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 > HOT@openstreetmap.org > https://lists.openstreetmap.org/listinfo/hot >
_______________________________________________ HOT mailing list HOT@openstreetmap.org https://lists.openstreetmap.org/listinfo/hot
