1. 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

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

3. 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

4. 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
668
442 37 2
334
221
103 3
167
111
309
Medium 4
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
128 9
2.500
1,15 10
1.000
0,577

5. 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

6. 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

7. 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

8. 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

9. 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

10. 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

11. (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

12. 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

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

14. Result (1)
New feature (object) detection

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

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

17. 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

18. Thank you for your attention18