This study aims to design a thematic map for Marina ParkCity, Miri, which needs a creative running route map using the GIS cartographic method. The proposed map was created using geospatial methods and tested for the map's effectiveness and practicality. The Miri City Council (MCC) recommended using the map from the Berlin Marathon 2010 to guide the map's creation. However, the Marathon map still needs improvement in cartographic elements and design principles such as legend, scale, and visual contrast. The respondents also agreed that the proposed map should be developed in the park, as it meets their criteria for a decent map design and practical needs, particularly for enhancing the runner experience and safety in the park. The study also recommends displaying distance and elevation information on the map for engaging and safe exploration.
This study investigates a cross-sensor point cloud registration methodology, utilizing roadmarks as robust semantic features due to their consistent visibility and uniform distribution in urban areas. The approach integrates data from ground-based mobile mapping systems (MMS) and unmanned aerial vehicles (UAVs). Key steps include selecting roadmarks, establishing graph nodes at their centroids, forming geometric structures based on node distances and angles, applying the Hungarian algorithm for bipartite graph matching, and minimizing an energy function to ensure geometric consistency. Experimental results, using data from a Velodyne VLP32 laser scanner and a DJI P4P UAV, show that the method achieves a 70
In the context of developing a framework for marine governance based on transdisciplinary research supporting the Global Ocean Decade, and to contribute to the development of a Marine Administration Domain model (MADM), there is a need to understand the differences between managing land and water and constructing relevant information models to support this. Given this need, this study aims at generating insights in how which conceptual and practical challenges, opportunities, dilemmas and contradictions exist and/or emerge when converting conventional monodisciplinary management and information management practices to integrated land and water (information) management. We formulate a set of key principles which guide the evaluation of transdisciplinary challenges. We approached the description of the problems and challenges in both land and water management from 5 perspectives: spatial /administrative domain modelling, participation and relevant actors, ontological and conceptual definitions, legislative formulations, practices, contradictions and redundancies, and practical case-based challenges, problems and inconsistencies. We conclude that a conversion to integrated land and water (and forest) management with the associated administrative domain models requires a complete rethinking of transdisciplinary integration and transformation. Nonetheless, the urgency of the global environmental problems requires a rethinking process. The principles of transdisciplinary research can help in this transition.
This paper assessed the 2005 drought impact on water yield deficit at sub-watershed level of Peninsular Malaysia using satellite-based algorithm. Year 2005 has been identified as one of the warmest years by the National Oceanic and Atmospheric Administration and Malaysia has been severely affected by large synoptic drought encompassing all the states in Peninsular Malaysia. The satellite-based run off model developed by Hashim et al. (2016) and Mahmud (2012) has been adapted with appropriate data from 2000 and 2007. The use of simplistic original water balance equation was able to simulate the run-off with good agreement with in-situ (NSME = 0.3; Bias = 37
Tourism is a significant source of income and employment for locals, particularly in rural areas like Sri Aman in Sarawak. However, the region may not be documented as a tourist attraction due to outdated promotion, lack of infrastructure, and lack of interactive geospatial web-based maps. This study developed an interactive web-based tourist system using a Geographical Information System (GIS). The system entailed user requirement analysis (URA), database system design, data collection, web-based development, and system assessment. The system underwent testing by distributing questionnaires to 24 respondents, with over 95
The extent to which digitalisation objectives and processes of land administration and land management are effective depends on multiple factors, such as (inter) organizational conditions, applicable legal and budgetary frameworks of their ecosystems and technological developments and dependencies.
This paper investigates the performance of CORS in UAV mapping in the State of Sarawak, Malaysia. The location of the study area is near equatorial, which is heavily affected by the troposphere conditions. As Sarawak moves into using a geocentric datum from a topocentric datum, no study has been conducted on the performance of CORS in UAV mapping. The dependency on coordinates obtained from the topocentric datum is still widely used for UAV missions. This study analyses the comparative differences contributing to the distance-dependent factor and attempts to tabulate the mapping applications that can be used based on the outcomes.
The current development approach in Malaysia is moving towards vertical development with the need for 3D elements in its land administration system. Rapid development with a vertical development approach, which consists of stratified development and multi-layered development, requires a solid foundation on the land administration legal framework and surveying practices. This research is an initial study on the legal framework for 3D development, attempting to meet the needs of such development while providing legal framework insight into the 3D aspect. This research is conducted through fully qualitative methods focusing on the legal aspect with critical reference to the land law in Malaysia involving the National Land Code [Act 828] and assessing the government initiative towards a multi-layered development approach. This research proposed that the current legal framework should include the initiative of 3D development in its foundation for the rights and ownership of the land.
Replanting in oil palm cultivation is essential for introducing new planting materials and improving infrastructure such as roads, drainage, terrace design, and planting density. This process is required every 25 years. Adopting a digital replanting blueprint is crucial for the Malaysian Oil Palm industry to tackle challenges like labor shortages and limited expertise. Planning for replanting should begin at least six months in advance. This study focuses on creating a precise replanting blueprint using advanced geospatial technology to maximize yield potential. The blueprint includes detailed designs for roads, drainage, terraces, and planting points, adhering to ARM standards, and undergoes statistical analysis for accuracy. Geospatial and remote sensing technologies, such as LiDAR and ortho imagery, are pivotal in this process. Tools like GIS and high-precision GPS instruments (EOS Arrow Gold and HIPER VR GNSS) ensure design reliability, with a variance of less than 0.5 m in most cases. The study finds that 65
There is a contention between the Federal Government and the Sarawak Government over the legitimacy of the Sarawak (Alteration of Boundaries) Order in Council (OIC) 1954 and its relation to the Territorial Sea Act 2012 (TSA 2012). The Sarawak OIC 1954, enacted under the Colonial Boundaries Act 1895, extended Sarawak's boundaries to cover the “continental shelf” and allowing resource exploration. The historical and legal implications of the OIC for Sarawak's maritime boundary and its alignment with international law, particularly the 1958 Geneva Convention on the Continental Shelf and the United Nations Convention on the Law of the Sea (UNCLOS) 1982 remain unclear. This paper aims to provide an initial understanding of the Sarawak OIC 1954 by examining its history and archives to clarify the complexities of this important document. It offers insights based on archived UK documents from 1945 to 1974, exploring the historical legitimacy and motivations behind the OICs and their role in delineating maritime boundaries adjacent to Sarawak.
Sandy beaches are well-known for their dynamic and complex environments. Traditional survey methods in these areas have limitations, such as requiring significant manpower, being time-consuming, and potentially expensive. These limitations have introduced the use of a more innovative and cost-effective, called Unmanned Aerial Vehicle (UAV). Generally, UAV Photogrammetry mapping requires Ground Control Points (GCPs). However, placing GCPs on beaches can be challenging due to accessibility issues and environmental factors. One practical method to overcome the issue is Post-Processing Kinematics (PPK) UAV Photogrammetry. The purpose of this study is to determine the optimal altitude for PPK UAV Photogrammetry beach mapping. The study uses CHCNAV AA450 for PPK UAV Photogrammetry and Light Detection and Ranging (LiDAR) mapping since the sensor has a built-in camera for aerial mapping. Three altitudes for PPK UAV Photogrammetry were selected, specifically 60 m, 100 m, and 140 m, over a horizontal distance of approximately 2 km. Data processing uses CoPre for LiDAR and Agisoft for PPK UAV Photogrammetry, while data analysis uses Global Mapper for volume estimation. The volume estimations calculated from Triangulated Irregular Network (TIN) and the results from PPK UAV Photogrammetry were compared to the result from LiDAR, as LiDAR can provide precise elevation data using high-resolution 3D point clouds acquired directly during data collection. The volume analysis demonstrates that the lowest altitude does not necessarily correspond to the optimal altitude for data collection. The accuracy assessment reveals that the 100 m altitude yields the most reliable results for volumetric measurements.
The continuous access to high-resolution all-weather, day-and-night large-scale synthetic aperture radar (SAR) data from the Sentinel-1 SAR, a critical satellite mission under the European Union's Copernicus program, has revolutionised satellite-based environmental monitoring applications, including three-dimensional (3D) mapping of the earth surface over the last decade. Consequently, synthetic aperture radar interferometry (InSAR) has emerged as a valuable technique for Earth’s surface Digital Elevation Model (DEM) generation using the Sentinel-1 SAR, especially in the arid and semi-arid regions. However, in the vegetated humid tropics (VHT), where InSAR holds the promise of mitigating the persistent cloud cover challenges associated with especially optical satellite-based DEM generation in the humid tropics, InSAR-DEM generation using Sentinel-1 SAR has remained challenging due to coherence constraints and the difficulties of acquiring image pairs with optimal baseline characteristics. This study assesses the impact of perpendicular and temporal baselines on Sentinel-1 InSAR coherence in VHT. The result revealed that while the optimal perpendicular baseline for achieving optimal coherence is between 201 m and 202 m, the optimal perpendicular baseline for generating InSAR-DEM with optimal precision is between 176 m and 188 m. The study also affirms the smaller the temporal baseline the better for improved coherence and precision of InSAR-DEM, with a critical temporal baseline of 36 days revealed. The findings shall provide a guide to the geoscientific community on InSAR-DEM generation and other InSAR applications with Sentinel-1 in the VHT especially on perpendicular and temporal baselines consideration.
In the present study, we address the urgent need for an accurate assessment of building damage in the aftermath of the recent earthquake in Marrakech, Morocco. Assessing the damage is necessary to determine the impact on buildings since they bear the brunt of most natural disasters. The aim of this study is to enhance the efficiency of detecting house damage by utilizing UAV images and advanced deep-learning algorithms. The visual surface characteristics were used to categorize the impact of the post-earthquake UAV imagery into multiple classes using the state-of-the-art SOTA model, YOLOv9. The methodology employed involved preparing an annotated dataset of 337 images and dividing it into training (70
Assessing the impact and efficiency of cadastral modernisation on coordinate systems for orthophoto production is a complex yet vital task. Cadastral modernisation involves the updating and digitalising of land records, which is crucial for accurate orthophoto production. The coordinate system used in cadastral modernisation greatly influences the accuracy and efficiency of orthophoto production. Therefore, a comprehensive evaluation of this relationship is of great significance. In this context, using accurate coordinate systems is paramount for ensuring that orthophotos are mapped correctly onto the digital cadastral data. This study analysed the output of the coordinate system from orthophoto production that was mapped to the cadastral plan to become the 3D cadastral. Apart from that, the significant use of the integration of cadastral plan and orthophoto output for urban planning analysis. Currently, the preparation of site analysis and the development plan proposal by the urban planners in the department only utilise the output from the 2D cadastral plan and orthophoto plan without a clear methodology which is unpractical, unrealistic, and unresponsive to the environment. From these challenges, the study analyses the significant use of the orthophoto output for the urban planners to do the slope and contour analysis and volume analysis for the new development project and analyse how effectively the existing development complies with the town planning guidelines. The study is based on the results obtained from the UAV surveys conducted at Kampung Santubong, Kuching, Sarawak, by improving the survey and mapping practices through the SGeD20 coordinate system. The findings of this study have explored how the cadastral plan can be overlayed with 3D spatial data to facilitate the use of urban planning analysis.
The ocean tide model is critical for determining accurate vertical datum transformations with high accuracy and resolution. The advent of satellite altimeters has increased the accuracy of tidal predictions for the open ocean by around 2 cm. However, there are significant variations in shallow water and coastal zones, which could correspond to the complex hydrodynamic systems and a lack of high-quality satellite altimetry data. Hence, an overview of the global tide model is performed to evaluate the performance of global ocean tide models in open ocean and coastal areas. This paper attempts to review the performance of several tide models based on the previous comprehensive tests. Such an attempt foresees the potential application of local hydrodynamic models for accurate tidal prediction and could provide an opportunity to produce a seamless hydrographic separation model in coastal and offshore areas, especially in Malaysian waters.
Integration of AI and geo-location technologies can transform healthcare services by providing more personalised and efficient recommendations. Current healthcare facility locators prioritise geographic proximity over specialised medical service. Due to reliance on static databases and needing more personalised, condition-centric matching, individuals need help finding suitable care based on specific conditions. Traditional systems often need to incorporate real-time data and advanced AI, limiting accuracy and usefulness. Recent advancements, particularly in GPT-3.5 and GPT-4, offer new opportunities for enhancing healthcare service recommendations. This study aims to develop a GeoAI-augmented web platform to recommend healthcare facilities for minor illnesses such as toothache, fever, headache, and stomachache. The platform combines AI-enhanced geo-location with precise condition-centric matching to improve user experience and accuracy, utilising a fine-tuned GPT-3.5 model to enhance interaction and generate relevant prompts. The system integrates OSM data to provide tailored facility suggestions. Methodologies include data collection of user prompts, model ne-tuning, expert reviews for relevance and accuracy, user surveys for satisfaction, and performance testing for response times. Comparative analysis between GPT-3.5 and GPT-4 assesses relevance, accuracy, user satisfaction, and response times. Results indicate that GPT-4 achieves higher accuracy and relevance, improved user satisfaction, and more contextually accurate responses compared to GPT-3.5, though with slightly longer response times. The study demonstrates GPT-4’s superior performance in generating accurate healthcare facility recommendations. The anticipated outcome is a chatbot-like web tool offering customised healthcare recommendations within specific regions, emphasising ease of use, precision, and contextual relevance. This study highlights the potential of AI and geo-location technologies in providing more personalised and effective healthcare solutions.
Data fusion has been employed to combine multiple point cloud datasets to generate more comprehensive 3D building models. However, discrepancies among datasets can result in unresolved holes, reducing model accuracy. This study aims to enhance data fusion techniques using the advanced Laplacian method, known for its multi-view 3D hole extraction capability, to address simple holes with an additional dataset. The fused 3D building model is derived from three-point cloud datasets, and the automated procedure identifies and restores holes through two main phases: 3D hole extraction and Laplacian data fusion. Python scripting facilitates the application of the Laplacian technique. Experiments on a small part of the 3D model showed that simple holes were covered more accurately by substitute points, with CloudCompare evaluations indicating a significant reduction in mean distance to the target: from 0.45 m to 0.29 m for ALS points and from 0.53 m to 0.31 m for image-based points. Future research will address complex holes, incorporating additional constraints and line extraction from image data to accurately reshape these intricate gaps.
This paper presents a methodology for enhancing the segmentation of 3D building models using the BuildingGNN method and converting these segmented outputs to CityGML, the standardised format for 3D city models. The process involves leveraging a pre-trained BuildingGNN model to segment various components of 3D building models, such as walls, roofs, windows, and doors. Key challenges addressed include georeferencing the segmented models using affine transformations and ground control points (GCPs) to ensure real-world positioning and geometry validation to comply with CityGML specifications. The segmented models were converted into 3D tiles to visualise their integration within a geographic context, demonstrating their location and orientation on a map. This approach provides a practical means for urban planning and geographic information systems by delivering precise and semantically rich 3D building models. The paper discusses the methods used, the challenges encountered, and the solutions implemented to enhance the conversion process.
Road accidents significantly contribute to traffic congestion, causing severe impacts on public health, the environment, and the economy. This study focuses on mapping road accident susceptibility in Casablanca using machine learning algorithms integrated with Geographic Information Systems (GIS). By analysing spatial relationships between various causality factors and accident pixels, models such as Random Forest (RF), Support Vector Machine (SVM), K-nearest neighbours (KNN), and Artificial Neural Network (ANN) were employed. The resulting predictive maps, particularly from the RF model with an AUC of 0.884, demonstrate high accuracy and are crucial tools for road safety management. These susceptibility maps are essential for optimizing planning, management, and safety measures on roadways, offering proactive solutions to mitigate traffic accidents in urban environments.
Sea surface salinity (SSS) is a crucial but often overlooked oceanic parameter that significantly influences the environment, mainly marine ecosystems. It plays a vital role in shaping the water cycle, predicting the global water balance, and enhancing our comprehension of ocean currents. Generally, Sea Surface Salinity (SSS) measurements obtained through in situ techniques, such as those conducted by ships, moored buoys, and profiling floats, are unable to provide extensive spatial and temporal resolution datasets. The advancement in space-based observation has introduced Sea Surface Salinity (SSS) measurements through the Soil Moisture and Ocean Salinity (SMOS) mission, enabling global SSS measurements. This information is crucial for climate studies, weather monitoring, and coastal surveillance. This paper focuses on evaluating the seasonal variability of sea surface salinity in Malaysian waters utilising data from the Soil Moisture and Ocean Salinity (SMOS) mission. The study extracts two years of corrected SSS data (from January 2022 to December 2023) from the European Space Agency (ESA) website. The extracted data is used to assess the SSS changes due to seasonal monsoon. Two major monsoons assessed include the Southwest monsoon (SW) and Northeast monsoon (NE). The analysis of seasonal variability reveals that the coastal area of the Malaysian Sea exhibits higher sea surface salinity during the SW monsoon compared to the NE monsoon. The study demonstrates that seasonal changes in Malaysia play a pivotal role in influencing the variations in sea surface salinity (SSS) over the Malaysian waters. Consequently, the findings of this paper could offer valuable insights into SSS conditions for local authorities, aiding in the mitigation and monitoring of the effects of climate change.
