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Critique of: Automatic and Accurate Extraction of Road Intersections from Raster Maps

Vikram Reddy Donthi Reddy Toufong Vang CSCI 8715 September 20, 2011. Critique of: Automatic and Accurate Extraction of Road Intersections from Raster Maps. by Yao-Yi Chiang · Craig A. Knoblock · Cyrus Shahabi · Ching-Chien Chen. Problem Statement.

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Critique of: Automatic and Accurate Extraction of Road Intersections from Raster Maps

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  1. Vikram Reddy Donthi Reddy Toufong Vang CSCI 8715 September 20, 2011 Critique of:Automatic and Accurate Extraction of RoadIntersections from Raster Maps by Yao-Yi Chiang · Craig A. Knoblock · Cyrus Shahabi · Ching-Chien Chen

  2. Problem Statement Difficult to accurately and automatically extract road intersections from raster maps. • Significance of Research • Spatial Data set for rasters does not include or identify road intersections. (Road intersections are object-based models .) • These features may be used to combine or process spatial data sets. • Developed a framework for accurate and automatic separation of specific object-based models from raster. • Difficulty in Accomplishing • Maps are complex. • Computers have difficulty distinguishing map features and elements from one another. • Current methods require user input/intervention to process.

  3. Contributions • Major Contributions of the Research • Developed method for automatically extracting road data. • 95% precision. • 75% completeness. • Researchers’ method does not require prior knowledge of the map. • Most significant? • 95% accuracy + 75% completeness. • Automatic extraction of map data. • Why? • Rapid development and integration of data where none may exist.

  4. Key Concepts Go from raster image of Tehran… (Google map.) …to hybrid map. (Google map + Tourist map.) Chiang et al, 2009

  5. Key Concepts The Approach Raster Map Binary Map Road Layer 1. Automatic Segmentation 2. Extract and rebuild road layer 3. Identify road intersections and extract. Road Intersection Pts, connectivity, and orientation

  6. Key Concepts Segmentation (remove background). Researchers premise: foreground colors has high contrast to background colors.

  7. Key Concepts Preprocessing (extract road layers a and b). Rebuild road layer (c and d). ID and extract road data (e).

  8. Validation Methodology The Experiment • Related Work • Utilized related research and methods. • Segmentation process. • Road extraction and rebuild. Researchers’ Prior Work Localized template matching (LTM) (compare experiment results with original raster)

  9. Validation Methodology Evaluation Verification of accuracy of process. Geometric Similarity (Lay term: how close is the extracted point from to the original point on the raster?)

  10. Critique Research assumption Road lines are straight within small distances. Linear structures are mainly roads. Falls apart when handling canals and other man-made non-road features.

  11. Revisions? • Framework • Straightforward. • Solid. • Add… • Process for handling artificial map features that are not necessarily roads (e.g., canals)

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