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Day3-1515-Using image detections in map editing

September 01, 2017

Day3-1515-Using image detections in map editing


September 01, 2017

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  1. 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
  2. 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
  3. State of the apps (iOS & Android) Action cam setup

    360º cams - are they any good? Professional (Ladybug) Hardware
  4. Images by device 42.7% 25.0% 15.7% 12.1% 3.9% 0.7% Android

    Action cam iOS Other Professional Windows
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. 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
  13. 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?