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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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Map inference with GPS trajectories
"Map inference in the face of noise and disparity." Biagioni, James, and Jakob Eriksson. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012.
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Map inference
Road Map Observation
GPS
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Map inference with GPS trajectories
"Map inference in the face of noise and disparity." Biagioni, James, and Jakob Eriksson. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012.
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Map inference
Perfect GPS
Road Map Observation
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Map inference with GPS trajectories
"Map inference in the face of noise and disparity." Biagioni, James, and Jakob Eriksson. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012.
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Map inference
GPS noiseRoad Map Observation
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Map inference with GPS trajectories
"Map inference in the face of noise and disparity." Biagioni, James, and Jakob Eriksson. Proceedings of the 20th International Conference on Advances in Geographic Information Systems. ACM, 2012.
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Map inference
Road Map ObservationGPS noise
Disparity
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Kernel Density Estimation (Cell Classification)
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Histogram
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Kernel Density Estimation (Cell Classification)
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Histogram
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Histogram
Kernel Density Estimation (Cell Classification)
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Histogram Histogram (After Blur)
Kernel Density Estimation (Cell Classification)
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Histogram (After Blur)
???
Kernel Density Estimation (Cell Classification)
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Skeletonization
26http://www.inf.u-szeged.hu/~palagyi/skel/skel.html
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27http://www.inf.u-szeged.hu/~palagyi/skel/skel.html
Iteration 0 Iteration 5 Iteration 10 Iteration 15 Iteration 20
Iteration 25 Iteration 30
Iteration 20
Iteration 35 Iteration 40 Iteration 70
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Skeletonization
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Histogram (After Blur)
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Skeletonization
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Histogram (After Blur)
Pick up a thresholdBinarize the histogram
(Not Real)
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Skeletonization
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Histogram (After Blur)
Pick up a thresholdBinarize the histogram
(Not Real)
Skeletonization
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Binary-Skeletonization: Issues
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Binary-Skeletonization: Issues High Threshold
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Binary-Skeletonization: Issues High Threshold
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Binary-Skeletonization: Issues High Threshold
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Binary-Skeletonization
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Binary-Skeletonization: Issues Low Threshold
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Binary-Skeletonization: Issues Low Threshold
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Summary: Kernel Density Estimation● Create histogram from GPS trajectories
● Blur the histogram
● Pick a threshold to convert grayscale histogram into a binary image
● Apply skeletonization to derive a road network38
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Inferring Maps from Aerial Imagery● May be able to obtain thousands of GPS trajectories per day in city centers● But in suburban and rural areas, the percentage of roads covered by
trajectories will be much smaller● Aerial imagery can fill in these gaps
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Image Segmentation
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CNN Post processing
Loss Function
CNN Output
OSM Rasterization
Road Network Graph
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Segmentation Methods
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CNN Post processing
Loss Function
CNN Output
OSM Rasterization
Road Network Graph
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Segmentation Methods
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Map Inference from Aerial Imagery: Extract Graph
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CNN-Based Segmentation: Problems
Noise in the CNN output lead to incorrect topology in the extracted graph● Missing roads● Frequent connections between parallel roads
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CNN-Based Segmentation: Problems
Complex intersections lead to very noisy graphs
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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Limited Precision in Challenging Scenarios
The inferred maps from two previous
state-of-the-art map inference algorithms
(using GPS data)
Los Angeles Boston Chicago
200m
200m
100m
100m
200m
200m
Previous state-of-the-art map inference algorithms have limited precision
Map Inference in the Face of Noise and Disparity. James Biagioni et al. SIGSPATIAL 2012.Robust Road Map Inference through Network Alignment of Trajectories. Rade Stanojevic et al. SIAM 2018.
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RoadRunner: Improving the Precision of Road Network Inference from GPS Trajectories
200m 200m
RoadRunnerOthers
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RoadRunner Key IdeaExploit Long-term Structure of GPS Trajectories
Road Network GPS Trajectories Individual Samples Inferred Graph
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RoadRunner Key IdeaExploit Long-term Structure of GPS Trajectories
Road Network GPS Trajectories Inferred GraphLong-term Structure
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RoadRunner: Iterative Construction
Known Starting Location
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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Iterative Tracing
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Iterative Construction
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Iterative Construction
Sliding Window
GPS Trajectories
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Iterative Construction
Sliding WindowInitialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Iterative Construction
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Merging
Initialize v = starting location v0Repeat:● Find GPS trajectories matching
current path● See what location(s) the trajectories
move in from v● Add vertices in those locations● (Check merging)
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RoadRunner: Merging
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RoadRunner: Merging
Sliding Window
Sliding
Wind
ow
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RoadRunner: Merging
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RoadRunner: Merging
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RoadRunner: Merging
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RoadRunner: Merging
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RoadRunner: Merging
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RoadRunner: Merging
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RoadRunner: Merging
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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CNN
RoadTracer
[0.1, 0.9, 0.03, 0.01]N E S W
Action: [Move, Stop]
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CNN
RoadTracer
[0.05, 0.6, 0.2, 0.01] N E S W
Action: [Move, Stop]
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CNN
RoadTracer
[0.05, 0.7, 0.5, 0.05] N E S W
Action: [Move, Stop]
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CNN
RoadTracer
[0.1, 0.8, 0.05, 0.01]N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.65, 0.6, 0.01, 0.01] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.8, 0.7, 0.01, 0.7] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.8, 0.05, 0.01, 0.05] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.01, 0.05, 0.01, 0.05] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.01, 0.05, 0.01, 0.05] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
[0.01, 0.7, 0.01, 0.7] N E S W
Action: [Move, Stop]
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CNN
RoadTracer: Forks
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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OpenStreetMap in Rural Indonesia
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Why hasn’t automatic map inference gained traction?
● Over a decade of research on automatically constructing road maps● Systems build maps from satellite imagery and GPS trajectories
Images from “Mining Large-Scale, Sparse GPS Traces for Map Inference: Comparison of Approaches.”Xuemei Liu, James Biagioni, Jakob Eriksson, Yin Wang, George Forman, Yanmin Zhu. KDD 2012.
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Why hasn’t automatic map inference gained traction?High Error Rates: ~5%1, but still too many false positives for full automation
[1, 19] “Robust Road Map Inference through Network Alignment of Trajectories.” Rade Stanojevic et al. SIAM 2018.[6] “Map Inference in the Face of Noise and Disparity.” James Biagioni et al. SIGSPATIAL 2012.
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● Machine-Assisted iD○ Pruning minor roads: add arterial roads in low-coverage regions○ Jump: accelerate improvement of map in high-coverage regions
● Map Inference for Interactive Mapping
Machine-Assisted Map Editing
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● Machine-Assisted iD○ Pruning minor roads: add arterial roads in low-coverage regions○ Jump: accelerate improvement of map in high-coverage regions
● Map Inference for Interactive Mapping
Machine-Assisted Map Editing
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(1) MAiD generates a yellow overlay covering automatically inferred road segments.
Machine-Assisted iD: Integrating Map Inference into Map Editors
(2) User presses H to hide the overlay and verify positions of roads in the imagery.
(3) User left clicks on the overlay to materialize inferred segments into the map.
(3) Right clicks remove incorrect edges in the overlay.
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Making Machine-Assistance UsefulInferred segments often correspond to short, straight roads that are not much faster to validate than to trace by hand.
Solution: focus on tasks where machine-assistance can improve productivity substantially:● Low-coverage regions: adding major, arterial roads to the map● High-coverage regions: eliminate painstaking effort of scanning the imagery for
unmapped roads
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Automation for Digital Maps
1. Why is automation useful?2. Per-Cell Classification3. RoadRunner: Iterative Tracing with GPS Trajectories4. RoadTracer: Iterative Tracing with Aerial Imagery5. Semi-automation Opportunities
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