Nurul Hawani Idris

A conceptual model of the automated credibility assessment of the volunteered geographic information

The use of Volunteered Geographic Information (VGI) in collecting, sharing and disseminating geospatially referenced information on the Web is increasingly common. The potentials of this localized and collective information have been seen to complement the maintenance process of authoritative mapping data sources and in realizing the development of Digital Earth. The main barrier to the use of this data in supporting this bottom up approach is the credibility (trust), completeness, accuracy, and quality of both the data input and outputs generated. The only feasible approach to assess these data is by relying on an automated process. This paper describes a conceptual model of indicators (parameters) and practical approaches to automated assess the credibility of information contributed through the VGI including map mashups, Geo Web and crowd – sourced based applications. There are two main components proposed to be assessed in the conceptual model – metadata and data. The metadata component comprises the indicator of the hosting (websites) and the sources of data / information. The data component comprises the indicators to assess absolute and relative data positioning, attribute, thematic, temporal and geometric correctness and consistency. This paper suggests approaches to assess the components. To assess the metadata component, automated text categorization using supervised machine learning is proposed. To assess the correctness and consistency in the data component, we suggest a matching validation approach using the current emerging technologies from Linked Data infrastructures and using third party reviews validation. This study contributes to the research domain that focuses on the credibility, trust and quality issues of data contributed by web citizen providers.

Engaging indigenous people as geo-crowdsourcing sensors for ecotourism mapping via mobile data collection: a case study of the Royal Belum State Park

ABSTRACT Web 2.0 and the proliferation of built-in Global Positioning System (GPS) on smartphones have influenced the increase of geo-crowdsourcing activities in a number of different contexts. The aim of this paper is to evaluate the performance of indigenous people’s use of mobile collection applications that are embedded in a smartphone to facilitate ecotourism asset mapping. In order to achieve this, field usability testing was conducted where structured observational method was used to assess the performance. The findings indicate majority of them can complete the data entry tasks using mobile data collection. The performance of data entries using radio button, icons, camera and audio methods were identified as better than free text and drop-down list methods. There was a correlation between the level of education with the ability of using radio button, drop-down list and image icon as data entry methods. The paper also discusses the extent of local knowledge relating to ecotourism within the community. The findings should be useful in the understanding of the design of mobile geo-crowdsourcing tools for use within other contexts that focus on data collection by semiliterate and indigenous groups.

CAWRES: A Waveform Retracking Fuzzy Expert System for Optimizing Coastal Sea Levels from Jason-1 and Jason-2 Satellite Altimetry Data

This paper presents the Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimise the Jason satellite altimetric sea levels from multiple retracking solutions. CAWRES’ aim is to achieve the highest possible accuracy of coastal sea levels, thus bringing measurement of radar altimetry data closer to the coast. The principles of CAWRES are twofold. The first is to reprocess altimeter waveforms using the optimal retracker, which is sought based on the analysis from a fuzzy expert system. The second is to minimise the relative offset in the retrieved sea levels caused by switching from one retracker to another using a neural network. The innovative system is validated against geoid height and tide gauges in the Great Barrier Reef, Australia for Jason-1 and Jason-2 satellite missions. The regional investigations have demonstrated that the CAWRES can effectively enhance the quality of 20 Hz sea level data and recover up to 16% more data than the standard MLE4 retracker over the tested region. Comparison against tide gauge indicates that the CAWRES sea levels are more reliable than those of Sensor Geophysical Data Records (SGDR) products, because the former has a higher (≥0.77) temporal correlation and smaller (≤19 cm) root mean square errors. The results demonstrate that the CAWRES can be applied to coastal regions elsewhere as well as other satellite altimeter missions.

Comparison of retracked coastal altimetry sea levels against high frequency radar on the continental shelf of the Great Barrier Reef, Australia

Abstract. Comparison of Jason-1 altimetry retracked sea levels and high frequency (HF) radar velocity is examined within the region of the Great Barrier Reef, Australia. The comparison between both datasets is not direct because the altimetry derives only the geostrophic component, while the HF radar velocity includes information on both geostrophic and ageostrophic components, such as tides and winds. The comparison of altimetry and HF radar data is performed based on the parameter of surface velocity inferred from both datasets. The results show that 48% (10 out of 21 cases) of data have high (≥0.5) spatial correlation. The mean of spatial correlation for all 21 cases is 0.43. This value is within the range (0.42 to 0.5) observed by other studies. Low correlation is observed due to disagreement in the trend of velocity signals in which sometimes they have contradictions in the signal direction and the position of the peak is shifted. In terms of standard deviation of difference and root mean square error, both datasets show reasonable agreement with ≤2.5  cm s−1.

Validation of Sea levels from coastal altimetry waveform retracking expert system: a case study around the Prince William Sound in Alaska

This paper presents the validation of Coastal Altimetry Waveform Retracking Expert System (CAWRES), a novel method to optimize the Jason satellite altimetric sea levels from multiple retracking solutions. The validation is conducted over the region of Prince William Sound in Alaska, USA, where altimetric waveforms are perturbed by emerged land and sea states. Validation is performed in twofold. First, comparison with existing retrackers (i.e. MLE4 and Ice) from the Sensor Geophysical Data Records (SGDR), and second, comparison with in-situ tide gauge data. From the first validation assessment, in general, CAWRES outperforms the MLE4 and Ice retrackers. In 4 out of 6 cases, the value of improvement percentage (standard deviation of difference) is higher (lower) than those of the SGDR retrackers. CAWRES also presents the best performance in producing valid observations, and has the lowest noise when compared to the SGDR retrackers. From the second assessment with tide gauge, CAWRES retracked sea level anomalies (SLAs) are consistent with those of the tide gauge. The accuracy of CAWRES retracked SLAs is slightly better than those of the MLE4. However, the performance of Ice retracker is better than those of CAWRES and MLE4, suggesting the empirical-based retracker is more effective. The results demonstrate that the CAWRES would have potential to be applied to coastal regions elsewhere.

Comparisons of skill index algorithm in driving for human adaptive mechatronics

Currently, Human Machine System (HMS) considers being a proven technology, which has gained an important role in various human activities. One of the most recent developments in this area is Human Adaptive Mechatronics (HAM) approach for enhancing human skills. This approach therefore is different compared to an ordinary HMS, in terms of its ability to adapt to the changes in its environment and in the human changing level of skills. The crucial issue in HAM is in evaluating the human skills level on machine operation. Therefore, this paper discussed about the comparisons between the proposed formula to quantify and classify the skill index of human operator with the Fuzzy Logic and other formula. An experiment using a driving car simulator is carried out to clarify those formulas in machine manipulation system. From the comparison it is found that the proposed formulas give better result compared to Fuzzy Logic System and other formula.