Automatic Large Scale Topographical Map Updating using Open Street Map (OSM) Data within NoSQL Database Platform 19th AGILE Conference Helsinki, June 14th 2016 Winhard Tampubolon AGIS, UniBW, München

Outline Background and Introduction Motivation & Objectives Technical Implementation Accuracy Assessment Results Closing statement

Background (1) Large Scale Topographical Mapping (LSTM) Space Borne Data Acquisition 1 2 Airborne Mission 3 Large Scale Topographical Mapping (LSTM) Open Access Geoinformation (Bing Imagery, OSM) UAV Mission 4 RADAR VHRS Ortho Project Map Updating Aerial Campaign DSM DTM TLM TanDEM-X IDEM SRTM / ASTER GDEM DTM RBI Contour Geodatabase Cartographic Map   3

Available (Map sheets) Not Available (Map sheets) Background (2) Nr. Scale Map Size Volume (Map sheets) Available (Map sheets) Not Available (Map sheets) Long (Km) Width (Km) Small 1 1.000.000 668 442 37 2 500.000 334 221 103 3 250.000 167 111 309 Medium 4 100.000 55,6 1.245 5 50.000 27,83 3.899 2.417 1.482 6 25.000 13,82 13.020 1.774 11.246 Large 7 10.000 4,6 91.547 658 90.889 8 5.000 2,3 379.014 128 378.886 9 2.500 1,15 880.206 10 1.000 0,577 2.729.319

Background (3) Large Scale Topographical Mapping as a mandatory Input/framework for disaster preparedness and emergency response Very High Resolution Satellite (VHRS) Imagery as an opportunity (SPOT-6, Pleiades, Worldview) Open access geoinformation (OSM) NoSQL database (document based) Map updating vs. map production Different topographical object classification e.g. Tags, Feature codes, Attributes   5

Background (4) Current approach for OSM positional accuracy assessment (Helbich, 2011) Based on attributical information i.e. concantenation of Street names as feature ID   6

Motivation NoSQL database for encountering various data schema and structure Geospatial analysis on topographical object recognition Dynamic large scale topographical map updating Topographical Map Accuracy 1:5,000 Flexible data transfer among different data structures

Objectives Algorithm creation based on geospatial aspects Updating mechanism within NoSQL Database i.e. mongoDB (pymongo) and ESRI Platform (arcpy) Using OSM Data to update Large Scale Topographical Map

Technical Implementation (1) Vector data extraction Attribute&Thematic Identification Topographic Line Map (TLM) Open Street Map (OSM)   Point Features Geospatial Signature Identification Accuracy Assessment Line features Threshold Feature Extraction Topological Check Vector data integration Updated TLM Topographic Geodatabase Production

Technical Implementation (2) 𝐺 𝑠𝑖𝑔𝑛 (𝑑𝑋,𝑑𝑌)= 𝑛=1 𝑚 𝑑𝑖𝑠𝑡 ∗sin ∝ , 𝑛=1 𝑚 𝑑𝑖𝑠𝑡 ∗cos ∝ 𝐷𝑒𝑣 𝑠𝑖𝑔𝑛 (𝑑𝑋,𝑑𝑌)=( min 𝑚 𝑑𝑋 2 + 𝑑𝑌 2 ) where : 𝐺 𝑠𝑖𝑔𝑛 = Geospatial signature (OSM and Topomap) m = the number of points within regional based approach dist = distance from each point to the analyzed point α = horizontal angle (direction) from analyzed point to each other point 𝐷𝑒𝑣 𝑠𝑖𝑔𝑛 = minimized deviation for each point (only for OSM) dX = deviation in X axis dY = deviation in Y axis Region-based approach Point-based analysis

(json, geospatial index, free schema) Technical Implementation (3) NoSQL Database (mongoDB) to combine different data structures Document-based approach to localize geospatial analysis processes in one single (geo)database Flexible update processes ($set) documents (json, geospatial index, free schema) topomap Geo-signature topo Geo-accuracy osmmap Geo-update

Accuracy Assessment (1) NSSDA (National Standard for Spatial Data Accuracy) 95 % level of confidence Not consider publication map scale (more strict) NMAS (National Mapping Accuracy Standard) 90 % level of confidence Consider publication map scale (moderate)   12

Accuracy Assessment (2) Two determinant parameters : Deviation (Dev sign ) & Distance (RMS) Iterative process

Result (1) New feature (object) detection

Result (2) 33 / 847 analyzed points (vertices) Updated feature objects Accuracy and deviation

Demo : Arcpython Toolboxes Result (3) Demo : Arcpython Toolboxes 5 major components direct connection to MongoDB Arcpy geospatial processing

Conclusion Recommendation Demonstration of the large scale topographical mapping updating mechanism by utilizing NoSQL database both as the GIS processing unit and the geospatial data warehouse; Region-based approach is still considered as the best solution Recommendation Further algorithm extension for automatic transformation/georeferencing; Implementation for other purposes e.g. GCP common points detection between different datasets

Thank you for your attention 18