28-30 July 2018, Milano: State of the Map 2018

[Daniel-Mietchen]

https://wiki.openstreetmap.org/wiki/State_of_the_Map_2018

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Hashtag: #sotm

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• https://openmaptiles.org/
• https://openmaptiles.com/
• https://twitter.com/klokantech

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• http://www.telenav.com/
• http://codalab.org/

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• https://github.com/mapbox/robosat

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In session Can we validate every change on OSM?, lots of good examples of the kinds of mistakes, errors, vandalism, spam etc. that occur and what can be done to detect, flag and address them by machines, humans or combinations thereof.

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• https://en.wikipedia.org/wiki/OpenStreetCam

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• https://crowd2map.wordpress.com/

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Here's the Twitter query I am using to follow:

• https://twitter.com/search?f=tweets&vertical=default&q=stateofthemap%20OR%20sotm2018

[Daniel-Mietchen]

Notes from the "OSM and Wikimedia" session:

• https://etherpad.wikimedia.org/p/State-of-the-Map-2018-OSM-and-Wikimedia

[Daniel-Mietchen]

• http://weeklyosm.com/
• http://osmand.net/

[Daniel-Mietchen]

Twitter search for Wikidata-based queries relating to maps:

• https://twitter.com/search?f=images&vertical=default&q=(map%20OR%20maps%20OR%20place%20OR%20places%20OR%20country%20OR%20countries)%20AND%20(query%20OR%20queries)%20AND%20wikidata (posting this here since Wikimedia sites are blocking Twitter searches for spam reasons)

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• https://lists.openstreetmap.org/listinfo

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• https://github.com/osm-science

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"Open Science Portal"

• perhaps http://openscience.ansto.gov.au/ ?

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• https://wiki.openstreetmap.org/wiki/Research
• https://wiki.openstreetmap.org/wiki/Research/Ideas

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• Left a note at https://github.com/osm-science/organisational/issues/5 .

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Note from the train journey to OSM:

• I saw a fire in Basel and took some pictures from the train.
• Summary: https://polizei.news/2018/07/28/basel-bs-alte-eisenbahnschwellen-geraten-in-brand-zeugenaufruf/
• More tweets: https://twitter.com/search?f=tweets&vertical=default&q=basel%20AND%20%28fire%20OR%20feuer%20OR%20feuerwehr%20OR%20brand%20OR%20rauch%29

[Daniel-Mietchen]

Overview of a subset of the unconference-style sessions: https://twitter.com/fofliajinal/status/1023209922983538688

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For recordings of the sessions, see https://www.youtube.com/channel/UCLqJsr_5PfdvDFbgv1qp2aQ

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Attendee stats: https://twitter.com/ldodds/status/1023522435159810048

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• http://cityzenapp.co/

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• https://www.ecosia.org/search?q=autonomous+driving+dataset

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• https://gist.github.com/LibrErli/a416a3f0d3400e3add3431f8b4a897a2 (on parsing Twitter lists, might be used for preparing data for Wikidata)

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• https://github.com/vvoovv/blender-osm

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• spontaneous Wikimedia & OSM meetup after the lightning talk 4 session - some more notes in the Etherpad listed above.

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• https://wiki.openstreetmap.org/wiki/WIWOSM , can be seen in action e.g. at https://de.wikipedia.org/wiki/Eiffelturm

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• https://wiki.openstreetmap.org/wiki/Blender

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World Cleanup Day

• https://www.letsdoitworld.org/

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OSM used to map waste online, and Let's Do It World has local volunteers to actually clean things up.

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My two lightning talks sit at

• https://meta.m.wikimedia.org/wiki/Wikimedians_for_Disaster_Response/Talks/State_of_the_Map_2018
• https://m.wikidata.org/wiki/Wikidata:WikiProject_Wikidata_for_research/State_of_the_Map_2018

I am embedding screenshots here. The latter has also been recorded on video.

[Daniel-Mietchen]

• https://twitter.com/DScientificly/status/1023873330124926976 Open Data Lab planned in Milano
• https://twitter.com/kode_solutions/status/1023907203944730627 data science and OSM

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Summary blog post by the organizers: https://blog.openstreetmap.org/2018/08/01/sotm-milan-thanks/

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https://twitter.com/Lebowskiana/status/1023854031939268608 crisis mapping app

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https://twitter.com/bhecht/status/1024617042995892224 Project on peer production of structured data, with Wikidata and OpenStreetMap serving as the main case studies. https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815507&HistoricalAwards=false

[Daniel-Mietchen]

http://sophox.org/ is a Wikibase for OSM, run by Yuri Astrakhan. Very promising.

[Daniel-Mietchen]

Just went through the list of those who participated in the wiki meetups and left their contact details in the etherpad above. Left the following messages:

• https://en.wikipedia.org/w/index.php?title=User_talk:Seav&diff=prev&oldid=853479702&diffmode=source
• https://en.wikipedia.org/w/index.php?title=User_talk:Yurik&diff=prev&oldid=853471344&diffmode=source
• https://en.wikipedia.org/w/index.php?title=User_talk:Mxn&diff=prev&oldid=853469992&diffmode=source
• https://en.wikipedia.org/w/index.php?title=User_talk:Kateregga1&type=revision&diff=853478299&oldid=793573660&diffmode=visual
• https://de.wikipedia.org/w/index.php?title=Benutzer_Diskussion:Simon04&diff=prev&oldid=179732139

In the process, I also discovered https://commons.wikimedia.org/wiki/Category:Commons_users_who_contribute_to_OpenStreetMap and their equivalents on other wikis, as per https://www.wikidata.org/wiki/Q7661017 .

[Daniel-Mietchen]

OSM-based routing service: http://routing.openstreetmap.de/?z=7&center=50.892639%2C11.832275&loc=50.889174%2C11.832275&loc=51.930718%2C7.591553&hl=en&alt=0&srv=1

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https://github.com/GIScience/OSM-realtime-update http://realtimeosm.openrouteservice.org/

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https://www.maplesotho.com/

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There was an interesting session Investigating the OSM mapping process after disasters: OsmEventAnalyst and its application for the 2016 Italian earthquakes, with slides at http://lucadelu.org/presentazioni/reveal/2018_sotm.html#/ .

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Analysis tool from the session above: https://github.com/osmItalia/OsmEventAnalyst .

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Link to the other Wikimedia talk in lightning talk session 4: https://2018.stateofthemap.org/2018/L031-OpenStreetMap_and_Wikimedia/

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OSM working groups: https://wiki.osmfoundation.org/wiki/Working_Groups . Includes a Data Working Group, focused on

• the resolution of issues in copyright violation, disputes, vandalism, and bots, beyond the normal means of the community;
  • helping to set policy on data;
  • detecting and stopping vandalism and imports that do not comply with guidelines.

[Daniel-Mietchen]

OpenStreetMap data used by EU Commission:

• https://twitter.com/laura_mugeha/status/1023704962872893440
• background: https://2018.stateofthemap.org/2018/T134-OSM_and_the_European_agenda/

[Daniel-Mietchen]

More on the session about the disaster response after Italian earthquakes:

• https://re.public.polimi.it/handle/11311/1054233#.W17Se5_OM0M
• paper at https://re.public.polimi.it/retrieve/handle/11311/1054233/286936/paper_GEAM_OSM_emergenze.pdf
  • reminds me of some similar work by Taha Yasseri, e.g. on plane crashes (sample coverage)

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Have pinged the Italian team: https://twitter.com/EvoMRI/status/1025953007513612288

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Some more on machine learning, from a message by Blake Girardot to the OSM mailing list

So if anyone is like me and sees all of these great tool chains and would like to learn how to use them with your peers learning along with you and hopefully some experts as well, I created a dedicated

mlearning-basic channel on the OSM-US slack (

https://osmus-slack.herokuapp.com/ )

OSM-US runs a lovely, informative, lively, international slack with many channels and everyone is welcome!

The #mlearning-basic channel is for the absolute beginner basics, how to install and use the existing and emerging tools chains and OSM/OAM data to generate usable vector data from Machine Learning quickly.

You are all invited to join, but it is very basic. Hopefully some of the ML experts from the projects below will be in there to hand hold us newbies through actually making use of what we are seeing more and more everyday. Excellent tool chains exist, world changing tool chains, now we just need to get them into the hands of the people who need and want to use them everyday :)

Everyone is welcome and encouraged to join, it is intended to be kind of a "learn-a-long". Our first project, my first project, is building on the Anthropocene Labs work and doing the same area using MapBoxes RobotSat tool chain using Danial's and Maning's posts as a guide.

For reference please see this incredible work the community has shared in the past months, much like humanitarian mapping in general, the projects you see below will start changing the world over the next 12 months. Apologies if I missed any other OSM ML public projects, please reply and let us all know!

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Anthropocene Labs @anthropoco

Humanitarian #drone imgs of #Rohingya refugee camps + pretrained

model finetuned w @hotosm data. Not perfect maps but fast, small data need, works w diff imgs. Thx @UNmigration @OpenAerialMap @geonanayi @WeRobotics 4 #opendata & ideas! #cloudnative #geospatial

deeplearning

https://twitter.com/anthropoco/status/1027268421442883584

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This post follows Daniel’s guide for detecting buildings in drone imagery in the Philippines. The goal of this exercise is for me to understand the basics of the pipeline and find ways to use the tool in identifying remote settlements from high resolution imagery (i.e drones). I’m not aiming for pixel-perfect detection (i.e precise geometry of the building). My main question is whether it can help direct a human mapper focus on specific areas in the imagery to map in OpenStreetMap.

https://www.openstreetmap.org/user/maning/diary/44462

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Recently at Mapbox we open sourced RoboSat our end-to-end pipeline for feature extraction from aerial and satellite imagery. In the following I will show you how to run the full RoboSat pipeline on your own imagery using drone imagery from the OpenAerialMap project in the area of Tanzania as an example.

https://www.openstreetmap.org/user/daniel-j-h/diary/44321

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Skynet is our machine learning platform. It quickly scans vast archives of satellite and drone imagery and delivers usable insights to decisionmakers. Our partners use Skynet to reliably extract roads and buildings from images that NASA, ESA, and private satellites and drones record daily. The tool is remarkably versatile. We are experimenting with using Skynet to detect electricity infrastructure, locate schools, and evaluate crop performance.

https://developmentseed.org/projects/skynet/

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Deep learning techniques, esp. Convolutional Neural Networks (CNNs), are now widely studied for predictive analytics with remote sensing images, which can be further applied in different domains for ground object detection, population mapping, etc. These methods usually train predicting models with the supervision of a large set of training examples.

https://www.geog.uni-heidelberg.de/gis/deepvgi_en.html

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OSMDeepOD - OpenStreetMap (OSM) and Machine Learning (Deep Learning) based Object Detection from Aerial Imagery (Formerly also known as "OSM-Crosswalk-Detection"). http://www.hsr.ch/geometalab

https://github.com/geometalab/OSMDeepOD

[Daniel-Mietchen]

Meanwhile, there is https://www.wikidata.org/wiki/Wikidata:WikiProject_Humanitarian_Wikidata .

[Daniel-Mietchen]

Interesting tool: https://rene78.github.io/Wikidata-Extractor/ allows to extract Wikidata IDs from OSM.