Of course the really nice thing to do would be to compare the new scanned buildings with the existing o nes then tag all the ones with an overlap where the old was shall we say twice the size of the new.
It really could make a tremendous differ ence to data quality especially if HOT ran a few clean up projects in the worst areas. Cheerio John On 9 Aug 2018 11:32 am, "Dale Kunce" <dale.ku...@gmail.com> wrote: @Blake Girardot <blake.girar...@hotosm.org> Fantastic Job pulling together all these resources for folks. I'm really am excited for all the possibilities and time savings and better data the machines will give us. However, with emphasis, I'm also excited by the work that HOT is doing and has been doing to prepare for the machines. To answer a lot of the questions will inevitably come up. For instance: How will humans verify the results? What will a mapathon look like in 2 years? Do we still need to trace X features? How do machines fit in with OSM culture? I believe firmly that OSM is best when a human is the one that makes the final edit. I do see many workflows happening that will allow us to take advantage of the great work that our corporate partners and community are coming up with. Looking forward to learning and working on these issues together. On Thu, Aug 9, 2018 at 6:02 AM john whelan <jwhelan0...@gmail.com> wrote: > One does hope that a manual check will be part of the process? > > Thanks John > > On 9 August 2018 at 08:10, Blake Girardot <bgirar...@gmail.com> wrote: > >> Dear Friends, >> >> In case you missed it, Dale Kunce tweeted this out yesterday: >> >> The day of Machine Learning and OSM/Humanitarian mapping reckoning is >> getting closer. Very excited for the possibilities these new methods >> have for @hotosm @RedCross. Next frontier is making HOT and >> @TheMissingMaps more valuable than just a training dataset for the >> machines. >> >> https://twitter.com/calimapnerd/status/1027275305440829440 >> >> Toward that end, I have been watching and in some cases working with >> various ML tool chains over the past 2 years and really, not having a >> lot of luck with my level of skill and knowledge. I am a pretty >> advanced sysadmin, comfortable on the command line, but understanding >> the terminology and installations has been a bit beyond me. >> >> 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! >> >> ============================= >> >> 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 >> >> ============================= >> >> 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 >> >> =============================== >> >> 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 >> >> ================================= >> >> 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/ >> >> ===================================== >> >> 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 >> >> =========================================== >> >> 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 >> >> >> ====================================== >> >> Respectfully, >> Blake >> >> ---------------------------------------------------- >> Blake Girardot >> OSM Wiki - https://wiki.openstreetmap.org/wiki/User:Bgirardot >> HOTOSM Member - https://hotosm.org/users/blake_girardot >> skype: jblakegirardot >> >> _______________________________________________ >> HOT mailing list >> h...@openstreetmap.org >> https://lists.openstreetmap.org/listinfo/hot >> > > _______________________________________________ > HOT mailing list > h...@openstreetmap.org > https://lists.openstreetmap.org/listinfo/hot > -- sent from my mobile device Dale Kunce
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