Automatic Extraction of Road Intersection Position, Connectivity and Orientations from Raster Maps
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1 Automatic Extraction of Road Intersection Position, Connectivity and Orientations from Raster Maps Yao-Yi Chiang and Craig Knoblock University of Southern California epartment of Computer Science and Information Sciences Institute 0
2 Introduction Raster maps are one important source of geospatial data: Contain information that is difficult to find elsewhere Contain the most complete set of data USGS topographic map Map of Tehran, Iran 1
3 Introduction In [Chen et al. 2008], we utilize the set of road intersection templates as the fingerprint of the raster map to integrate raster maps with other geospatial data Road intersection template: Road intersection position, connectivity, and road orientation Vector data 2 Satellite Imagery Raster map
4 USGS Topographic Map, El Segundo, CA USA
5 USGS Topographic Map, El Segundo, CA USA
6 0 USGS Topographic Map, El Segundo, CA USA
7 90 0 USGS Topographic Map, El Segundo, CA USA
8 USGS Topographic Map, El Segundo, CA USA
9 TIGER/Line Vector Data with Geo-coordinate Information
10 TIGER/Line Vector Data with Geo-coordinate Information
11 TIGER/Line Vector Data with Geo-coordinate Information
12 TIGER/Line Vector Data with Geo-coordinate Information
13 TIGER/Line Vector Data with Geo-coordinate Information
14 TIGER/Line Vector Data with Geo-coordinate Information
15 Found the map location!! TIGER/Line Vector Data with Geo-coordinate Information
16 Previous Work The accuracy of the road intersection templates is important Help to prune the searching space during the matching Challenges for extracting the road intersection templates: Limited access to the metadata of the maps Maps are complex
17 Previous Work In [Chiang et al. 2008], we worked on the pixel level to decompose the raster maps and to extract the road intersections automatically Remove Background Remove Noise and Rebuild Road Layer Identify Road Intersection Candidates
18 Previous Work A simpler method to identify road intersections and extract the road intersection templates We also determine the road format (i.e., single or double line) and extract the road width 17
19 Previous Work A simpler method to identify road intersections and extract the road intersection templates We also determine the road format (i.e., single or double line) and extract the road width Road Intersection!! Road width 18
20 Previous Work Lines are distorted by the thinning operator The extracted road intersection templates are not accurate 19
21 Extract Accurate Road Intersection Templates The distortion is caused by using the thinning operator on thick lines The extent of the distortion is determined by the road width Rebuild road layer 20
22 Extract Accurate Road Intersection Templates In this work, we skip the distorted areas and trace the straight lines to extract accurate road intersection templates Road width 21
23 Extract Accurate Road Intersection Templates The thinned lines Intersection The blob image Positions 22
24 Extract Accurate Road Intersection Templates The size of a blob is determined using the road with for covering the distorted lines The thinned lines Intersection The blob image Positions 23
25 Extract Accurate Road Intersection Templates The size of a blob is determined using the road with for covering the distorted lines The thinned lines Intersect the thinned line image with the blob image Intersection The blob image Positions 24
26 Extract Accurate Road Intersection Templates Identify contact points Trace road line candidates from contact points 25
27 Extract Accurate Road Intersection Templates Trace road line candidates from contact points Trace only a certain amount of line pixels to prevent looping Road lines are straight within a small distance (e.g., 5 pixels) Fit a line function (Y= ax+b) to the traced pixels using Least- Squares Fitting algorithm 26
28 Extract Accurate Road Intersection Templates Trace road line candidates from contact points Trace only a certain amount of line pixels to prevent looping Road lines are straight within a small distance (e.g., 5 pixels) Fit a line function (Y= ax+b) to the traced pixels using Least- Squares Fitting algorithm 27
29 Extract Accurate Road Intersection Templates Trace road line candidates from contact points Trace only a certain amount of line pixels to prevent looping Road lines are straight within a small distance (e.g., 5 pixels) Fit a line function (Y= ax+b) to the traced pixels using Least- Squares Fitting algorithm 28
30 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the intersection of the line candidates to update the template Original map 2 Blob image intersected with thinned lines Traced line functions 29
31 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Original map Blob image intersected with thinned lines 30
32 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Blob image intersected with thinned lines Traced line functions 31
33 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Blob image intersected with thinned lines Traced line functions 32
34 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Blob image intersected with thinned lines Traced line functions 33
35 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Blob image intersected with thinned lines Traced line functions 34
36 Extract Accurate Road Intersection Templates Update road intersection templates Keep every road line candidate Use the centroid of the intersections of the line candidates to update the template Blob image Centroid intersected with thinned lines Traced line functions 35
37 Extract Accurate Road Intersection Templates Update road intersection templates Remove outliers and use the centroid of remaining intersections Original map Blob image intersected with thinned line 36
38 Extract Accurate Road Intersection Templates Update road intersection templates Remove outliers and use the centroid of remaining intersections 2 ntersection ( pproximate location) Original map 3 Blob image intersected with thinned 4 line Traced line functions 37
39 Extract Accurate Road Intersection Templates Update road intersection templates Remove outliers and use the centroid of remaining intersections 2 ntersection ( pproximate location) Original map 3 Blob image intersected with thinned 4 line Traced line functions 38
40 Extract Accurate Road Intersection Templates Update road intersection templates Remove outliers and use the centroid of remaining intersections 2 ntersection ( pproximate location) Outlier 3 4 Original map 3 Blob image intersected with thinned 4 line Traced line functions 39
41 Extract Accurate Road Intersection Templates Update road intersection templates Remove outliers and use the centroid of remaining intersections Outlier Centroid 3 4 Original map 3 Blob image intersected with thinned 4 line Traced line functions 40
42 Extract Accurate Road Intersection Templates Previous Results This Paper 41
43 Experiments Ground truth We evaluate 10 raster maps from five different sources Manually verify each extracted road intersection templates with the ground truth The ground truth 42
44 Experiments - Metrics Positional offset: The average number of pixels between the extracted road intersection templates and the actual road intersections in the raster maps 43
45 Experiments - Metrics Orientation offset: The average number in degrees between the extract road orientations and the actual road orientations. 44
46 Experiments - Metrics The connectivity offset: The total number of missed road lines. Connectivity offset is one 45
47 Experimental Results Extracted139 road intersection templates with 438 lines from 10 test maps The average positional offset: 0.4 pixels The average orientation offset: 0.24 degrees Extracted road intersection templates are very close to the ground truth The connectivity offset: We missed 13 lines from a total of 451 lines 97% of the lines are extracted Lines that do not have accurate orientations were discarded 46
48 Positional Offset Compared to Previous Work Previous Work This Paper Pixel 47
49 25 20 Orientation Offset Compared to Previous Work Previous Work This Paper Degree
50 Related Work Localized Template Matching to improve the positional offset (Chiang et al. 08) The templates used for matching are not accurate Cluster corner points to extract road intersections (Habib and Uebbing 99) Cannot extract accurate intersection templates Geometrical analyses to extract lines (Cao et al. 02 and Li et al. 00) Do not extract intersection templates Color segmentation to extract lines (Khotanzad and Zink 03; Chen et al. 06) Do not extract intersection templates 49
51 Discussion Our technique automatically extracts accurate road intersection templates from raster maps. Average positional offset: 0.4 pixels Average orientation offset: 0.24 degrees Accurate road intersection templates help to: Reduce searching space for map conflation application Use the intersection templates as seed points to extract road from imagery More 50
52 Future Work Include manual training to extract more information from raster maps Labels, landmarks Include manual training to process more complex maps A metro map with different types of lines Identify the training process that minimizes human intervention Reuse the training results on similar maps 51
53 Thank You 52
54 Introduction In our previous work [Chen et al. 2008], we extract the road intersection templates to integrate raster maps with imagery Road intersection template: Road intersection position, connectivity, and road orientation Extract Intersections Extract Intersections Vector data Extract Intersections 53 Satellite Imagery Raster map
55 Introduction Label the imagery with features on the map Satellite imagery of Tehran, Iran from Google Maps Tehran map from Google Image Search Align the map with the satellite imagery 54
56 Introduction Use the intersection templates as seed points to extract road from imagery 55
57 Experiments - Metrics Positional offset: The average number of pixels between the extracted road intersection templates and the actual road intersections in the raster maps Orientation offset: The average number in degrees between the extract road orientations and the actual road orientations. The connectivity offset: The total number of missed road lines. 56
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