1. why is automation useful? 2. per-cell classification

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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http://www.youtube.com/watch?v=at7STAaajHk



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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

Perfect GPS

Road Map Observation



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Map inference

GPS noiseRoad Map Observation



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Map inference

Road Map ObservationGPS noise

Disparity

Kernel Density Estimation (Cell Classification)

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Histogram


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Histogram

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Histogram


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Histogram Histogram (After Blur)


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Histogram (After Blur)

???


Skeletonization

26http://www.inf.u-szeged.hu/~palagyi/skel/skel.html

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

Skeletonization

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Skeletonization

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Pick up a thresholdBinarize the histogram

(Not Real)

Skeletonization

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Pick up a thresholdBinarize the histogram

(Not Real)

Skeletonization

Binary-Skeletonization: Issues

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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

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

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

CNN-Based Segmentation: Problems

Noise in the CNN output lead to incorrect topology in the extracted graph● Missing roads● Frequent connections between parallel roads

CNN-Based Segmentation: Problems

Complex intersections lead to very noisy graphs



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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.

RoadRunner: Improving the Precision of Road Network Inference from GPS Trajectories

200m 200m

RoadRunnerOthers

RoadRunner Key IdeaExploit Long-term Structure of GPS Trajectories

Road Network GPS Trajectories Individual Samples Inferred Graph

RoadRunner Key IdeaExploit Long-term Structure of GPS Trajectories

Road Network GPS Trajectories Inferred GraphLong-term Structure

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)

Iterative Tracing





Sliding Window

GPS Trajectories





Sliding WindowInitialize v = starting location v0Repeat:● Find GPS trajectories matching



RoadRunner: Merging




RoadRunner: Merging

Sliding Window

Sliding

Wind

ow

RoadRunner: Merging



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CNN

RoadTracer

[0.1, 0.9, 0.03, 0.01]N E S W

Action: [Move, Stop]

CNN

RoadTracer

[0.05, 0.6, 0.2, 0.01] N E S W


CNN

RoadTracer

[0.05, 0.7, 0.5, 0.05] N E S W


CNN

RoadTracer

[0.1, 0.8, 0.05, 0.01]N E S W


CNN

RoadTracer: Forks

[0.65, 0.6, 0.01, 0.01] N E S W


CNN

RoadTracer: Forks

[0.8, 0.7, 0.01, 0.7] N E S W


CNN

RoadTracer: Forks

[0.8, 0.05, 0.01, 0.05] N E S W


CNN

RoadTracer: Forks

[0.01, 0.05, 0.01, 0.05] N E S W


CNN

RoadTracer: Forks

[0.01, 0.7, 0.01, 0.7] N E S W


CNN

RoadTracer: Forks

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http://www.youtube.com/watch?v=bh0OxTbccg4



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OpenStreetMap in Rural Indonesia

http://www.youtube.com/watch?v=lsnRRsW25vY

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.

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.

● 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

(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.

http://www.youtube.com/watch?v=1_BbTgUWQsw

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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http://www.youtube.com/watch?v=i-6nbuuX6NY

http://www.youtube.com/watch?v=iyVB0gmmnSc



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1. why is automation useful? 2. per-cell classification

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