Challenges in Coastal Spatial Data Infrastructure implementation: A review

The ability to cope with the complexity surrounding the coastal zone requires an integrated approach for  sustainable socio-economic development and environmental management. The concept of integrated coastal  zone management (ICZM) was advanced in response to this. In line with the success story of spatial data  infrastructure (SDI), initiatives are currently emerging to develop SDI for marine and coastal environment. The aim of this paper is to review emerging initiatives so as to identify the problems faced with  implementation and discuss the way forward. The result may support stakeholders, policy makers, academia, and the government to leverage on the experience of others for a robust and sustainable policy and action plans on coastal management.Keywords: Coastal SDI, Integrated Coastal Zone Management, environmental protection, spatial planning

Characterization of Macro- and Micro-Geomorphology of Cave Channel from High-Resolution 3D Laser Scanning Survey: Case Study of Gomantong Cave in Sabah, Malaysia

Three-dimensional documentation of hypogene cave morphology is one of the major applications of laser scanning survey. This chapter presents applications of terrestrial laser scanning (TLS) survey for analyzing endogenic cave passage geomorphologic structure and morphometry using 3D meshing, high-resolution 3D texture modeling for geovisualization, and its potential for cave art documentation. To achieve this, multi-scale resolution 3D models were generated; one using the mesh model for macro-morphological analysis and the other with the full-resolution scan to produce high quality 3D texture model for identification of micro-morphological features. The mesh model of the cave makes it possible to analyze the general shape, distinguish phreatic tube from post-speleogenetic modified conduits and carry out morphometric measurements including the cave volume and channel surface area. The 3D texture model provides true to live visualization of the cave with exceptionally high level of accuracy and details that would be impossible to obtain with direct observation by visiting the site or from the mesh model. The model allows discerning different speleogenetic phases, karstification processes and micro-morphologies such as wall and ceiling seepage, hanging rocks, fractures, scallops, ceiling flush dome, pockets, bell-hole and avens. Also, the texture model permits identifying cave arts and engravings along the passage

Building extraction for 3D city modelling using airborne laser scanning data and high-resolution aerial photo

Light detection and ranging (LiDAR) technology has become a standard tool for three-dimensional mapping because it offers fast rate of data acquisition with unprecedented level of accuracy. This study presents an approach to accurately extract and model building in three-dimensional space from airborne laser scanning data acquired over Universiti Putra Malaysia in 2015. First, the point cloud was classified into ground and non-ground xyz points. The ground points was used to generate digital terrain model (DTM) while digital surface model (DSM) was  produced from the entire point cloud. From DSM and DTM, we obtained normalise DSM (nDSM) representing the height of features above the terrain surface.  Thereafter, the DSM, DTM, nDSM, laser intensity image and orthophoto were  combined as a single data file by layer stacking. After integrating the data, it was segmented into image objects using Object Based Image Analysis (OBIA) and subsequently, the resulting image object classified into four land cover classes: building, road, waterbody and pavement. Assessment of the classification accuracy produced overall accuracy and Kappa coefficient of 94.02% and 0.88 respectively. Then the extracted building footprints from the building class were further processed to generate 3D model. The model provides 3D visual perception of the spatial pattern of the buildings which is useful for simulating disaster scenario for  emergency management

A decade of modern cave surveying with terrestrial laser scanning: a review of sensors, method and application development

During the last decade, the need to survey and model caves or caverns in their correct three-dimensional geometry has increased due to two major competing motivations. One is the emergence of medium and long range terrestrial laser scanning (TLS) technology that can collect high point density with unprecedented accuracy and speed, and two, the expanding sphere of multidisciplinary research in understanding the origin and development of cave, called speleogenesis. Accurate surveying of caves has always been fundamental to understanding their origin and processes that lead to their current state and as well provide tools and information to predict future. Several laser scanning surveys have been carried out in many sophisticated cave sites around the world over the last decade for diverse applications; however, no comprehensive assessment of this development has been published to date. This paper reviews the state-of-the-art three-dimensional (3D) scanning in caves during the last decade. It examines a bibliography of almost fifty high quality works published in various international journals related to mapping caves in their true 3D geometry with focus on sensor design, methodology and data processing, and application development. The study shows that a universal standard method for 3D scanning has been established. The method provides flexible procedures that make it adaptable to suit different geometric conditions in caves. Significant progress has also been recorded in terms of physical design and technical capabilities. Over time, TLS devices have seen a reduction in size, and become more compact and lighter, with almost full panoramic coverage. Again, the speed, resolution, and measurement accuracy of scanners have improved tremendously, providing a wealth of information for the expanding sphere of emerging applications. Comparatively, point cloud processing packages are not left out of the development. They are more efficient in terms of handling large data volume and reduced processing time with advanced and more powerful functionalities to visualize and generate different products

Fusion of airborne LiDAR with multispectral SPOT 5 image for enhancement of feature extraction using Dempster–Shafer theory

This paper presents an application of data-driven Dempster-Shafer theory (DST) of evidence to fuse multisensor data for land-cover feature extraction. Over the years, researchers have focused on DST for a variety of applications. However, less attention has been given to generate and interpret probability, certainty, and conflict maps. Moreover, quantitative assessment of DST performance is often overlooked. In this paper, for implementation of DST, two main types of data were used: multisensor data such as Light Detection and Ranging (LiDAR) and multispectral satellite imagery [Satellite Pour l'Observation de la Terre 5 (SPOT 5)]. The objectives are to classify land-cover types from fused multisensor data using DST, to quantitatively assess the accuracy of the classification, and to examine the potential of slope data derived from LiDAR for feature detection. First, we derived the normalized difference vegetation index (NDVI) from SPOT 5 image and the normalized digital surface model (DSM) (nDSM) from LiDAR by subtracting the digital terrain model from the DSM. The two products were fused using the DST algorithm, and the accuracy of the classification was assessed. Second, we generated a surface slope from LiDAR and fused it with NDVI. Subsequently, the classification accuracy was assessed using an IKONOS image of the study area as ground truth data. From the two processing stages, the NDVI/nDSM fusion had an overall accuracy of 88.7%, while the NDVI/slope fusion had 75.3%. The result indicates that NDVI/nDSM integration performed better than NDVI/slope. Although the overall accuracy of the former is better than the latter (NDVI/slope), the contribution of individual class reveals that building extraction from fused slope and NDVI performed poorly. This study proves that DST is a time- and cost-effective method for accurate land-cover feature identification and extraction without the need for a prior knowledge of the scene. Furthermore, the ability to generate ot- er products like certainty, conflict, and maximum probability maps for better visual understanding of the decision process makes it more reliable for applications such as urban planning, forest management, 3-D feature extraction, and map updating

Advanced differential interferometry synthetic aperture radar techniques for deformation monitoring: a review on sensors and recent research development

This paper reviews the advanced differential interferometry synthetic aperture radar (A-DInSAR) techniques, with two major components in focus. First is the basic concepts, synthetic aperture radar (SAR) data sources and the different algorithms documented in the literature, primarily focusing on persistent scatterers. In the second part, the techniques are compared in order to establish more linkage in terms of the variability of their applications, strength and validation of the interpreted results. Also, current issues in sensor and algorithm development are discussed. The study identified six existing A-DInSAR algorithms used for monitoring various deformation types. Generally, reports of their performance indicate that all the techniques are capable of measuring deformation phenomena at varying spatial resolution with high level of accuracy. However, their usability in suburban and vegetated areas yields poor results, compared to urbanized areas, due to inadequate permanent features that could provide sufficient coherent point targets. Meanwhile, there is continuous development in sensors and algorithms to expand the applicability domain of the technology for a wide range of deformable surfaces and displacement patterns with higher precision. On the sensor side, most of the latest SAR sensors employ longer wavelength (X and P bands) to increase the penetrating power of the signal and two other sensors (ALOS-2 PALSA-2 and SENTINEL-1) are scheduled to be launched in 2013. Researchers are investigating the possibility of using single-pass sensors with different look angles for SAR data collection. With these, it is expected that more data will be available for various applications. Algorithms such as corner reflector interferometry SAR, along track interferometry, liqui-InSAR, and squeeSAR are emerging to increase reliable estimation of deformation from different surfaces

Automatic keypoints extraction from UAV image with refine and improved scale invariant features transform (RI-SIFT)

In this study, the performance of Refine and Improved Scale Invariant Features Transform (RI-SIFT) recently developed and patented to automatically extract key points from UAV images was examined. First the RI- SIFT algorithm was used to detect and extract CPs from two overlapping UAV images. To evaluate the performance of RI-SIFT, the original SIFT which employs nearest neighbour (NN) algorithms was used to extract keypoints from the same adjacent UA V images. Finally, the quality of the points extracted with RI- SIFT was evaluated by feeding them into polynomial, adjust, and spline transform mosaicing algorithms to stitch the images. The result indicates that RI-SIFT performed better than SIFT and NN with 271, 1415, and 1557points extracted respectively. Also, spline transform gives the most accurate mosaicked image with subpixel RMSE value of 1.0925 pixels equivalent to 0.10051m, followed by adjust transform with root mean square error (RSME) value of 1.956821 pixel (0.17611m) while polynomial transform produced the least accuracy result

Maximizing urban features extraction from multi-sensor data with Dempster-Shafer theory and HSI data fusion techniques

This paper compares two multi-sensor data fusion techniques – Dempster-Sharfer Theory (DST) and Hue Saturation Intensity (HSI). The objective is to evaluate the effectiveness of the methods interm in space and time and quality of information extraction. LiDAR and hyperspectral data were fused using the two methods to extract urban land scape features. First, digital surface model (DSM), LiDAR intensity and hyperspectral image were fused with HSI. Then the result was classified into five classes (metal roof building, non-metal roof building, tree, grass and road) using supervised classification (minimum distance) and the classification accuracy assessment was done. Second, Dempster Shafer Theory (DST) utilized the evidences available to fuse normalized DSM, LiDAR intensity and hyperspectral derivatives to classify the surface materials into five classes as before. It was found out that DST perform well in the ability to discriminate different classes without expert information from the scene. Overal accuracy of 87% achieved using DST. While in HSI technique, the overal accuracy obtained was 74.3%. Also, metal and non-metal roof types were clearly classified with DST which, does not have a good result with HSI. A fundamental setback of HSI is its limitation to fusion of only two sensor data at a time whereas we could integrate different sensor data with DST. Besides, the time required to select trainimg site for supervised classificition, the accuracy of feature classification with HSI fused data is dependent on the knowledge of the analyst about the scene with the other one. This study shows DST to be an accurate and fast method to extract urban features and roof types. It is hoped that the increasing number of remote sensing technology transforming to era of redundant data will make DST a desired technique available in most commercial image processing software packages

Frontier in three-dimensional cave reconstruction—3D meshing versus textured rendering

Underground caves and their specific structures are important for geomorphological studies. This paper investigates the capabilities of a new modelling approach advanced for true-to-life three-dimensional (3D) reconstruction of cave with full resolution scan relative to 3D meshing. The cave was surveyed using terrestrial laser scanner (TLS) to acquire high resolution scans. The data was processed to generate a 3D-mesh model and textured 3D model using sub-sampled points and full resolution scan respectively. Based on both point and solid surface representation, comparative analysis of the strengths and weaknesses of the two approaches were examined in terms of data processing efficiency, visualization, interactivity and geomorphological feature representation and identification. The result shows that full scan point representation offers advantage for dynamic visualization over the decimated xyz point data because of high density of points and availability of other surface information like point normal, intensity and height which can be visualized in colour scale. For the reconstructed surface, mesh model is better with respect to interactivity and morphometric but 3D rendering shows superiority in visual reality and identification of micro detail of features with high precision. Complementary use of the two will provide better understanding of the cave, its development and processes

Urban Planning Using a Geospatial Approach: A Case Study of Libya

Large scale developmental projects firstly require the selection of one or more cities to be developed. In Libya, the selection process is done by selected organizations, which is highly influenced by human judgement that can be inconsiderate of socioeconomic and environmental factors. In this study, we propose an automated selection process, which takes into consideration only the important factors for city (cities) selection. Specifically, a geospatial decision-making tool, free of human bias, is proposed based on the fuzzy overlay (FO) and technique for order performance by similarity to ideal solution (TOPSIS) techniques for development projects in Libya. In this work, a dataset of 17 evaluation criteria (GIS factors) across five urban conditioning factors were prepared. The dataset served as input to the FO model to calculate weights (importance) for each criterion. A support vector machine (SVM) classifier was then trained to refine weights from the FO model. TOPSIS was then applied on the refined results to rank the cities for development. Experimental results indicate promising overall accuracy and kappa statistics. Our findings also show that highest and lowest success rates are 0.94 and 0.79, respectively, while highest and lowest prediction rates are 0.884 and 0.673, respectively