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Las Vegas road network inference, colored by road speed from yellow (25 mph) to red (65 mph).

Inferring Route Travel Times with SpaceNet

Adam Van Etten
Aug 9, 2019 · 6 min read
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Figure 1: Training data. (Left) Input image. (Middle) Typical binary road training mask (not used in this study). (Right) Multi- class training mask showing individual speed channels: red = 21–30 mph, green = 31–40 mph, blue = 41–50 mph.
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Figure 2: Segment speed estimation. Top: Example road edge (yellow), with midpoints of each segment shown in blue. Bottom left: Zoomed in region around the green mid- point. Bottom right: Corresponding portion of the prediction mask (8 × 8 pixel patch size in red); speed is estimated with this mask patch.
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Table 1. Full CRESIv2 algorithm for extracting road networks at scale.
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Table 2. Performance on SpaceNet test chips.
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Figure 3: Algorithm performance on SpaceNet. (a) Ground truth and (b) predicted multi-class masks: red = 21–30 mph, green = 31–40 mph, blue = 41–50 mph, yellow = 51–60 mph. © Ground truth and (d) predicted graphs overlaid on the SpaceNet test chip; edge widths are proportional to speed limit. The scores for this proposal are APLS_length = 0.80 and APLS_time = 0.64.
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Figure 4: CRESIv2 road speed. Output of CRESIv2 inference as applied to a portion of the SpaceNet Las Vegas large area test region. The APLS_length score for this prediction is 0.85, and the APLS_time score is 0.82. Roads are colored by inferred speed limit, from yellow (20 mph) to red (65 mph).
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Figure 5. Road speed inference over Shanghai.

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab…

Thanks to Jake Shermeyer

Adam Van Etten

Written by

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

Adam Van Etten

Written by

The DownLinQ

Welcome to the official blog of CosmiQ Works, an IQT Lab dedicated to exploring the rapid advances delivered by artificial intelligence and geospatial startups, industry, academia, and the open source community

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