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PO-15 Introduction to a New Geo-referenced Street Level Content : 3D GeoPano Boram Yoon (Dept. of Geoinformatics, The University of Seoul), Impyeong Lee PO-16 GIS Oriented Service Optimization Tool For Fecal Sludge Collection Mohammad Dalower Hossain (Asian Institute of Technology), Sarawut Ninsawat, Shulaxan Sharma, Thammarat Koottatep, Yuttachai Sarathai PO-17 A Study of the Development and Distribution of Open Geospatial Data in Japanese Local Governments Toshikazu Seto (CSIS of the University of Tokyo), Yoshihide Sekimoto, Shu Higashi PO-18 Neo-geography and Geography Education in the Japanese Universities Yuichiro Nishimura (Nara Women's University) PO-19 Modeling of Terminology Database in Field of Geodesy and Geographic Information System Zolzaya Lkhamsuren (Mongolian Geospatial Association), Oyuntsetseg Dash, Alimaa Jargalsaikhan, Ochirkhuyag Lkhamjav PO-20 Vulnerability Assessment Using Free and Open Source Software (FOSS) and Open Source Data: Talomo District , Davao City, Philippines Glenn Depra (Ateneo de Davao University) PO-21 Building a Time Server with RaspberryPI & GPS, Connecting with a Free Web GIS Software Orkut Murat Yilmaz, Kerem Halicioglu (Bogazici University), Osman Bal, Ayca Eraslan, Ahmet Alper Yilmaz, Wumiti Julaiti, Nicholas Paul Libassi PO-22 Lightweight Urban Computation Interchange (LUCI) System Lukas Treyer (ETH Zurich), Bernhard Klein, Reinhard Koenig, PO-23 Development of Opensource-based Photogrammetric UAV System Using Smart Camera Jinwoo Park (Pukyong National University), Chuluong Choi, Hohyun Jung, Dongyoon Shin PO-24 Case Study: Mapping Mangroves and Coastal Wetlands Christopher Dubia (MapWorks Learning) PO-26 FREEWAT: FREE and open source tools for W ATer resource management Massimiliano Cannata (SUPSI) 12
AUTOMATIC IMPROVEMENT OF POINT-OF-INTEREST TAGS FOR OPENSTREETMAP DATA Sabine Storandt 1 and Stefan Funke 2 1 Insitut für Informatik, Albert-Ludwigs-Universität Freiburg 79110 Freiburg, Germany Email: storandt@informatik.uni-freiburg.de 2 FMI, Universität Stuttgart 70569 Stuttgart, Germany Email: funke@fmi.uni-stuttgart.de ABSTRACT The user experience of any OpenStreetMap (OSM) based service heavily depends on the quality of the underlying data. If the service deals with points-of-interest (POIs), consistent and comprehensive tagging of the respective map elements is a necessary condition for a satisfying service. In this paper, we develop methods that can automatically infer tags characterizing POIs solely based on the POI names. The idea being that many POI names already contain sufficient information for tagging. For example, 'Pizzeria Bella Italia' most certainly indicates an Italian restaurant. As the OSM data contains hundred of thousands POIs for Germany alone, we aim for a tool that can accomplish tag extrapolation in an automated way. In a first step, we automatically extract typical words and phrases that occur in names associated with a certain tag. For example, learning indicators for ‘shop=hairdresser’ on German OSM tags led to high scores for ‘fris’, ‘cut’, hair’ and ‘haar’. Having available such indicator phrases, we use standard machine learning techniques to derive the probability for a POI to exhibit a certain tag. If this probability exceeds a certain threshold, we assign the tag to the POI in an automated fashion. We used our extrapolation framework to create new amenity, shop, tourism, and leisure tags. The accuracy of our approach was over 85% for all considered tags. Moreover, for POIs tagged with amenity=restaurant, we aimed for extrapolating the respective cuisine tag. For more than 19 thousand out of 28 thousand restaurants in Germany lacking the cuisine-tag, our approach assigned a cuisine. In a random sample of those assignments 98% of these appeared to be true. 1. INTRODUCTION In particular in the context of community-based or crowd-sourced data gathering efforts like OpenStreetMap (OSM), the issue of data quality is of utmost importance. Irrespectively, how sophisticated and advanced the employed algorithms are, the user experience when using services based on the gathered data is first and foremost affected by the quality of the underlying data. Competing commercial offerings will always claim an alleged superior data quality to justify their business. Sometimes, their typically much more centralized approach of data maintenance and collection indeed allows for an easier monitoring of quality issues, for crowed-based approaches like OSM this is much harder to achieve. In the concrete case of OSM, data quality is very much dependent on the contributors' tagging discipline. While there are well thought out guidelines how to tag mapped elements, in principle every contributor can tag at his/her discretion. This freedom undisputedly has its advantages in terms of flexibility, it also creates some consistency problems, though. For example, when querying a geo-search engine or a location-based service for points-of-interest (POIs) in a certain region or next to the current user location, one often asks FOSS4G Seoul, South Korea | September 14 th – 19 th , 2015 13
Page 1 and 2: PROCEEDINGS Honil Gangni Yeokdae Gu
Page 3 and 4: C O N T E N T S Welcome Messages Lo
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4.3 Point data Figure 8: Diverging
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4.4 Non-spatial visualisations Figu
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6 ACKNOWLEDGEMENTS The Cooperative
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educational resources (OER) and mod
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workshops have revealed that microc
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300 250 200 150 100 50 0 Figure 4.
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H 27 Male Student Maths / Geography
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5. CONCLUSIONS AND FUTURE WORK We h
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DEVELOPMENT OF DATA ARCHIVING AND D
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2. RELATED WORK Recent developments
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3. Significance and Objectives With
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Figure 3. Map Tiles Grid Selection
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The Ceph architecture comprises of
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Boundless, 2015. GeoExplorer — Ge
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Object-Based Building Boundary Extr
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2.2. A Grid-Based Algorithm A grid-
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3.1 Boundary Extraction The extract
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The Development of Web3D-based Open
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Singh et al.(2012) have utilized op
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Monitoring concept on operating asp
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(a) Mining area (Left:2007, Right:2
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geospatial information open platfor
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open platform utilized in open-pit
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Data Acquisition Methods Acquisitio
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(a) Ecological Restoring Area 1 (Le
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possibility of development of open-
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2. DATA ACQUISITION 2.1 Image gathe
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Hugin Pix4Dmapper Pablo d'Angelo et
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utit may be improved using many ste
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Wójcicka, A., and Wróbel, Z., 201
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platforms, which are easy to use an
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SWE framework proposes the followin
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Steenkamp et al. (2009) state that
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technology and are published under
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Measurement value yes Measurement s
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4.3.1 Explore The explore view allo
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Figure 8. Registration of a generic
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participants completely agree with
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467 - 471. Sohraby, K., Minoli, D.,
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In augmented reality a live view of
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Figure 4. Dwellings in an informal
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1. Gen eral crite ria 1.1. Platform
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matching, 3D engine, image tracking
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1.4. Implemented standards OGC ARML
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2.3. Object events 2.4. Display rad
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instructional videos, and basic exa
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application for addressing, the pre
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Environments, 6(4), 355-385. Carmig
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1. INTRODUCTION Use of landmarks in
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the junctions which made with major
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4. IMPLEMENTATION 4.1 Technology Se
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Figure 3: Linear map output with si
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AN OPEN SOURCE WEB SERVICE FOR REGI
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2. IGSN OVERVIEW Figure 1 shows the
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types (e.g., dateType and relationT
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REST API(Fielding, 2000). The servi
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An Open Source Web Service for Regi
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Research Centre (ARRC) and the Nati
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Developing a land use database of t
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Figure 3. Land use input applicatio
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Paddy 19.4 23.7 Upland field, orcha
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Figure 6. Overlay point based land
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Integrating Open Source GIS Softwar
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Advanced GIS course, we recognized
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for undergraduates. Research take t
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Figure 8 The USDA Foodshed project
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4. Data is sent to the web pages ei
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Advances in Civic Co-management wit
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2. JAKARTA'S DISASTER RISK MANAGEME
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The aim of the system is to improve
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8. REFERENCES Baker, J. L., 2012. (
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EVALUATION OF AN OPEN-SOURCE COLLAB
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therelevant stakeholderswith differ
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at each stage of the exercise: · l
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differentmitigationmeasuresaccordin
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students before the exercise. 5 4 3
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the third stage of the exercise in
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doi:10.1016/j.envsoft.2003.12.019.
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e integrated directly in the Web-GI
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3. Implementation The main features
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Phase 1: Earthquake data Input Data
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Figure 4 . Magnitude 7.8 Earthquake
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After adding all the layer maps, th
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EVALUATING FLOOD HAZARD POTENTIAL I
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σ 0 = 10*log 10 (DN 2 ) + CF (1) W
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Figure 4. The geomorphological feat
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from water class in land cover map
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ANALYSIS OF SPATIAL DENSITY UTILIZI
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demographic data. He also states th
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Table 1. The combination of COUNTRY
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NUMBER OF SUBSCRIBERS 300,000 250,0
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Figure 9. The number of foreign tou
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Figure 16. Spatial call activity pa
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Recently, additional tourist inform
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country. It uses R-Studio, a statis
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Geosocial Big Data Analysis Using P
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AN ON-BOARD VISUAL-BASED ATTITUDE E
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3.1 Keypoints detection and matchin
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complete the process. This has been
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Fleet, D., & Weiss, Y. (2006). Opti
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technologies in Geoinformation Scie
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technologies becomes irrelevant in
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Figure 2. Server (upper block) and
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Name Presence Description Table 1.
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SampleExtraApp sampleExtraApp annot
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4. RESULTS Summing up, we should no
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Paradigms. John Wiley & Sons Inc.,
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FREEWAT: FREE and open source tools
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An Evaluation of Open Source Geogra
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2. DATA AND METHOD The approach ado
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the beginning and end of each trip
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Table 4 - GIS Routing Tools S/N GIS
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State cold store- Hungu Primary Hea
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Figure 3 - Comparing Discrepancy in
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nations based on human per capita i
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investigating metropolitan traffic.
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A Cross National Comparison on the
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in 1980(Saaty, 1980). It assesses t
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ex or current government officials
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Herold, S., & Sawada, M. C. (2012).
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frameworkwhich covers an infrastruc
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The architecture of the application
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5. CONCLUDING REMARKS Open source s
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cloud environment, so that end-user
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Figure 2 represents user interface
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geo-science application. Also the o
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PO-04 GeoDjango-Framwork-based Popu
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pH of 5.5 to 7.0; forest loam, rock
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Figure 4. Study Area (Davao Region)
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Figure 6. Topographic Map of Mindan
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Figure 8. Land Areas suitable for C
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It is respectfully recommended that
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ECDIS through introduction of Open
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to adopt development using open sou
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3. Conclusion ECDIS is navigation e
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PO-08 Development of an Agent Based
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1. INTRODUCTION 1.1 Background of t
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Figure 2. Workflow of the semi-auto
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threshold set. GRASS 7.0 is used in
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Figure 7. Result of the application
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Ming, D. M. D., Luo, J. L. J., Shen
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PO-10 3D Visualization of City GML
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As A Service (PAAS) and Infrastruct
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Figure 2: System Architecture and D
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The resulted crowd source data cont
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Table 1: Confusion Matrix Crowd sou
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PO-12 House Number Interpolation Fo
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(DOE) has awarded a total of 82 Gri
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Most solar radiation models compute
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2. OBJECTIVES, SCOPE, AND LIMITATIO
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Figure 4. BSWM Solar Sensors for Va
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4.2 Site Suitability Analysis Inter
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5. RESULTS AND DISCUSSION 5.1 Month
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Figure 8. Monthly Average Real-sky
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Figure 19. GHI for November Figure
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Figure 26. Non-Resource Criteria Su
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Kryza, M. et al. 2010. “Spatial i
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example coordinates agents, based o
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A benefit of using multiple agents
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A local GeoServer instance was set
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Figure 3. Screen captures of search
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In combination with ranking of resu
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INTRODUCTION TO A NEW GEO-REFERENCE
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Figure 2. ‘Click-to-go’ functio
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Figure 5. The conceptual structure
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Figure 6. Software architecture of
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GIS ORIENTED SERVICE OPTIMIZATION T
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Vehicle Routing Problem (VRP) refer
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So, buffer of 2 meters was created
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log data which was the actual dista
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was being served by three vehicles.
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Analyzing the number of customers,
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PO-17 A Study of the Development an
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MODELING OF TERMINOLOGY DATABASE IN
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3.2 Interpretation of the terms 4 I
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Figure 4 shows the result of SWOT a
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Add terms in the database: Super us
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6. REFERENCES Damdinsuren A., 2013.
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PO-20 Vulnerability Assessment Usin
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LEIGHTWEIGHT URBAN COMPUTATION INTE
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where events are instantly propagat
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not be conceived as responsive by a
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3.5. Remote Services Among a few
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4. USE CASES 4.1. Transition Worksh
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to display the rank of the player i
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PO-23 Development of Opensource-bas
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Author Index Aburizaiza, Ahmad O. 2
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