bluuebunny/crossref_metadata_2025 huggingface dataset:
UNMANNED AERIAL VEHICLE USE FOR WOOD CHIPS PILE VOLUME ESTIMATION
Authors: Mokroš M. , Tabačák M. , Lieskovský M. , Fabrika M.
Published: August 2016

The rapid development of unmanned aerial vehicles is a challenge for applied research. Many technologies are developed and then researcher are looking up for their application in different sectors. Therefore, we decided to verify the use of the unmanned aerial vehicle for wood chips pile monitoring.

 We compared the use of GNSS device and unmanned aerial vehicle for volume estimation of four wood chips piles. We used DJI Phantom 3 Professional with the built-in camera and GNSS device (geoexplorer 6000). We used Agisoft photoscan for processing photos and ArcGIS for processing points.

 Volumes calculated from pictures were not statistically significantly different from amounts calculated from GNSS data and high correlation between them was found (p = 0.9993). We conclude that the use of unmanned aerial vehicle instead of the GNSS device does not lead to significantly different results. Tthe data collection consumed from almost 12 to 20 times less time with the use of UAV. Additionally, UAV provides documentation trough orthomosaic.
BUDGET UAV SYSTEMS FOR THE PROSPECTION OF SMALL- AND MEDIUM-SCALE ARCHAEOLOGICAL SITES
Authors: Ostrowski W. , Hanus K.
Published: August 2016

One of the popular uses of UAVs in photogrammetry is providing an archaeological documentation. A wide offer of low-cost (consumer) grade UAVs, as well as the popularity of user-friendly photogrammetric software allowing obtaining satisfying results, contribute to facilitating the process of preparing documentation for small archaeological sites. However, using solutions of this kind is much more problematic for larger areas. The limited possibilities of autonomous flight makes it significantly harder to obtain data for areas too large to be covered during a single mission. Moreover, sometimes the platforms used are not equipped with telemetry systems, which makes navigating and guaranteeing a similar quality of data during separate flights difficult. The simplest solution is using a better UAV, however the cost of devices of such type often exceeds the financial capabilities of archaeological expeditions.

 The aim of this article is to present methodology allowing obtaining data for medium scale areas using only a basic UAV. The proposed methodology assumes using a simple multirotor, not equipped with any flight planning system or telemetry. Navigating of the platform is based solely on live-view images sent from the camera attached to the UAV. The presented survey was carried out using a simple GoPro camera which, from the perspective of photogrammetric use, was not the optimal configuration due to the fish eye geometry of the camera. Another limitation is the actual operational range of UAVs which in the case of cheaper systems, rarely exceeds 1 kilometre and is in fact often much smaller. Therefore the surveyed area must be divided into sub-blocks which correspond to the range of the drone. It is inconvenient since the blocks must overlap, so that they will later be merged during their processing. This increases the length of required flights as well as the computing power necessary to process a greater number of images.

 These issues make prospection highly inconvenient, but not impossible. Our paper presents our experiences through two case studies: surveys conducted in Nepal under the aegis of UNESCO, and works carried out as a part of a Polish archaeological expedition in Cyprus, which both prove that the proposed methodology allows obtaining satisfying results. The article is an important voice in the ongoing debate between commercial and academic archaeologists who discuss the balance between the required standards of conducting archaeological works and economic capabilities of archaeological missions.
COMPARATIVE ANALYSIS OF DATA STRUCTURES FOR STORING MASSIVE TINS IN A DBMS
Authors: Kumar K. , Ledoux H. , Stoter J.
Published: August 2016

Point cloud data are an important source for 3D geoinformation. Modern day 3D data acquisition and processing techniques such as airborne laser scanning and multi-beam echosounding generate billions of 3D points for simply an area of few square kilometers. With the size of the point clouds exceeding the billion mark for even a small area, there is a need for their efficient storage and management. These point clouds are sometimes associated with attributes and constraints as well. Storing billions of 3D points is currently possible which is confirmed by the initial implementations in Oracle Spatial SDO PC and the PostgreSQL Point Cloud extension. But to be able to analyse and extract useful information from point clouds, we need more than just points i.e. we require the surface defined by these points in space. There are different ways to represent surfaces in GIS including grids, TINs, boundary representations, etc. In this study, we investigate the database solutions for the storage and management of massive TINs. The classical (face and edge based) and compact (star based) data structures are discussed at length with reference to their structure, advantages and limitations in handling massive triangulations and are compared with the current solution of PostGIS Simple Feature. The main test dataset is the TIN generated from third national elevation model of the Netherlands (AHN3) with a point density of over 10 points/m2. PostgreSQL/PostGIS DBMS is used for storing the generated TIN. The data structures are tested with the generated TIN models to account for their geometry, topology, storage, indexing, and loading time in a database. Our study is useful in identifying what are the limitations of the existing data structures for storing massive TINs and what is required to optimise these structures for managing massive triangulations in a database.
URBAN RAIN GAUGE SITING SELECTION BASED ON GIS-MULTICRITERIA ANALYSIS
Authors: Fu Yanli , Jing Changfeng , Du Mingyi
Published: August 2016

With the increasingly rapid growth of urbanization and climate change, urban rainfall monitoring as well as urban waterlogging has widely been paid attention. In the light of conventional siting selection methods do not take into consideration of geographic surroundings and spatial-temporal scale for the urban rain gauge site selection, this paper primarily aims at finding the appropriate siting selection rules and methods for rain gauge in urban area. Additionally, for optimization gauge location, a spatial decision support system (DSS) aided by geographical information system (GIS) has been developed. In terms of a series of criteria, the rain gauge optimal site-search problem can be addressed by a multicriteria decision analysis (MCDA). A series of spatial analytical techniques are required for MCDA to identify the prospective sites. With the platform of GIS, using spatial kernel density analysis can reflect the population density; GIS buffer analysis is used to optimize the location with the rain gauge signal transmission character. Experiment results show that the rules and the proposed method are proper for the rain gauge site selection in urban areas, which is significant for the siting selection of urban hydrological facilities and infrastructure, such as water gauge.
TOWARDS ADAPTIVE HIGH-RESOLUTION IMAGES RETRIEVAL SCHEMES
Authors: Kourgli A. , Sebai H. , Bouteldja S. , Oukil Y.
Published: August 2016

Nowadays, content-based image-retrieval techniques constitute powerful tools for archiving and mining of large remote sensing image databases. High spatial resolution images are complex and differ widely in their content, even in the same category. All images are more or less textured and structured. During the last decade, different approaches for the retrieval of this type of images have been proposed. They differ mainly in the type of features extracted. As these features are supposed to efficiently represent the query image, they should be adapted to all kind of images contained in the database. However, if the image to recognize is somewhat or very structured, a shape feature will be somewhat or very effective. While if the image is composed of a single texture, a parameter reflecting the texture of the image will reveal more efficient. This yields to use adaptive schemes. For this purpose, we propose to investigate this idea to adapt the retrieval scheme to image nature. This is achieved by making some preliminary analysis so that indexing stage becomes supervised. First results obtained show that by this way, simple methods can give equal performances to those obtained using complex methods such as the ones based on the creation of bag of visual word using SIFT (Scale Invariant Feature Transform) descriptors and those based on multi scale features extraction using wavelets and steerable pyramids.
ANALYSIS OF THE PIT REMOVAL METHODS IN DIGITAL TERRAIN MODELS OF VARIOUS RESOLUTIONS
Authors: Šamanović S. , Medak D. , Gajski D.
Published: August 2016

Digital terrain model (DTM) is the base for calculation of the surface runoff under the influence of the gravity (gravity flow) in hydrological analysis. It is important to produce hydrologically corrected DTM with the removed natural and artificial depressions to avoid numerical problems in algorithms of the gravity flow. The pit removal procedure changes geomorphometry of the DTM. GIS software packages use pit removal algorithm independently of geomorphmetric features of the analyzed area. In need of minimally modified DTM after the pit removal areas, the carving method (deepen drainage routes) and the filling method (fill sink) were analyzed on three different geomorphometric areas (bare mountain range, hilly wooded area and the plain area intersected with the network of the drainage canals). The recommendation is given for the choice of geomorphometric least changing DTM algorithm. The input data are raster data of elevation points created by stereoscopic photogrammetry method in 5x5 and 25x25 meter resolution. Differences have been noticed during the process of creating raster data. The recommendation is given for the choice of the most acceptable method for each type of area on the basis of comparison of the original elevation points with the elevation points in created DTM.
CROWDSOURCING BIG TRACE DATA FILTERING: A PARTITION-AND-FILTER MODEL
Authors: Yang X. , Tang L.
Published: August 2016

GPS traces collected via crowdsourcing way are low-cost and informative and being as a kind of new big data source for urban geographic information extraction. However, the precision of crowdsourcing traces in urban area is very low because of low-end GPS data devices and urban canyons with tall buildings, thus making it difficult to mine high-precision geographic information such as lane-level road information. In this paper, we propose an efficient partition-and-filter model to filter trajectories, which includes trajectory partitioning and trajectory filtering. For the partition part, the partition with position and angle constrain algorithm is used to partition a trajectory into a set of sub-trajectories based on distance and angle constrains. Then, the trajectory filtering with expected accuracy method is used to filter the sub-trajectories according to the similarity between GPS tracking points and GPS baselines constructed by random sample consensus algorithm. Experimental results demonstrate that the proposed partition-and-filtering model can effectively filter the high quality GPS data from various crowdsourcing trace data sets with the expected accuracy.
ANALYSIS AND VALIDATION OF GRID DEM GENERATION BASED ON GAUSSIAN MARKOV RANDOM FIELD
Authors: Aguilar F. J. , Aguilar M. A. , Blanco J. L. , Nemmaoui A. , García Lorca A. M.
Published: August 2016

Digital Elevation Models (DEMs) are considered as one of the most relevant geospatial data to carry out land-cover and land-use classification. This work deals with the application of a mathematical framework based on a Gaussian Markov Random Field (GMRF) to interpolate grid DEMs from scattered elevation data. The performance of the GMRF interpolation model was tested on a set of LiDAR data (0.87 points/m2) provided by the Spanish Government (PNOA Programme) over a complex working area mainly covered by greenhouses in Almería, Spain. The original LiDAR data was decimated by randomly removing different fractions of the original points (from 10% to up to 99% of points removed). In every case, the remaining points (scattered observed points) were used to obtain a 1 m grid spacing GMRF-interpolated Digital Surface Model (DSM) whose accuracy was assessed by means of the set of previously extracted checkpoints. The GMRF accuracy results were compared with those provided by the widely known Triangulation with Linear Interpolation (TLI). Finally, the GMRF method was applied to a real-world case consisting of filling the LiDAR-derived DSM gaps after manually filtering out non-ground points to obtain a Digital Terrain Model (DTM). Regarding accuracy, both GMRF and TLI produced visually pleasing and similar results in terms of vertical accuracy. As an added bonus, the GMRF mathematical framework makes possible to both retrieve the estimated uncertainty for every interpolated elevation point (the DEM uncertainty) and include break lines or terrain discontinuities between adjacent cells to produce higher quality DTMs.
MODELING URBAN DYNAMICS USING RANDOM FOREST: IMPLEMENTING ROC AND TOC FOR MODEL EVALUATION
Authors: Ahmadlou M. , Delavar M. R. , Shafizadeh-Moghadam H. , Tayyebi A.
Published: August 2016

The importance of spatial accuracy of land use/cover change maps necessitates the use of high performance models. To reach this goal, calibrating machine learning (ML) approaches to model land use/cover conversions have received increasing interest among the scholars. This originates from the strength of these techniques as they powerfully account for the complex relationships underlying urban dynamics. Compared to other ML techniques, random forest has rarely been used for modeling urban growth. This paper, drawing on information from the multi-temporal Landsat satellite images of 1985, 2000 and 2015, calibrates a random forest regression (RFR) model to quantify the variable importance and simulation of urban change spatial patterns. The results and performance of RFR model were evaluated using two complementary tools, relative operating characteristics (ROC) and total operating characteristics (TOC), by overlaying the map of observed change and the modeled suitability map for land use change (error map). The suitability map produced by RFR model showed 82.48% area under curve for the ROC model which indicates a very good performance and highlights its appropriateness for simulating urban growth.
VALIDATION AND UPSCALING OF SOIL MOISTURE SATELLITE PRODUCTS IN ROMANIA
Authors: Sandric I. , Diamandi A. , Oana N. , Saizu D. , Vasile C. , Lucaschi B.
Published: August 2016

The study presents the validation of SMOS soil moisture satellite products for Romania. The validation was performed with in-situ measurements spatially distributed over the country and with in-situ measurements concentrated in on small area. For country level a number of 20 stations from the national meteorological observations network in Romania were selected. These stations have in-situ measurements for soil moisture in the first 5 cm of the soil surface. The stations are more or less distributed in one pixel of SMOS, but it has the advantage that covers almost all the country with a wide range of environmental conditions. Additionally 10 mobile soil moisture measurements stations were acquired and installed. These are spatially concentrated in one SMOS pixel in order to have a more detailed validation against the soil type, soil texture, land surface temperature and vegetation type inside one pixel. The results were compared and analyzed for each day, week, season, soil type, and soil texture and vegetation type. Minimum, maximum, mean and standard deviation were extracted and analyzed for each validation criteria and a hierarchy of those were performed. An upscaling method based on the relations between soil moisture, land surface temperature and vegetation indices was tested and implemented. The study was financed by the Romanian Space Agency within the framework of ASSIMO project http://assimo.meteoromania.ro.
GEOSPATIAL MODELLING APPROACH FOR 3D URBAN DENSIFICATION DEVELOPMENTS
Authors: Koziatek O. , Dragićević S. , Li S.
Published: August 2016

With growing populations, economic pressures, and the need for sustainable practices, many urban regions are rapidly densifying developments in the vertical built dimension with mid- and high-rise buildings. The location of these buildings can be projected based on key factors that are attractive to urban planners, developers, and potential buyers. Current research in this area includes various modelling approaches, such as cellular automata and agent-based modelling, but the results are mostly linked to raster grids as the smallest spatial units that operate in two spatial dimensions. Therefore, the objective of this research is to develop a geospatial model that operates on irregular spatial tessellations to model mid- and high-rise buildings in three spatial dimensions (3D). The proposed model is based on the integration of GIS, fuzzy multi-criteria evaluation (MCE), and 3D GIS-based procedural modelling. Part of the City of Surrey, within the Metro Vancouver Region, Canada, has been used to present the simulations of the generated 3D building objects. The proposed 3D modelling approach was developed using ESRI’s CityEngine software and the Computer Generated Architecture (CGA) language.
AIRBORNE LIGHT DETECTION AND RANGING (LIDAR) DERIVED DEFORMATION FROM THE MW 6.0 24 AUGUST, 2014 SOUTH NAPA EARTHQUAKE ESTIMATED BY TWO AND THREE DIMENSIONAL POINT CLOUD CHANGE DETECTION TECHNIQUES
Authors: Lyda A. W. , Zhang X. , Glennie C. L. , Hudnut K. , Brooks B. A.
Published: August 2016

Remote sensing via LiDAR (Light Detection And Ranging) has proven extremely useful in both Earth science and hazard related studies. Surveys taken before and after an earthquake for example, can provide decimeter-level, 3D near-field estimates of land deformation that offer better spatial coverage of the near field rupture zone than other geodetic methods (e.g., InSAR, GNSS, or alignment array). In this study, we compare and contrast estimates of deformation obtained from different pre and post-event airborne laser scanning (ALS) data sets of the 2014 South Napa Earthquake using two change detection algorithms, Iterative Control Point (ICP) and Particle Image Velocimetry (PIV). The ICP algorithm is a closest point based registration algorithm that can iteratively acquire three dimensional deformations from airborne LiDAR data sets. By employing a newly proposed partition scheme, “moving window,” to handle the large spatial scale point cloud over the earthquake rupture area, the ICP process applies a rigid registration of data sets within an overlapped window to enhance the change detection results of the local, spatially varying surface deformation near-fault. The other algorithm, PIV, is a well-established, two dimensional image co-registration and correlation technique developed in fluid mechanics research and later applied to geotechnical studies. Adapted here for an earthquake with little vertical movement, the 3D point cloud is interpolated into a 2D DTM image and horizontal deformation is determined by assessing the cross-correlation of interrogation areas within the images to find the most likely deformation between two areas. Both the PIV process and the ICP algorithm are further benefited by a presented, novel use of urban geodetic markers. Analogous to the persistent scatterer technique employed with differential radar observations, this new LiDAR application exploits a classified point cloud dataset to assist the change detection algorithms. Ground deformation results and statistics from these techniques are presented and discussed here with supplementary analyses of the differences between techniques and the effects of temporal spacing between LiDAR datasets. Results show that both change detection methods provide consistent near field deformation comparable to field observed offsets. The deformation can vary in quality but estimated standard deviations are always below thirty one centimeters. This variation in quality differentiates the methods and proves that factors such as geodetic markers and temporal spacing play major roles in the outcomes of ALS change detection surveys.
A CONCEPTUAL FRAMEWORK FOR VIRTUAL GEOGRAPHIC ENVIRONMENTS KNOWLEDGE ENGINEERING
Authors: You Lan , Lin Hui
Published: August 2016

VGE geographic knowledge refers to the abstract and repeatable geo-information which is related to the geo-science problem, geographical phenomena and geographical laws supported by VGE. That includes expert experiences, evolution rule, simulation processes and prediction results in VGE. This paper proposes a conceptual framework for VGE knowledge engineering in order to effectively manage and use geographic knowledge in VGE. Our approach relies on previous well established theories on knowledge engineering and VGE. The main contribution of this report is following: (1) The concepts of VGE knowledge and VGE knowledge engineering which are defined clearly; (2) features about VGE knowledge different with common knowledge; (3) geographic knowledge evolution process that help users rapidly acquire knowledge in VGE; and (4) a conceptual framework for VGE knowledge engineering providing the supporting methodologies system for building an intelligent VGE. This conceptual framework systematically describes the related VGE knowledge theories and key technologies. That will promote the rapid transformation from geodata to geographic knowledge, and furtherly reduce the gap between the data explosion and knowledge absence.
THE EFFECTIVENESS OF PANORAMIC MAPS DESIGN: A PRELIMINARY STUDY BASED ON MOBILE EYE-TRACKING
Authors: Balzarini R. , Murat M.
Published: August 2016

This paper presents preliminary results from an ongoing research based on the study of visual attention through mobile eye-tracking techniques. The visual-cognitive approach investigates the reading-comprehension of a particular territorial representation: ski trails maps. The general issue of the study is to provide insights about the effectiveness of panoramic ski maps and more broadly, to suggest innovative efficient representation of the geographic information in mountain. According to some mountain operators, the information provided by paper ski maps no longer meets the needs of a large part of the customers; the question now arises of their adaptation to new digital practices (iPhone, tablets). In a computerized process perspective, this study particularly focuses on the representations, and the inferred information, which are really helpful to the users-skiers to apprehend the territory and make decisions, and which could be effectively replicated into a digital system. The most interesting output relies on the relevance of the panorama view: panorama still fascinates, but contrary to conventional wisdom, the information it provides does not seem to be useful to the skier. From a socio-historical perspective this study shows how empirical evidence-based approach can support the change: our results enhance the discussion on the effectiveness of the message that mountain operators want to convey to the tourist and therefore, on the renewal of (geographical) information in ski resorts.
CHALLENGES OF A MODERN ATLAS OF THE AGEING SOCIETY
Authors: Bleisch S. , Hil D. , Korkut S. , Meyer P.
Published: August 2016

Atlases are collections of illustrated data, often maps, which give an overview - as well as some details - of one or several topic areas. We noted that this description serves well especially for traditional paper and digital atlases. However, in our today's world of entertainment it might give a somewhat dated impression. For the topic area 'Ageing Society' we aim to visualise age related data in an interactive digital way that supports not only the content but also engages the users, offers opportunities for different stakeholders and levels of interest, and is able to accommodate a range of data as well as future updates. A set of guiding principles for the development process addresses these challenges. First implementations show that following the principles is feasible but expensive in terms of time and attention to detail needed. For each selected topic, a story guides the users through the data and highlights interesting aspects. The user can interrupt the story at any time and explore the data further through interacting with the detailed data representations, and switch back to the story when needed. This allows different levels of access which in combination with the specifically designed navigation concept as well as through the adherence to user aware design principles are very promising for the future developments of the Atlas of the Ageing Society and potentially other atlas products.
GROUND SURFACE VISUALIZATION USING RED RELIEF IMAGE MAP FOR A VARIETY OF MAP SCALES
Authors: Chiba T. , Hasi B.
Published: August 2016

There are many methods to express topographical features of ground surface. In which, contour map has been the traditional method and along with development of digital data, surface model such as shaded relief map has been using for ground surface expression. Recently, data acquisition has been developed very much quick, demanding more advanced visualization method to express ground surface so as to effectively use the high quality data. In this study, the authors using the Red Relief Image Map (RRIM, Chiba et al., 2008) to express ground surface visualization for a variety of map scales. The authors used 30 m mesh data of SRTM to show the topographical features of western Mongolian and micro-topographical features of ground surface in tectonically active regions of Japan. The results show that, compared to traditional and other similar methods, the RRIM can express ground surface more precisely and 3-dimensionally, suggested its advanced usage for many fields of topographical visualization.
VISA: AN AUTOMATIC AWARE AND VISUAL AIDS MECHANISM FOR IMPROVING THE CORRECT USE OF GEOSPATIAL DATA
Authors: Hong J. H. , Su Y. T.
Published: August 2016

With the fast growth of internet-based sharing mechanism and OpenGIS technology, users nowadays enjoy the luxury to quickly locate and access a variety of geospatial data for the tasks at hands. While this sharing innovation tremendously expand the possibility of application and reduce the development cost, users nevertheless have to deal with all kinds of “differences” implicitly hidden behind the acquired georesources. We argue the next generation of GIS-based environment, regardless internet-based or not, must have built-in knowledge to automatically and correctly assess the fitness of data use and present the analyzed results to users in an intuitive and meaningful way. The VISA approach proposed in this paper refer to four different types of visual aids that can be respectively used for addressing analyzed results, namely, virtual layer, informative window, symbol transformation and augmented TOC. The VISA-enabled interface works in an automatic-aware fashion, where the standardized metadata serve as the known facts about the selected geospatial resources, algorithms for analyzing the differences of temporality and quality of the geospatial resources were designed and the transformation of analyzed results into visual aids were automatically executed. It successfully presents a new way for bridging the communication gaps between systems and users. GIS has been long seen as a powerful integration tool, but its achievements would be highly restricted if it fails to provide a friendly and correct working platform.
UNDERWATER 3D MODELING: IMAGE ENHANCEMENT AND POINT CLOUD FILTERING
Authors: Sarakinou I. , Papadimitriou K. , Georgoula O. , Patias P.
Published: August 2016

This paper examines the results of image enhancement and point cloud filtering on the visual and geometric quality of 3D models for the representation of underwater features. Specifically it evaluates the combination of effects from the manual editing of images’ radiometry (captured at shallow depths) and the selection of parameters for point cloud definition and mesh building (processed in 3D modeling software). Such datasets, are usually collected by divers, handled by scientists and used for geovisualization purposes. In the presented study, have been created 3D models from three sets of images (seafloor, part of a wreck and a small boat's wreck) captured at three different depths (3.5m, 10m and 14m respectively). Four models have been created from the first dataset (seafloor) in order to evaluate the results from the application of image enhancement techniques and point cloud filtering. The main process for this preliminary study included a) the definition of parameters for the point cloud filtering and the creation of a reference model, b) the radiometric editing of images, followed by the creation of three improved models and c) the assessment of results by comparing the visual and the geometric quality of improved models versus the reference one. Finally, the selected technique is tested on two other data sets in order to examine its appropriateness for different depths (at 10m and 14m) and different objects (part of a wreck and a small boat's wreck) in the context of an ongoing research in the Laboratory of Photogrammetry and Remote Sensing.
SPATIAL-TEMPORAL DETECTION OF CHANGES ON THE SOUTHERN COAST OF THE BALTIC SEA BASED ON MULTITEMPORAL AERIAL PHOTOGRAPHS
Authors: Michalowska K. , Glowienka E. , Pekala A.
Published: August 2016

Digital photogrammetry and remote sensing solutions applied under the project and combined with the geographical information system made it possible to utilize data originating from various sources and dating back to different periods. Research works made use of archival and up-to-date aerial images, satellite images, orthophotomaps. Multitemporal data served for mapping and monitoring intermediate conditions of the Baltic Sea shore zone without a need for a direct interference in the environment. The main objective of research was to determine the dynamics and volume of sea shore changes along the selected part of coast in the period of 1951-2004, and to assess the tendencies of shore development in that area. For each of the six annual data sets, the following were determined: front dune base line, water line and the beach width. The location of the dune base line, which reflects the course of the shoreline in a given year was reconstructed based on stereoscopic study of images from each annual set. Unidirectional changes in the period of 1951-2004 occurred only within 10% of the examined shore section length. The examined shore is marked by a high and considerable dynamics of changes. Almost half of the shore, in particular the middle coast shows big changes, in excess of 2 m/year. The limits of shoreline changes ranged from 120 to -90 m, and their velocity from 0 to 11 m/year, save that the middle and west parts of the examined coast section were subjected to definitely more intense shore transformations. Research based on the analysis of multitemporal aerial images made it possible to reconstruct the intermediate conditions of the Baltic Sea shoreline and determine the volume and rate of changes in the location of dune base line in the examined period of 53 years, and to find out tendencies of shore development and dynamics.
ANALYSIS OF SPATIO-TEMPORAL TRAFFIC PATTERNS BASED ON PEDESTRIAN TRAJECTORIES
Authors: Busch S. , Schindler T. , Klinger T. , Brenner C.
Published: August 2016

For driver assistance and autonomous driving systems, it is essential to predict the behaviour of other traffic participants. Usually, standard filter approaches are used to this end, however, in many cases, these are not sufficient. For example, pedestrians are able to change their speed or direction instantly. Also, there may be not enough observation data to determine the state of an object reliably, e.g. in case of occlusions. In those cases, it is very useful if a prior model exists, which suggests certain outcomes. For example, it is useful to know that pedestrians are usually crossing the road at a certain location and at certain times. This information can then be stored in a map which then can be used as a prior in scene analysis, or in practical terms to reduce the speed of a vehicle in advance in order to minimize critical situations. In this paper, we present an approach to derive such a spatio-temporal map automatically from the observed behaviour of traffic participants in everyday traffic situations. In our experiments, we use one stationary camera to observe a complex junction, where cars, public transportation and pedestrians interact. We concentrate on the pedestrians trajectories to map traffic patterns. In the first step, we extract trajectory segments from the video data. These segments are then clustered in order to derive a spatial model of the scene, in terms of a spatially embedded graph. In the second step, we analyse the temporal patterns of pedestrian movement on this graph. We are able to derive traffic light sequences as well as the timetables of nearby public transportation. To evaluate our approach, we used a 4 hour video sequence. We show that we are able to derive traffic light sequences as well as time tables of nearby public transportation.
A THREE-STEP SPATIAL-TEMPORAL-SEMANTIC CLUSTERING METHOD FOR HUMAN ACTIVITY PATTERN ANALYSIS
Authors: Huang W. , Li S. , Xu S.
Published: August 2016

How people move in cities and what they do in various locations at different times form human activity patterns. Human activity pattern plays a key role in in urban planning, traffic forecasting, public health and safety, emergency response, friend recommendation, and so on. Therefore, scholars from different fields, such as social science, geography, transportation, physics and computer science, have made great efforts in modelling and analysing human activity patterns or human mobility patterns. One of the essential tasks in such studies is to find the locations or places where individuals stay to perform some kind of activities before further activity pattern analysis.

 In the era of Big Data, the emerging of social media along with wearable devices enables human activity data to be collected more easily and efficiently. Furthermore, the dimension of the accessible human activity data has been extended from two to three (space or space-time) to four dimensions (space, time and semantics). More specifically, not only a location and time that people stay and spend are collected, but also what people “say” for in a location at a time can be obtained. The characteristics of these datasets shed new light on the analysis of human mobility, where some of new methodologies should be accordingly developed to handle them.

 Traditional methods such as neural networks, statistics and clustering have been applied to study human activity patterns using geosocial media data. Among them, clustering methods have been widely used to analyse spatiotemporal patterns. However, to our best knowledge, few of clustering algorithms are specifically developed for handling the datasets that contain spatial, temporal and semantic aspects all together. In this work, we propose a three-step human activity clustering method based on space, time and semantics to fill this gap. One-year Twitter data, posted in Toronto, Canada, is used to test the clustering-based method. The results show that the approximate 55% spatiotemporal clusters distributed in different locations can be eventually grouped as the same type of clusters with consideration of semantic aspect.
A method for studying the development pattern of urban commercial service facilities based on customer reviews from social media
Authors: Wang Y. D. , Jiang B. T. , Ye X. Y.
Published: August 2016

Urbanization is one of the most important human social activities in the 21st century (Chaolin et al., 2012). With an increasing number of people visiting cities, the provision of adequate urban service facilities, including public and commercial service facilities, in locations where people live has become an important guarantee of the success of urbanization. Exploring the commercial service facilities in a specific area of a city can help us understand the progress and trends of urban renewal in the area, provide a quantitative basis for evaluating the rationality of planning implementation, and facilitate an analysis of the effects of different factors on the regional development of a city (Schor et al. 2003).

 In this paper, we proposed a data processing and analysis method for studying the distribution and development pattern of urban commercial facilities based on customer reviews. In addition, based on road network constraints, we explored the patterns contained in customer reviews data, including patterns for the spatial distribution and spatial-temporal evolution of facilities as well as the number of facilities and degree of satisfaction.
THE POTENTIAL OF STRAVA DATA TO CONTRIBUTE IN NON-MOTORISED TRANSPORT (NMT) PLANNING IN JOHANNESBURG
Authors: Selala M. K. , Musakwa W.
Published: August 2016

Transportation is one of the most fundamental challenges of urban development in the modern world. Cities are currently moving towards sustainable transportation, which includes non-motorised transportation (NMT). The city of Johannesburg has a goal of becoming a smart city, with sustainable development and smart mobility. Encouraging the use of non-motorised transportation and public transport has the potential of reducing the use of private motorised transport and therefor its negative consequences within the city of Johannesburg. There is limited research on cycling and how cyclists interact with cycling infrastructure within the city. The lack of such knowledge hinders proper planning for NMT. This research studies the ability of geolocation based services to provide such information which can be useful in planning for NMT. Strava Metro application is the example of such geolocation based services. The potential of this app to influence planning is investigated. According to the information acquired by Strava Metro, there are more cyclists in suburbs at the centre of Johannesburg and towards the north and to the east, and in Kibler Park at the south western part of the city. There are indications that the numbers of cycling activities recorded by Strava Metro are affected by the availability of gated communities, income levels, crime levels and the provision of infrastructure.
POSITIONAL ACCURACY ASSESSMENT OF THE OPENSTREETMAP BUILDINGS LAYER THROUGH AUTOMATIC HOMOLOGOUS PAIRS DETECTION: THE METHOD AND A CASE STUDY
Authors: Brovelli M. A. , Minghini M. , Molinari M. E. , Zamboni G.
Published: August 2016

OpenStreetMap (OSM) is currently the largest openly licensed collection of geospatial data. Being OSM increasingly exploited in a variety of applications, research has placed great attention on the assessment of its quality. This work focuses on assessing the quality of OSM buildings. While most of the studies available in literature are limited to the evaluation of OSM building completeness, this work proposes an original approach to assess the positional accuracy of OSM buildings based on comparison with a reference dataset. The comparison relies on a quasi-automated detection of homologous pairs on the two datasets. Based on the homologous pairs found, warping algorithms like e.g. affine transformations and multi-resolution splines can be applied to the OSM buildings to generate a new version having an optimal local match to the reference layer. A quality assessment of the OSM buildings of Milan Municipality (Northern Italy), having an area of about 180 km2, is then presented. After computing some measures of completeness, the algorithm based on homologous points is run using the building layer of the official vector cartography of Milan Municipality as the reference dataset. Approximately 100000 homologous points are found, which show a systematic translation of about 0.4 m on both the X and Y directions and a mean distance of about 0.8 m between the datasets. Besides its efficiency and high degree of automation, the algorithm generates a warped version of OSM buildings which, having by definition a closest match to the reference buildings, can be eventually integrated in the OSM database.
IMPREMENTATION OF VGI-BASED GEOPORTAL FOR EMPOWERING CITIZEN’S GEOSPATIAL OBSERVATORIES RELATED TO URBAN DISASTER MANAGEMENT
Authors: Lee Sanghoon
Published: August 2016

The volunteered geospatial information (VGI) will be efficient and cost-effective method for generating and sharing large disasterrelated geospatial data. The national mapping organizations have played the role of major geospatial collector have been moving toward the considering public participation data collecting method. Due to VGI can conduct to encourage public participation and empower citizens, mapping agency could make a partnership with members of the VGI community to help to provide well-structured geospatial data. In order to effectively be understood and sharing the public semantics, datasets and action model of the public participation GeoPortal, the implemented VGI-GeoPortal designated as the basis of ISO 19154, ISO 19101 and OGC Reference Model. The proof of concepts of VGI-GeoPortal has been implemented urban flooding use-case in Republic of Korea to collect from the public, and analyze disaster-related geospatial data including high-disaster potential information such as the location of poor drainage sewer, small signs of occurring landslide, flooding vulnerability of urban structure, and etc.
GEOMATICS FOR SMART CITIES: OBTAINING THE URBAN PLANNING BAF INDEX FROM EXISTING DIGITAL MAPS
Authors: Casella V. , Franzini M. , De Lotto R.
Published: August 2016

The urban analytics expression is spreading out. To our understanding, it deals with the capability of measuring cities and their communities, as a support to their effective planning and management. In other words, being an analytically well-known city is a precondition for pursuing smartness. Urban planning is a very important item for city management and is interrelated with many layers, including urban environmental quality, air quality and well-being. Effective urban planning is based on the knowledge of quantitative parameters such as the biotope area factor (BAF), which was originally proposed for the city of Berlin and is currently used in other cities. The BAF index is used to evaluate the degree of soil permeability and measures, to a certain extent and from a specific point of view, how a city is eco-friendly. The usual way of evaluating the BAF is based on the manual construction of dedicated maps, using existing orthophotos and oblique imagery as a support. But this method is expensive, time-consuming and non-objective, as it is prone to different interpretations. The paper presents a newly-developed methodology for calculating the BAF. It is based on the use of existing digital cartography and on the application of geoprocessing techniques from GIS science: it is therefore fully automated and objective. The Pavia city (Northern Italy) is used as a testsite and a careful validation of the developed methodology is carried out, by comparison to 12 manually surveyed test areas, corresponding to 5% of the built-up areas of the municipality.
INVESTIGATING THE INFLUENCE OF TREE COVERAGE ON PROPERTY CRIME: A CASE STUDY IN THE CITY OF VANCOUVER, BRITISH COLUMBIA, CANADA
Authors: Chen Yifei , Li Yuenan , Li Jonathan
Published: August 2016

With the development of Geographic Information Systems (GIS), crime mapping becomes an effective approach to investigate the spatial pattern of crime in a defined area. Understanding the relationship between crime and its surrounding environment can reveal possible strategies that can reduce crime in a neighbourhood. The relationship between vegetation density and crime has been under debate for a long time. This research is conducted to investigate the impacts of tree coverage on property crime in the City of Vancouver. High spatial resolution airborne LiDAR data collected in 2013 was used for the extraction of tree covered area for cross-sectional analysis. The independent variables were put into Ordinary Least-Squares (OLS) regression, Spatial Lag regression, and Geographically Weighted Regression (GWR) models to examine their influences on property crime rates. According to the results, the cross-sectional analysis demonstrated statistical evidences that property crime rates had negative correlations with tree coverage, with greater influences occurred around Downtown Vancouver.
ROAD NETWORK EXTRACTION FROM DSM BY MATHEMATICAL MORPHOLOGY AND REASONING
Authors: Li Yan , Wu Jianliang , Zhu Lin , Tachibana Kikuo
Published: August 2016

The objective of this research is the automatic extraction of the road network in a scene of the urban area from a high resolution digital surface model (DSM). Automatic road extraction and modeling from remote sensed data has been studied for more than one decade. The methods vary greatly due to the differences of data types, regions, resolutions et al. An advanced automatic road network extraction scheme is proposed to address the issues of tedium steps on segmentation, recognition and grouping. It is on the basis of a geometric road model which describes a multiple-level structure. The 0-dimension element is intersection. The 1-dimension elements are central line and side. The 2-dimension element is plane, which is generated from the 1-dimension elements. The key feature of the presented approach is the cross validation for the three road elements which goes through the entire procedure of their extraction. The advantage of our model and method is that linear elements of the road can be derived directly, without any complex, non-robust connection hypothesis. An example of Japanese scene is presented to display the procedure and the performance of the approach.
ESTIMATING PM2.5 IN THE BEIJING-TIANJIN-HEBEI REGION USING MODIS AOD PRODUCTS FROM 2014 TO 2015
Authors: Li Yuenan , Wang Jianjun , Chen Cheng , Chen Yifei , Li Jonathan
Published: August 2016

Fine particulate matter with a diameter less than 2.5 μm (PM2.5) has harmful impacts on regional climate, economic development and public health. The high PM2.5 concentrations in China’s urban areas are mainly caused by the combustion of coal and gasoline, industrial pollution and unknown/uncertain sources. The Beijing-Tianjin-Hebei (BTH) region with a land area of 218,000 km2, which contains 13 cities, is the biggest urbanized region in northern China. The huge population (110 million, 8% of the China’s population), local heavy industries and vehicle emissions have resulted in severe air pollution. Traditional models have used 10 km Moderate-resolution Imaging Spectroradiometer (MODIS) Aerosol Optical Depth (AOD) products and proved the statistical relationship between AOD and PM2.5. In 2014, the 3 km MODIS AOD product was released which made PM2.5 estimations with a higher resolution became possible. This study presents an estimation on PM2.5 distributions in the BTH region from September 2014 to August 2015 by combining the MODIS satellite data, ground measurements of PM2.5, meteorological parameters and social-economic factors based on the geographically weighted regression model. The results demonstrated that the 10 km AOD product provided results with a slightly higher accuracy although the 3 km AOD product could provide more information about the spatial variations of PM2.5 estimations. Additionally, compared with the global regression, the geographically weighed model was able to improve the estimation results.