Day3-1515-Using image detections in map editing

Sunday 3:15pm, Room 1
Edoardo Neerhut

http://2017.stateofthemap.org/2017/using-image-detections-in-map-editing/

Transcript

1. SotM 2017 Deriving map data from street-level images
2. 15:15 | Mapillary recap - where are we now 15:20 | Computer vision developments 15:40 | Community stories 15:50 | Mapillary tools 16:00 | Map editing from street-level imagery 16:30 | Fin Agenda
3. Understand how street-level imagery can be applied in mapping Inspired by other community projects Aware of available tools Objectives Widen scope of map editing to include new/ ignored attributes Engage others in your locality to contribute street-level imagery New-timers Old-timers
4. 172,373,552 images, 3,141,352,533 meters
5. Hardware
6. State of the apps (iOS & Android) Action cam setup 360º cams - are they any good? Professional (Ladybug) Hardware
7. Mobile Apps
8. 360º cameras
9. Semi-professional
10. The pro’s
11. Images by device 42.7% 25.0% 15.7% 12.1% 3.9% 0.7% Android Action cam iOS Other Professional Windows
12. Computer vision
13. Privacy
14. Blurring License plate blurring Face blurring
15. Point clouds
16. The method we use to create a 3D environment from images Points are identified across images and combined with camera data such as GPS information and angle Image locations can be improved with this new information 3D models improve viewing experience and object detection/ positioning Structure from Motion
17. Semantic segmentation
18. 25,000 human annotated images Geographically diverse Diversity of cameras and conditions 100 object categories, 60 instance specific Training set for our deep learning models Free for research purposes Mapillary Vistas Dataset
19. Semantic Segmentation
21. AI Detections
22. Point cloud to map data Point cloud data with semantic classifications
23. Point cloud to map data Map data overlaid
24. Point cloud to map data Map data extracted
25. Community stories
26. Bangladesh - MapDhaka
27. Bike Ottawa
28. Bike Ottawa
29. Fukushima
30. Urban mobility
31. University of Washington Capstone Project
32. University of Washington
33. University of Washington
34. University of Washington
35. Mapillary tools
36. V2 has been deprecated GeoJSON default geographic format No more search in API - v2/search/im > v3/images Leaderboards built in Easier bounding boxes API V3
37. OSMAnd
38. #CompletetheMap
39. Map editing
40. 1. Get out our laptop and a mouse and open the editor of your choice. iD editor is recommended because it supports 360 degree imagery, something JOSM is not currently capable of. If you prefer JOSM, you may wish to open a Mapillary windows separately. Step 1
41. Step 1
42. 2. Choose an area you would like to map. • The area around Aizuwakamatsu City Cultural Centre should hopefully have a lot of imagery from our photo walk and the awesome Fukushima mapping community. • Amsterdam is a fun place to start because there are 800,000 high quality panoramic images to work with. Step 2a
43. 2. Choose an area you would like to map. • MapLesotho continues to improve. Help them add greater detail in Maseru and elsewhere using imagery captured from a range of devices. • Choose an area you’re more familiar with and take a look at what Mapillary images are available in the area. • You can do this in iD editor by clicking Map Data and enabling the Photo Overlay. • Get familiar with objects that Mapillary automatically detects in images and see if you can use them to edit. Access them via mapillary.com by selecting AI Detections up the top. Step 2b
44. 3. Work through the list of tags you can add. • Try editing some features you wouldn’t usually. • Use street-level imagery or detections as your primary data sources. • If you're uncertain about any terms, the OSM wiki is a great resource, as are the mappers sitting next to you. Step 3
45. 4. The internet cuts out at 16:30 sharp. Make sure you have added changeset comments and saved any edits you’re currently working on before then. Step 4
46. Buildings
47. Roads
48. Ammenities & nature
49. Parks
50. Remember to add your changes comments and save Thanks for editing
51. 170 million + images. Let’s make the most of them Different devices being used to contribute Technology allowing us to derive more map data Community stories & local inspiration Additional ways you can edit with street-level imagery What have we covered?
52. Map with us! ed@mapillary.com hello@mapillary.com @mapillary @mapillary