bluuebunny/crossref_metadata_2025 huggingface dataset:
Contribution of Integrated Urban Agriculture to Household Income: A Case of Kinondoni Municipality, Tanzania
Authors: Victor Kelvin , Massawe Fatihiya A , Sikira Anna
Published: September 2018

Purpose: Urban farmers make efficient utilization of resources by integrating crop-livestock-fish subsectors to maximize accrued benefits. However, types of integration practices adopted and associated income outcome are still undocumented. Thus this study specifically assessed the types of integrated urban agriculture (IUA) mostly practiced by farmers, types of livelihood strategies adopted simultaneously with IUA and its contribution to total household income. Research Method: The study adopted cross-sectional research design. Both quantitative and qualitative data were collected from 132 respondents. Descriptive statistics employed to assess livelihood strategies prioritised by integrated urban farmers. Multiple linear regression technique was adopted to model factors influencing IUA annual income. Findings: The results show that integrated urban agriculture is the most prioritised livelihood strategy to urban farmers. Majority of urban farmers (98.5%) practising integrated farming concentrated on crop-livestock, while fish-livestock and crop livestock-fish integration adopted only by 0.75% each. This implies that, majority of urban farmers did not practice fish sub-sector. Integrated urban agriculture gives average annual income of 2 295 288 TSH (equivalent to 1093$); which was sufficient for household of six members being above monetary food poverty line per year. Research Limitation: This study focuses on income livelihood indicator; however, farmers can also, benefit from integrated agriculture through food security, social inclusion and ecology to mention some.Originality/Value: This study provides an comprehensive layout adopted by integrated urban farmers to benefit from the sub-sectors and what is missing for improvement of the enterprises for improved livelihood outcome in Tanzania
Morphology of testes, spermatogenesis, sperm bundles, and spermatozoa ofKerria chinensis(Hemiptera: Kerriidae)
Authors: Seubparu Lucksanaveejit , Nipitwathanaphon Mingkwan , Wisoram Wijit , Merritt David , Ngernsiri Lertluk
Published: September 2018

The filamentous spermatozoa of scale insects (Hemiptera) are highly modified compared with those of typical insects. Here, we investigate the morphology of the testes, sperm bundles, spermatozoa, and spermatogenesis of the winglessKerria chinensis(Mahdihassan) (Hemiptera: Kerriidae), a shellac-producing scale insect. Each testis contains two antiparallel groups of several hundred syncytial sperm bundles. In each spermatocyte cyst, 16 primary spermatocytes divide via inverted meiosis, resulting in 16 quadrinucleated spermatids, each having two euchromatic and two heterochromatic nuclei. During spermiogenesis, each spermatid produces two spermatozoa protruding out of the spermatid close to the two euchromatic nuclei and their tails then grow in opposite directions. In each cyst, the 32 spermatozoa form two sperm bundles lying in an antiparallel direction oriented to different ends of the testis. Each spermatozoon has three distinct regions, an apex, a filamentous region and a tail. The spermatozoa have long thread-like nuclear cores that occupy about one-fourth of the sperm body length, located primarily in the posterior half. At the anterior end of the spermatozoon is a translucent, swollen vesicle and a distal, densely-stained structure; a putative acrosome of a type not previously reported in the spermatozoa of scale insects.
Psychological capital as a moderator in the relationship between occupational stress and organisational citizenship behaviour among Nigerian graduate employees
Authors: Aderibigbe John K. , Mjoli Themba Q.
Published: September 2018

Orientation: It is a documented fact that occupational stress is widespread worldwide. Moreover, there are clear signs of many variables that affect Nigerian graduate employees, which are most likely to cause severe occupational stress, and this, in turn, could negatively affect employees and their organisational ability to demonstrate citizenship behaviour.Research purpose: The purpose of this study was twofold: firstly, to examine the nature of relationship between occupational stress and organisational citizenship behaviour, and, secondly, to investigate whether psychological capital significantly moderates the relationship between occupational stress and organisational citizenship behaviour.Motivation for the study: The study was conducted to demonstrate whether the presence of psychological capital could result in a better level of employee performance, even as employees experience a certain level of occupational stress. In view of the above, the study might have contributed to form a new model of psychological intervention for occupational stress and organisational citizenship behaviour.Research approach/design and method: The study adopted the positivist explanatory cross-sectional (survey) research design to systematically sample opinions of 1532 male and female graduate employees across various sectors of the Nigerian economy, using a structured and validated questionnaire and the Statistical Package for the Social Sciences (SPSS) software.Main findings: The results showed that there was a weak positive relationship between occupational stress and organisational citizenship behaviour. Psychological capital significantly moderated the relationship between occupational stress and organisational citizenship behaviour.Practical/managerial implications: Human resource managers should develop psychological capital in employees in order to increase the level of organisational performance and reduce the negative impact of occupational stress.Contribution/value-add: Recommendations of the study could assist in training and developing effective workforce capacity towards improving the economy of the nation.
Iron deficiency in children and adolescents with attention deficit hyperactivity disorder: Does the relationship exist?
Authors: Magula Luzuko , Moxley Karis , Lachman Anusha
Published: September 2018

Background: Iron is a cofactor in the production and breakdown of neurotransmitters like dopamine and is vital for normal brain function. Iron deficiency potentially contributes to the development of attention deficit hyperactivity disorder (ADHD) because dopamine imbalances can cause hyperactivity, restlessness and problems with concentration and attention. However, a direct association between iron deficiency and ADHD remains to be determined.Objectives: The aim of this study was to investigate the possible correlation between iron deficiency and ADHD in children and adolescents seen at the child psychiatry outpatient service at Tygerberg Hospital, South Africa.Methods: A retrospective chart review was conducted to gather data of all outpatient children and adolescents who had their serum ferritin and/or iron levels tested between February 2011 and January 2016. Relevant demographic and clinical information was extracted from 255 records, and statistical methods were used to test for correlations between ADHD and certain variables, including iron deficiency.Results: Out of 255 patients, 88 (34.5%) had iron deficiency, 157 (61.6%) had ADHD and 54 (21.0%) had both iron deficiency and ADHD. Of those patients with ADHD, 11 (7.0%) had other psychiatric comorbidities, and more males (89.0%) had this dual diagnosis compared to females (11.0%). Variables found to be significantly associated with ADHD included gender, age, Ritalin treatment and psychiatric comorbidities, but there was no significant association between ADHD and iron deficiency (p = 0.150).Conclusion: There was no relationship between ADHD and iron deficiency in this cohort of children and adolescents. Further studies using a treatment-naïve sample are required.
Fracture resistance of the permanent restorations for endodontically treated premolars
Authors: Lin Galvin Sim Siang , Ghani Nik Rozainah Nik Abdul , Noorani Tahir Yusuf , Ismail Noor Huda
Published: September 2018

Aim: This study aims to compare the fracture strength, fracture pattern, types of fracture involved, and areas of fractured restoration among endodontically treated permanent lower premolars restored with different restorative materials. Materials and Methods: Sixty-nine mature human permanent lower premolars recently extracted for orthodontic, periodontal, or other reasons were selected and divided into three groups (n = 23). Groups 1 and 2 were endodontically treated. Standardized mesio-occlusal distal cavities were then prepared in both Groups 1 and 2. Groups 1 and 2 were restored with amalgam using Nayyar’s core technique and glass fiber post with composite resin core, respectively. Group 3 consisted of intact teeth which acted as control group. All teeth were tested under constant occlusal load until fracture occurred using a Universal Testing Machine. Data analysis was carried out using Kruskal–Wallis test complemented by Mann–Whitney test. Results: The mean values of fracture strength were 388.05 N (± 158.09) for Group 1, 588.90 N (± 151.33) for Group 2, and 803.05 N (± 182.23) for Group 3. Kruskal–Wallis test showed significant differences among all three groups in terms of fracture strength. The mean load required to fracture intact teeth in Group 3 was significantly highest, followed by Group 2 (P < 0.01) and finally Group 1 (P < 0.01). Most fractures occurred within the coronal structure and were considered favorable pattern. Besides, majority of the fractures occurred on restorations and particularly at the distal side. Conclusions: Teeth restored with fiber post and composite core resulted in higher fracture resistance than teeth restored with Nayyar’s core amalgam restoration.
Comparison of International Caries Detection and Assessment System and digital radiographs for detecting occlusal dental caries: An In vivo Study
Authors: Bhumireddy Jayachandra Reddy , Challa Ramasubbareddy , Mallineni Sreekanth Kumar , Nuvvula Sivakumar
Published: September 2018

Aim: The aim of the present study was to evaluate the accuracy in the diagnosis of occlusal caries lesion using International Caries Detection and Assessment System II (ICDAS II) and digital radiographs in the primary molars. Materials and Methods: A total of 35 children was selected randomly and recruited for visual ICDAS II and digital bitewing radiographic examination by two independent trained examiners. A total of 249 teeth were examined at D1 (enamel caries lesions) and D3 (dentine caries lesions) thresholds. Results: Sensitivity at D1 for ICDAS II were 95%, and 22.97% for digital radiographs While specificity was 1005 for the both tools. At D3, threshold sensitivity of ICDAS II was 94.%, whereas it was 69% for digital radiographs, while specificity was almost the same for both methods and both D1 and D3 thresholds. Excellent inter- and intra-examiner reliabilities were evident. Conclusions: ICDAS II is better than digital radiographs in detecting carious lesions confined to enamel, and both are equally effective in the detection of dentinal carious lesions.
THE MAASAI OF SOUTHERN KENYA DOMAIN MODEL OF LAND USE
Authors: Karamesouti M. , Schultz C. , Chipofya M. , Jan S. , Murcia Galeano C. E. , Schwering A. , Timm C.
Published: September 2018

Abstract. We present a domain model that formalises the human-land relations in the Maasai nomadic pastoralist society in Kenya, referred to as MSKDM, and its integration with the prominent Land Administration Domain Model (LADM). Our long-term aim is to facilitate a land administration system that can accurately capture and express salient Maasai concepts of land use, ownership, communal tenure, and to assist in transparency during land transactions. We use an extensive corpus of existing research literature, and input from our own on-site workshops, as source material for our domain model. We use real sketch maps drawn by Maasai community members that we collected during our field studies for validation, and to demonstrate how our model can be operationalised.
INTEROPERABLE VISUALIZATION OF 3D CITY MODELS USING OGC’S STANDARD 3D PORTRAYAL SERVICE
Authors: Koukofikis A. , Coors V. , Gutbell R.
Published: September 2018

Abstract. The demand of serving large 3D spatial data, mainly of urban areas, reflects the need of hierarchical data structures for 3D data. During the last years the OGC community standard I3S (Indexed 3d Scene Layer, ESRI) and 3D Tiles (Analytical Graphics, Inc.) emerged in order to deal with this challenge. Conceptually, hierarchical structures for 3D data operate similarly to web map tiles, differing only in the implementation. Although 3D hierarchical formats can transmit arbitrary sized geospatial data, they are not interoperable with consuming/visualization technologies on the client. A series of prototype implementations focus on rendering of hierarchical organized massive 3D data in various web client technologies employing the 3D Portrayal Service. As a result, the user can query a scene via the 3D Portrayal Service by specifying a spatial region, rather than a specific resource via a URI. The result is delivered either using I3S or 3D Tiles as a data delivery format, depending on which data is available for the specified region. The client APIs are capable of rendering either the I3S or the 3D Tiles content.
EFFICIENT AND ACCURATE FUSION OF MASSIVE VECTOR DATA ON 3D TERRAIN
Authors: Liu Z. , Li C. , Zhao Z. , Zhang D. , Wang F. , Wang Y.
Published: September 2018

Abstract. This paper presents a viewpoint-related fusion method of massive vector data and 3D terrain, in order to superpose the massive 2D vector data onto the undulating multi-resolution 3D terrain precisely and efficiently. First, the method establishes an adaptive hierarchical grid spatial index for vector data. It will determine the geographic spatial relationship between vector data and the tiles of 3D terrain in the visible area; secondly, this method will use the improved sub-pixel graphics engine AggExt to generate textures for vector data that has been bound to terrain tiles in real time; Finally, considering that a large amount of vector data will generate a lot of 2D textures in the computer memory, the method should release the “expired” vector textures. In this paper, in order to take into account the real-time convergence and the smooth interactivity of 3D scenes, this method will adopt a multi-threading strategy. The experimental results show that this method can realize the real-time and seamless fusion of massive vector objects on the 3D terrain, and has a high rendering frame rate. It can also reduce the aliasing produced by traditional texture-based methods and improve the quality of vector data fusion.
TOWARDS AN INTELLIGENT PLATFORM FOR BIG 3D GEOSPATIAL DATA MANAGEMENT
Authors: Mazroob Semnani N. , Kuper P. V. , Breunig M. , Al-Doori M.
Published: September 2018

Abstract. The use of intelligent technologies within 3D geospatial data analysis and management will decidedly open the door towards efficiency, cost transparency, and on-time schedules in planning processes. Furthermore, the mission of smart cities as a future option of urban development can lead to an environment that provides high-quality life along stable structures. However, neither geospatial information systems nor building information modelling systems seem to be well prepared for this new development. After a review of current approaches and a discussion of their limitations we present our approach on the way to an intelligent platform for the management and analysis of big 3D geospatial data focusing on infrastructure projects such as metro or railway tracks planning. three challenges are presented focusing on the management of big geospatial data with existing geo-database management systems, the integration of heterogeneous data, and the 3D visualization for database query formulation and query results. The approach for the development of a platform for big geospatial data analysis is discussed. Finally, we give an outlook on our future research supporting intelligent 3D city applications in the United Arab Emirates.
GEOMETRICAL NETWORK MODEL GENERATION USING POINT CLOUD DATA FOR INDOOR NAVIGATION
Authors: Nakagawa M. , Nozaki R.
Published: September 2018

Abstract. Three-dimensional indoor navigation requires various functions, such as the shortest path retrieval, obstacle avoidance, and secure path retrieval, for optimal path finding using a geometrical network model. Although the geometrical network model can be prepared manually, the model should be automatically generated using images and point clouds to represent changing indoor environments. Thus, we propose a methodology for generating a geometrical network model for indoor navigation using point clouds through object classification, navigable area estimation, and navigable path estimation. Our proposed methodology was evaluated through experiments using the benchmark of the International Society for Photogrammetry and Remote Sensing for indoor modeling. In our experiments, we confirmed that our methodology can generate a geometrical network model automatically.
A DEEP LEARNING STUDY OF EXTRACTING NAVIGATION AREA FROM CAD BLUEPRINTS
Authors: Niu L. , Song Y. Q. , Su J. , Zhang H. M.
Published: September 2018

Abstract. Deep learning technology is a cutting edge topic of AI region, and draws more attention from photogrammetry and remote sensing society. In this study, we strive to combine deep learning with CAD designs to extract navigation area (room). To this, we mark more than 200 2D building blueprint in CAD forms to construct the learning set to train object detection model based on TensorFlow. This model is the faster R-CNN inception v2 model from COCO dataset. The test and result section is composed of three parts: First part demonstrates the model performance on learning dataset; second part applies the generated model to extract rooms from untrained raw CAD blueprints; Third part covers the comparison between deep learning extracted result and geometric based algorithm extracted result. Test result shows that the deep learning approach could achieve higher accuracy than geometric approach under regular shape situations. In conclusion, we have proposed a well-trained deep learning model that could be utilized to construct a schema of the navigation area for 2D CAD blueprints.
PLAY-BASED HYDROCARBON EXPLORATION UNDER SPATIAL UNCERTAINTY USING EVIDENTIAL THEORY
Authors: Seraj S. , Delavar M. R.
Published: September 2018

Abstract. Hydrocarbon exploration is a process based on the prediction of existing hydrocarbon in the underground formations which is associated with uncertainties. A number of studies have been undertaken on the extent of these uncertainties in the risk maps concerned with hydrocarbon exploration. This paper has addressed this issue using a novel approach. The differences of the proposed method are checked in a few cases. Firstly, the level of studying the hydrocarbon system is play which refers to an area with a potential for trapping hydrocarbon with a unique petroleum system. Second, the evidential theory was used to accurately examine the uncertainty in the maps of the hydrocarbon system. Finally, the model used to produce the final risk map is developed in a geospatial information system environment. The results of the research show that the functions proposed in the model are accurately estimated the uncertainty in the prediction of the existence of hydrocarbon systems in the study area. The CCRS map outlines approximately 25.9 % of the study area which is highly promising for the hydrocarbon potential reservation. According to the obtained results, around 61.2 % of the prospects have low risk of hydrocarbon potential in the area having high belief and about 43.7 % of the prospects are available with high risk of hydrocarbon potential in the regions with high uncertainty.
DYNAMICAL PREDICTION TECHNIQUE FOR GEOSIMULATION USING MULTISPECTRAL REMOTE SENSING DATA
Authors: Villalon-Turrubiates I. E.
Published: September 2018

Abstract. The analysis of dynamical models for prediction and geosimulation using the information extracted from a geographical region processed from the data provided by multispectral remote sensing systems provides useful information for urban planning and resource management. However, several topics of interest on this particular matter are still to be properly studied. Using the remote sensing data that has been extracted from multispectral images from a particular geographic region in discrete time, its dynamic study is performed in both, spatial resolution and time evolution, in order to obtain the dynamical model of the physical variables and the evolutionary information about the data. This provides a background for understanding the future trends in development of the dynamics inherent in the multispectral and high-resolution images. This proposition is performed via an intelligent computational paradigm based on the use of dynamical filtering techniques modified to enhance the quality of reconstruction of the data extracted from multispectral remote sensing images and using high-performance computational techniques to unify the available data scheme with its dynamic analysis and, therefore, provide a behavioral model of the sensed data.
ENHANCED CLASSIFICATION MODEL FOR MULTISPECTRAL OBSERVATIONS FROM THE EARTH
Authors: Villalon-Turrubiates I. E. , Llovera-Torres M. J.
Published: September 2018

Abstract. The image classification procedure to identify remote sensing signatures from a particular geographical region can be performed with an identification model that has the ability to use large datasets to reach an accurate result. This novel methodology is referred to as the Statistical Enhanced Classification algorithm, which has been developed to employ multispectral images based in the statistical supervised learning theory and can be used for applications in environmental monitoring and analysis. This paper presents the performance study of the proposed methodology using both, multispectral synthetic images and multispectral remote sensing images. The obtained results are accurate due to the use of several spectral bands, the use of statistics such as mean and standard deviation for the training classes and for the pixel neighborhood, which provides more robust information, and the decision-making rule that has the ability to decide if the pixel is not belonging to a predefined class, which leads to an accurate decision model.
TRAVEL TIME ESTIMATION USING SPATIO-TEMPORAL INDEX BASED ON CASSANDRA
Authors: Wu Z. , Li C. , Wu Y. , Xiao F. , Zhu L. , Shen J.
Published: September 2018

Abstract. Travel time estimation plays an important role in traffic monitoring and route planning. Taxicabs equipped with Global Positioning System (GPS) devices have been frequently used to monitor the traffic state, and GPS trajectories of taxicabs also used to estimate path travel time in an urban area. However, in most cases, it is difficult to find a trajectory that fits perfectly with the query path, as some road segments may be traveled by no taxicab in present time slot. This makes it hard to estimate the travel time of the query path. This paper proposes a framework to estimate the travel time of a path by using the GPS trajectories of taxicabs as well as map data sources. In this framework, the travel time is represented as a series of residence time in cells (one cell is the gird segmentation unit), thus the key issues of the estimation are: finding the local traffic patterns of frequently shared paths from historical data and computing the stay time in cells. There are three major processes in this framework: trajectories preprocessing, establishing the temporal-spatial index and cell-based travel time estimation. Based on the temporal-spatial index, an algorithm is developed that uses similar route patterns, the cell-based travel time over a period of history and road network information to estimate the travel time of a path. This paper uses GPS trajectories of 10,357 taxicabs over a period of one week to evaluate the framework. The results demonstrate that this paper’s method is effective and feasible in city-wide scenarios.
AN EXTRACTION APPROACH OF THE TOP-BOUNDED SPACE FORMED BY BUILDINGS FOR PEDESTRIAN NAVIGATION
Authors: Yan J. , Diakité A. A. , Zlatanova S.
Published: September 2018

Abstract. The navigation of pedestrians can be regarded as their movements from one unoccupied space to another unoccupied and connected space. These movements generally occur in three types of environments: indoor, outdoor, and semi-bounded (top-bounded, and/or side-bounded) spaces. While the two former types of spaces are subject to most of the attention, the latter (semi-bounded) also presents a valuable impact on the navigation behaviour. For example, top-bounded environments (e.g. roofs, shelters, etc.) are very popular for pedestrian navigation since a top structure can offer protection from harsh weather, rain, or strong sun. However, such semibounded spaces are completely missing in current navigation models and systems. This is partly explained by the fact that modelling the space, which is by defining a three-dimensional boundless and extensible component (mainly out of the indoor environment), is a very challenging task. In this paper, we propose a structure-based approach for top-bounded space extraction in the built environment, relying on 3D models. Thanks to the rapid expansion and availability of 3D city models, our approach can help to account for such type of spaces in 3D pedestrian navigation systems.
ROOM SHAPES AND FUNCTIONAL USES PREDICTED FROM SPARSE DATA
Authors: Dehbi Y. , Gojayeva N. , Pickert A. , Haunert J.-H. , Plümer L.
Published: September 2018

Abstract. Many researchers used expensive 3D laser scanning techniques to derive indoor models. Few papers describe the derivation of indoor models based on sparse data such as footprints. They assume that floorplans and rooms are rather rectangular and that information on functional use is given. This paper addresses the automatic learning of a classifier which predicts the functional use of housing rooms. The classification is based on features which are widely available such as room areas and orientation. These features are extracted from an extensive database of annotated rooms. A Bayesian classifier is applied which delivers probabilities of competing class hypotheses. In a second step, functional uses are used to predict the shape of the rooms in a further classification.
IMPLEMENTING AUGMENTED REALITY SANDBOX IN GEODESIGN: A FUTURE
Authors: Afrooz A. , Ballal H. , Pettit C.
Published: September 2018

Abstract. Geodesign method and tools are extensively used for collaborative decision making focused on different fields such as transportation, land use, and landscape and has been applied in various places around the world. Nowadays, Augmented Reality (AR), Virtual Reality (VR) and more recently AR sandbox are increasingly becoming very popular particularly as a pedagogical tool. This research aims to investigate whether an AR sandbox could enhance the understanding of people around the development of design proposals and their impacts. We explored if AR sandbox could be implemented in a collaborative geodesign workflow. We reported an experiment where people were asked to build new trails using the sandbox and how the trails they designed were integrated with a larger design. Results explore opportunities and limitations of implementing AR sandbox in a collaborative geodesign workflow based on the experiment in this paper. Our AR sandbox experiment revealed a wide range of benefits to participants in the trail planning and to the geodesign structure.
A COMPUTATIONALLY CHEAP TRICK TO DETERMINE SHADOW IN A VOXEL MODEL
Authors: Gorte B. G. H. , Zhou K. , van der Sande C. J. , Valk C.
Published: September 2018

Abstract. Representation of scenes on the Earth surface by using voxels is gaining attention because of its suitability for integrating heterogeneous data sources in simulations and quantitative models. Computation of shadows in such models is needed, for example, to obtain crop suitability of agricultural fields in the presence of trees and buildings, or to analyze urban heat island causes and effects. We present an efficient algorithm to compute which of the voxels in a dataset receive direct sunlight, given the solar azimuth and elevation angles. The algorithm can work with multiple (sparse and dense) voxel storage strategies.
A SEMANTIC GRAPH DATABASE FOR BIM-GIS INTEGRATED INFORMATION MODEL FOR AN INTELLIGENT URBAN MOBILITY WEB APPLICATION
Authors: Hor A.-H. , Sohn G. , Claudio P. , Jadidi M. , Afnan A.
Published: September 2018

Abstract. Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology.
HIGH-LEVEL-OF-DETAIL SEMANTIC 3D GIS FOR RISK AND DAMAGE REPRESENTATION OF ARCHITECTURAL HERITAGE
Authors: Colucci E. , Noardo F. , Matrone F. , Spanò A. , Lingua A.
Published: September 2018

Abstract. The need to share information about architectural heritage effectively after a disaster event, in order to foster its preservation, requires the use of a common language between the involved actors and stakeholders. A database able to connect the architectural heritage representation with the data useful for hazard and risk analysis can thus be a powerful instrument. This paper outlines a methodology to represent 3D models of the architectural heritage, according to some existing standards data models, and relate their geometric features to the damage mechanisms that could occur after an earthquake. Among all the existing standard to represent cartographic, cultural heritage and hazard/risk information, respectively INSPIRE, CityGML, UNESCO, CIDOC-CRM, its extension MONDIS and the Getty Institute vocabularies, compliant to the CIDOC-CRM, have been taken into account. An INSPIRE extension has been proposed for increasing the level of detail (LoD) of the representation and improving the description of heritage buildings, adding some macro-elements and elements “feature types” connected with the damage mechanisms, identified in structural studies. The suggested method allows to archive, in a multi-scale database, 3D information with a very high level of detail about architectural heritage and can help structural engineers and conservator-restorers in preventing further damages through individuating useful targeted actions.
USING THE COMBINED LADM-INDOORGML MODEL TO SUPPORT BUIILDING EVACUATION
Authors: Alattas A. , van Oosterom P. , Zlatanova S. , Hoeneveld D. , Verbree E.
Published: September 2018

Abstract. During an incident, many people that are located in indoor environments require to follow emergency evacuation procedures. The ‘emergency evacuation’ term has been defined as ‘a critical movement of people from a dangerous area due to the risk or an incident of a tragic event’ (Bonabeau, 2002). An emergency evacuation could be needed in a life or death situation, regardless if it begins with a natural non-intended incident or a terrorist attack. Many researchers have studied the behaviour of the people during the evacuation because of several incidents with panic attacks that have led to injuries including death of people being crushed or trampled down by others. In crisis situation, the perception of the indoor environment, which differs from person to person, play a critical role in the evacuation. Also, the access rights of the indoor spaces are different from those rights (and restrictions) during normal times. They may positively impact the movements of the people during the evacuation by providing suggestions for shorter/better route. This paper addresses the impact of the access rights of the indoor spaces during an emergency evacuation. We employ the conceptual model of LADM-IndoorGML that defines the accessibility of the indoor spaces based on the rights, restrictions, and responsibilities of the user of the indoor space. The access rights of the indoor spaces are affected by the crisis event and this needs to be modelled explicitly (and before crisis situation). Actually, the rights/restrictions persons have on spaces is time dependent: normal operation hours, outside normal operation hours (e.g. during night time in case of a University building) or during crisis times. These actual/valid rights and restrictions affect the movement/accessibility of the users to reach the nearest emergency exits or the safe zone. For this reason, different scenarios have to be developed to study the impact of the accessibilities for different types of users. In this paper we will present the 3D model of an educational building that was built for the purpose of evacuation study. The 3D model is supported by real data for all spaces from the facility management department such as information on departments, sections, groups of users (visitors, employees, and students), and public/private spaces, etc. and a real evacuation exercise. We consider it extremely important to develop our information model based on international standards (LADM/ISO 19152, OGC IndoorGML, ISO 19141, ISO 19107) as we expect that this information will be part of the future ‘building infrastructure’ and applications all over the world can understand and use this data when entering or leaving a certain building both during normal and crisis situations. Different types of applications are anticipated to be based on this information model; e.g. mobile indoor routing app (for normal building users and Emergency Response Team members), crisis evacuation desktop application for command centre, etc.
EXPLORATION ON QUANTIFYING CARBON DIOXIDE (CO2) EMISSION FROM ROAD TRAFFIC IN MEGACITY
Authors: Cong R. , Saito M. , Hirata R. , Ito A. , Maksyutov S.
Published: September 2018

Abstract. As the increasing concern for climate change, quantification on greenhouse gas (GHG) emissions in urban scale has been a key component for local climate actions. To explore an approach on estimating the GHG emissions closely to the real-world condition, in this paper, we make efforts on counting the carbon dioxide (CO2) emissions from road traffic in Tokyo. The road traffic emissions mapping is achieved by linking spatial road line data with detailed activity data (traffic census). Through the data processing by Geographic Information System technique, the emissions of each road segment are estimated basing on the daily average traffic amount and speed of vehicles on each road segment. As our estimation, the CO2 emissions from road traffic of Tokyo in 2015 are about 16,323 Gg. The highlight points mainly refer to linking the traffic census information for the observed road segments on map and allocation efforts for unserved ones. As the limited amount of observation points in traffic census, accurate estimation for unobserved road segments is a challenge. Our approach overcomes it by assumption of a familiar traffic condition for all road segments in the same sub-city area. This approach could simulate the traffic patterns closely to the real traffic condition so that it will more effectively support the emission mitigation policies on road traffic for local climate.
IDENTIFICATION OF LOW ACCURACY REGIONS IN LAND COVER MAPS USING UNCERTAINTY MEASURES AND CLASSIFICATION CONFIDENCE
Authors: Fonte C. C. , Gonçalves L. M. S.
Published: September 2018

Abstract. The aim of this article is to assess if the data provided by soft classifiers and uncertainty measures can be used to identify regions with different levels of accuracy in a classified image. To this aim a soft Bayesian classifier was used, which enables the assignment of classifications confidence levels to all pixels. Two uncertainty measures were also used, namely the Relative Maximum Deviation (RMD) uncertainty measure and the Normalized Entropy (NE). The approach was tested on a case study. A multispectral IKONOS image was classified and the classification uncertainty and confidence where computed and analysed. Regions with different levels of uncertainty and confidence were identified. Reference datasets were then used to assess the classification accuracy of the whole study area and also in the regions with different levels of uncertainty and confidence. A comparative analysis was made on the variation of accuracy and classification uncertainty and confidence along the map and per class. The results show that for the regions with more uncertainty or less confidence the spatially constrained confusion matrices always generate lower values of global accuracy than for global accuracy of the regions with less uncertainty or more confidence. The analysis of the user’s and producer’s accuracy also shows the same general tendency. Proposals are then made on methodologies to use the information provided by the uncertainty and confidence to identify less reliable regions and also to improve classification results using fully automated approaches.
MULTIGRID CELL SHAPE EVALUATION IN DTM FILTERING OF MOBILE LIDAR POINT CLOUDS
Authors: Gézero L. , Antunes C.
Published: September 2018

Abstract. The georeferenced information acquisition for large scales digital terrain models generation is usually a rather time-consuming and costly process. The use of point clouds gathered by mobile LiDAR systems therefore appears as a possible source for the creation of those models, particularly in remote areas where security issues requires a maximum speed field collection information. However, the filtering challenge to separate the points that represents the terrain surface from the remaining, in an automatic and consistent way, is an open challenge that continues to arouse the interest of researchers. Using a multigrid filtering method, this work presents a comparative analysis of different shapes of regular grid cells, including the implementation algorithms for each cell shape. Also, a coordinate grid concept applied to each cell shape is proposed. Finally, a discussion about the obtained results of the method application using point clouds collected in different environments from different ground LiDAR systems are presented.
INFORMATION CONTENT ANALYSIS FROM VERY HIGH RESOLUTION OPTICAL SPACE IMAGERY FOR UPDATING SPATIAL DATABASE
Authors: Alkan M.
Published: September 2018

Abstract. High resolution satellite images started with IKONOS imagery. After the launch of the very high resolution IKONOS in the 1990s, a new generation of commercial Earth-imaging satellites have pioneered a new era of space imaging for observations of Earth. The IKONOS satellite image has an important place sampling range with 1 m GSD. In the subsequent Quickbird satellite image, the GSD is down to 62 cm and the sensitivity is even higher. Advancements in the geometric resolution of space images have improved the conditions for generations of large-scale topographic maps. With using WorldView-1, WorldView-2, and GeoEye-1, images can now be captured from space with a 0.5 m ground sampling distance (GSD). The Worldview-4 display with the highest technology and resolution is being used in various application areas. WorldView-4 (formerly GeoEye-2), launched in November 2017, provides a second sensor which is capable of delivering imagery at 30 cm resolution, the highest level of detail commercially available from satellite. WorldView-4 greatly expands the 30 cm collection capabilities and archive growth in today’s imagery environment. Geometric accuracy and information content are the most significant components of mapping from space images. By using economical, rapid and periodic acquisition, and corresponding ground resolution, these satellites have established an alternative to aerial photos and have been widely used for various applications such as object extraction, change detection, topographic map production, and development of Geographic Information Systems (GIS). The utility of VHR images is dependent on their geometric accuracy and information content. Related with the study, the generally required production scale of 0.05 to 0.1 mm GSD in the map scale has been confirmed. This corresponds to a topographic map scale of 1 : 10,000 respectively 1 : 5000 for 1 m and 0.5 m GSD images. In this study, images from IKONOS, QuickBird, WorldView-1, Worldview-2 and WorldView-4 have been used for topographic mapping. For this reason, İstanbul and Zonguldak test fields are an important area for applications of the high resolution imageries. The details which can be identified in the space images dominantly depends upon the ground resolution, available as ground sampling distance (GSD). In this study, high resolution imageries have been tested depending on the GSD and corresponding to the map scales for updating GIS database.
TIME-RELATED QUALITY DIMENSIONS OF URBAN REMOTELY SENSED BIG DATA
Authors: Kugler Z. , Szabó G. , Abdulmuttalib H. M. , Batini C. , Shen H. , Barsi A. , Huang G.
Published: September 2018

Abstract. Our rapidly changing world requires new sources of image based information. The quickly changing urban areas, the maintenance and management of smart cities cannot only rely on traditional techniques based on remotely sensed data, but also new and progressive techniques must be involved. Among these technologies the volunteer based solutions are getting higher importance, like crowd-sourced image evaluations, mapping by satellite based positioning techniques or even observations done by unskilled people. Location based intelligence has become an everyday practice of our life. It is quite enough to mention the weather forecast and traffic monitoring applications, where everybody can act as an observer and acquired data – despite their heterogeneity in quality – provide great value. Such value intuitively increases when data are of better quality. In the age of visualization, real-time imaging, big data and crowd-sourced spatial data have revolutionary transformed our general applications. Most important factors of location based decisions are the time-related quality parameters of the used data. In this paper several time-related data quality dimensions and terms are defined. The paper analyses the time sensitive data characteristics of image-based crowd-sourced big data, presents quality challenges and perspectives of the users. The data quality analyses focus not only on the dimensions, but are also extended to quality related elements, metrics. The paper discusses the connection of data acquisition and processing techniques, considering even the big data aspects. The paper contains not only theoretical sections, strong practice-oriented examples on detecting quality problems are also covered. Some illustrative examples are the OpenStreetMap (OSM), where the development of urbanization and the increasing process of involving volunteers can be studied. This framework is continuing the previous activities of the Remote Sensing Data Quality Working Group (ICWGIII/IVb) of the ISPRS in the topic focusing on the temporal variety of our urban environment.
IMPROVED CLASSIFICATION OF SATELLITE IMAGERY USING SPATIAL FEATURE MAPS EXTRACTED FROM SOCIAL MEDIA
Authors: Leichter A. , Wittich D. , Rottensteiner F. , Werner M. , Sester M.
Published: September 2018

Abstract. In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into a system for the classification of satellite images. For that purpose, we present a quite general data fusion framework based on Convolutional Neural Network (CNN) and an initial examination of our approach on features from geo-located social media postings on the Twitter and Sentinel images. For this examination, we selected six simple Twitter features derived from the metadata, which we believe could contain information for the spatial context. We present initial experiments using geotagged Twitter data from Washington DC and Sentinel images showing this area. The goal of classification is to determine local climate zones (LCZ). First, we test whether our selected feature maps are not correlated with the LCZ classification at the geo-tag position. We apply a simple boost tree classifier on this data. The result turns out not to be a mere random classifier. Therefore, this data can be correlated with LCZ. To show the improvement by our method, we compare classification with and without the Twitter feature maps. In our experiments, we apply a standard pixel-based CNN classification of the Sentinel data and use it as a baseline model. After that, we expand the input augmenting additional Twitter feature maps within the CNN and assess the contribution of these additional features to the overall F1-score of the classification, which we determine from spatial cross-validation.
DESIGN AND IMPLEMENTATION OF DYNAMIC UPDATE SYSTEM FOR GEOGRAPHICAL NAMES AND ADDRESSES
Authors: Liu Y. , Sun W. , Li C. , Liu X. , Fang C.
Published: September 2018

Abstract. In order to solve the problem of the long update cycle of geographical names and addresses, this paper puts forward a dynamic update technique of mobile & PC integration. The combination of related services of NewMap software and mobile APP can effectively improve the speed and accuracy of updating geographical names and addresses. Taking the mobile & PC integration model as a starting point and comprehensively using GIS-related technologies, mobile positioning technologies, offline map technologies, etc., the dynamic update of geographical names and addresses has been successfully implemented and has been successfully applied to the dynamic update of geographical names.