTICS OF A MOROCCAN ATLANTIC BAY, STUDY CASE BAY OF SAFI CITY

This study focuses on morpho-sedimentary changes in the bay of Safi (Atlantic coast of Morocco), due to a progressive extension of the port. For this purpose, several bathymetric and sedimentary surveys carried out by the Hydrographic and Oceanographic Service of the Navy (SHOM) in 1892, 1906 and 1940 respectively, coupled with a bathymetric and sedimentary measurement mission in 2009, were analyzed to understand the impact of the port developments on the bottom of Safi Bay. This analysis consists of making maps of the evolution of (i) sedimentary facies (of different dates 1892, 1906, 1940 and 2009) and (ii) the shallow seabed of the three periods 1892–1906, 1906–1940 and 1940–2009. The sedimentary facies maps show that the facies appear unstable and evolve intermittently in response to environmental changes in the bay (port construction and expansion). In addition, the overlay of the bathymetric maps indicates that the bay has undergone changes (lowering, stability, and raising) controlled by hydrodynamic conditions before, during, and even after harbor construction. Analysis of the data showed that the expansion of the port often reshaped the morphology of the bay's seabed. The consequences of these evolutions are the appearance of the fattening or the erosion of the bank and the filling of small depressions of sediments. This evolution is reflected in the modification of the funds near the port and the beach of Safi.

DEEP LEARNING APPROACH FOR URBAN MAPPING

Researchers in computer vision and machine learning are becoming increasingly interested in image semantic segmentation. Many methods based on convolutional neural networks (CNNs) have been proposed and have made considerable progress in the building extraction mission. This other methods can result in suboptimal segmentation outcomes. Recently, to extract buildings with a great precision, we propose a model which can recognize all the buildings and present them in mask with white and the other classes in black. This developed network, which is based on U-Net, will boost the model's sensitivity. This paper provides a deep learning approach for building detection on satellite imagery applied in Casablanca city, Firstly, to begin we describe the terminology of this field. Next, the main datasets exposed in this project which’s 1000 satellite imagery. Then, we train the model UNET for 25 epochs on the training and validation datasets and testing the pretrained weight model with some unseen satellite images. Finally, the experimental results show that the proposed model offers good performance obtained as a binary mask that extract all the buildings in the region of Casablanca with a higher accuracy and entirety to achieve an average F1 score on test data of 0.91.

DEVELOPMENT OF 3D MARINE CADASTRE DATA MODEL BASED ON LAND ADMINISTRATION DOMAIN MODEL

A new version of the Land Administration Domain Model (LADM) has been discussed and is under further development in ISO/TC 211 on Geographic Information. One of the extending parts is where the model can accommodate complex and advanced marine properties and cadastral objects. Currently, the fundamentals part of this new version (LADM Edition II) has been examined by the committee, and a few elements need to be considered, especially for marine space georegulation. Based on the possibility of embedding LADM with marine cadastre as agreed by several researchers, the concept of marine cadastre data model within land administration context has been anticipated in many countries (e.g., Canada, Greece, Turkey, Australia, and Malaysia). Part of the research focused on constructing and developing the appropriate data models to manage marine spaces and resources most effectively. Several studies have attempted to establish a conceptual model for marine cadastre in Malaysia. However, there is still no acceptable marine data model. Thus, this paper proposed a marine data model for Malaysia based on the international standard, LADM. The approach, by definition, can be applied to the marine environment in terms of controlling and modelling a variety of rights, responsibilities, and restrictions. The Unified Modelling Language (UML) application was utilized to construct the conceptual and technical models via Enterprise Architect as part of the validation process. The data model was constructed within the marine's concept in Malaysia to meet international standards. The features of the data model were also discussed in the FIG workshop (9th LADM International Workshop 2021). The experiment on the data model also includes 3D visualization and simple query.

ANALYSIS OF VISIBLE INFRARED IMAGING RADIOMETER SUITE CAPABILITY FOR POPULATION ESTIMATION ON JAVA ISLAND

Population data, despite their significance, are often missing or difficult to access, especially in cities/regencies not belonging to the metropolitan areas or centers of various human activities. This hinders practices that are contingent on their availability. In this study, population estimation was carried out using nighttime light imagery generated by the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument. The variable illuminated area was integrated with the population data using linear regression based on an allometric formula so as to produce a regression value, correlation coefficient (r), and coefficient of determination (r2). The average r2 between the illuminated area and the total population was 0.86, indicating a strong correlation between the two variables. Validation using samples of population estimates from three different years yielded an average error of 73% for each city and 7% for the entire study area. The estimation results for the number of residents per city/regency cannot be used as population data due to the high percent error, but for the population on a larger regional scale, in this case, the island of Java, they have a much smaller percent error and can be used as an initial picture of the total population.

CARTOGRAPHY AND MAPPING FOR PRECISION HEALTH: REVIEW OF ESSENTIAL ELEMENTS FOR DISEASE MAPPING

A well-developed healthcare system, decent access to clean water and sanitation, and programmes to eliminate poverty and build modern infrastructure are essential components to create healthier Malaysia's population. Non-communicable diseases currently account for most of the mortality and morbidity, although communicable diseases such as dengue fever, avian flu and covid-19 still pose a threat. The World Health Organization (WHO) identified COVID-19 is a rare pneumonia disease that originated in Wuhan, on January 12, 2020, before it became an outbreak in all countries including Malaysia. The requirement of a precise mapping and Cartography for the accurate disease mapping and data management are crucial due to a precise map gives higher resolution of the data and for more specific data analysis, interpretation and decision making process. In Malaysia, there no specific report on precise mapping for health applications, and it is therefore this paper is to identify the potential criteria and factors needed for precise health mapping applications. A precise health mapping is essential to create a precise risk map towards the surveillance and signal detection, predicting future risk, targeted interventions, and understanding disease phenomena.

IFC TO CITYGML CONVERSION ALGORITHM BASED ON GEOMETRY AND SEMANTIC MAPPING

Geographic information system (GIS) is known traditionally for the modelling of two-dimensional (2D) geospatial analysis and therefore present information about the extensive spatial framework. On the other hand, building information modelling (BIM) is digital representation of building life cycle. The increasing use of both BIM and GIS simultaneously because of their mutual relationship, as well as their similarities, has resulted in more relationships between both worlds, therefore the need for their integration. A significant purpose of these similarities is importing BIM data into GIS to significantly assist in different design-related issues. However, currently this is challenging due to the diversity between the two worlds which includes diversity in coordinate systems, three-dimensional (3D) geometry representation, and semantic mismatch. This paper describes an algorithm for the conversion of IFC data to CityGML in order to achieve the set goal of sharing information between BIM and GIS domains. The implementation of the programme developed using python was validated using an IFC model (block HO2) of a student’s hostel, Kolej Tun Fatima (KTF). The conversion is based on geometric and semantic information mapping and the use of 3D affine transformation of IFC data from local coordinate system (LCS) to CityGML world coordinate system (WCS) (EPSG:4236). In order to bridge the gap between the two data exchange formats of BIM and GIS, we conducted geometry and semantic mapping. In this paper, we limited the conversion of the IFC model on level of details 2 (LOD2). The conversion will serve as a bridge toward the development of a software that will perform the conversion to create a strong synergy between the two domains for purpose of sharing information.

GPU UTILIZATION IN GEOPROCESSING BIG GEODATA: A REVIEW

The expansion of data collection from remote sensing and other geographic data sources, as well as from other technology such as cloud, sensors, mobile, and social media, have made mapping and analysis more complex. Some geospatial applications continue to rely on conventional geospatial processing, where limitation on computation capabilities often lacking to attain significant data interpretation. In recent years, GPU processing has improved far more GIS applications than using CPU alone. As a result, numerous researchers have begun utilising GPUs for scientific, geometric, and database computations in addition to graphics hardware use. This paper summarizes parallel processing concept and architecture, the development of GPU geoprocessing for big geodata ranging from remote sensing and 3D modelling to smart cities studies. This paper also addresses the GPU future trends advancement opportunities with other technologies, machine learning, deep learning, and cloud-based computing.

MODELLING THE CHLOROPHYLL-A CONCENTRATION OF LAGUNA LAKE USING HIMAWARI-8 SATELLITE IMAGERY AND MACHINE LEARNING ALGORITHMS FOR NEAR REAL TIME MONITORING

Recent studies have investigated the use of satellite imaging combined with machine learning for modelling the Chlorophyll-a (Chl-a) concentration of bodies of water. However, most of these studies use satellite data that lack the temporal resolution needed to monitor dynamic changes in Chl-a in productive lakes like Laguna Lake. Thus, the aim of this paper is to present the methodology for modelling the Chl-a concentration of Laguna Lake in the Philippines using satellite imaging and machine learning algorithms. The methodology uses images from the Himawari-8 satellite, which have a spatial resolution of 0.5–2 km and are taken every 10 minutes. These are converted into a GeoTIFF format, where differences in spatial resolution are resolved. Additionally, radiometric correction, resampling, and filtering of the Himawari-8 bands to exclude cloud-contaminated pixels are performed. Subsequently, various regression and gradient boosting machine learning algorithms are applied onto the train dataset and evaluated, namely: Simple Linear Regression, Ridge Regression, Lasso Regression, and Light Gradient Boosting Model (LightGBM). The results of this study show that it is indeed possible to integrate algorithms in Machine Learning in modelling the near real-time variations in Chl-a content in a body of water, specifically in the case of Laguna Lake, to an acceptable margin of error. Specifically, the regression models performed similarly with a train RMSE of 1.44 and test RMSE of 2.51 for Simple Linear Regression and 2.48 for Ridge and Lasso Regression. The linear regression models exhibited a larger degree of overfitting than the LightGBM model, which had a 2.18 train RMSE.