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 -- OSM Wiki - https://wiki.openstreetmap.org/wiki/User:Bgirardot HOTOSM Member - https://hotosm.org/users/blake_girardot skype: jblakegirardot _______________________________________________ talk mailing list talk@openstreetmap.org https://lists.openstreetmap.org/listinfo/talk