GISA2018_Mapillary

* 地理情報システム学会第27回学術研究発表大会
* Mapillary＆OpenStreetMapによるマイクロマッピング

Transcript

1. /2 8    !       10 M
2. /0 12 8 -- 0 : 1 A B 4 C 1 :2
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4. 01 / / 2 5 8 https://www.mapillary.com/app/
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8. 01 / / 2 89 Source: https://taginfo.openstreetmap.org/keys/mapillary#overview
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10. /0 12 8 # # # https://mapillary.github.io/mapillary_greenhouse/
11. /0 12 8 https://mapillary.github.io/mapillary_greenhouse/global-challenge/q2-2018/ Score = (total unique KM contributed + (images/1000)) * (1 + (count of local participants/10))
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13. 01 / / 2 4 8 Mapillary images are available under the Creative Commons Attribution-ShareAlike 4.0 (CC BY-SA 4.0)
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     https://www.mapillary.com/pricing
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       http://openaccess.thecvf.com/content_ICCV_2017/papers/Neuhold_The_Mapillary_Vi stas_ICCV_2017_paper.pdf
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    The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes doi: 10.1109/ICCV.2017.534
17. /0 12 8 - - - - ( - ( - - ) https://research.mapillary.com/lsun.html
18. 01 / / 2 89 Mapillary Vistas Dataset  25,000 & 100   
      https://research.mapillary.com/
19. /0 12 8 0 2 1 , •Road markings: increased from 6 classes to 21 classes including speed limits and direction arrows, which are essential classes for building navigation and HD maps. •Driveway: a new class that we’ve introduced to help enhance data extraction for last-mile •Barriers: increased from 6 to 11 classes. Detailed refinement of the barrier classes is the first step to enhance the perception capacity for autonomous driving under different scenarios. •Signages: the billboard class is now refined to advertisement, storefront, and general information signages. This facilitates automatic and in-depth location-aware data extraction for marketing, business, and point-of-interest analysis. •Traffic light states: dynamic properties are associated with static objects. In this case, we have refined 100K traffic light annotations with different state
20. /0 12 8 http://www.robustvision.net/
21. /0 12 8 https://blog.mapillary.com/update/2018/06/14/robust-cv.html
22. 01 / / 23 8 : https://arxiv.org/abs/1712.02616
23. 01 / / 2 4 8 In-Place Activated BatchNorm for Memory-Optimized Training of DNNs https://arxiv.org/abs/1712.02616
24. 01 / / 2 5 8 3/ ) ( 0 . 3 . https://github.com/mapillary/mapillary-js
25. 01 / / 2 6 8 https://blog.mapillary.com/update/2018/06/12/mapillary-for-organizations.html
26. . 788 / 6 1 66 . MQ e V e W 3 . ( https://github.com/enricofer/go2mapillary • 0 1 ( lnx i WM G • tpJO V i W • 0 tp 0 1 M IG 0365276 • . 788 1 b Jr • aS Vw Thanks! Christopher Beddow
27. 0 0 C . 0 RSe Wle cm . . / . • Fdmh m O r . 0 F Ok fF F • 0 0 C . 0: / 8 : 2 0 :: P 18 • yv Stw O nsoMp • mai fF gm 4 0:0 1 0 1 Thanks! Christopher Beddow
28. /8244/9 81 . 911 /8 M PS a • a I . C c • 1IS 198/ Ie • 20 /9 • IOa i nI j • in p r om l AM . . / . Thanks! Christopher Beddow
29. 01 / / 23 8  
    
30. 01 / / 23 8 Thank you! tosseto@csis.u-tokyo.ac.jp http://researchmap.jp/tosseto https://speakerdeck.com/tosseto