Detect

BA & MA in Physics
PhD in Computational Neuroscience
now at Department of Physiology, Uni Bern; modeling of plasticity & learning in brains
so far: consumer of geo data
why this hackathon?
- learn something
- build something
- open data is awesome!

twitter: @_jakobj

github: https://github.com/jakobj/

About me

Locate crosswalks in Bern

https://www.swisstopo.admin.ch/en/geodata/images/ortho/swissimage10.html

with the help of aerial photographs

(SWISSIMAGE 10cm)

Supervised machine learning

-> need (lots of) examples!

(46.94926, 7.45258, ...)

(46.94984, 7.45432, ...)

(46.94836, 7.45973, ...)

(Semi)manual annotation of full-scale images

-> extraction of ~1000 positive examples (50x50px)

(negative samples were generated automatically & reviewed)

Small convolutional neural network

(2x [Conv2d + ReLU + MaxPool2d] + 1x FC)

Output: probability that input image patch contains crosswalk

~20k parameters, ~40min training time, ~150 loc

positions of crosswalks within the photograph in pixels

coordinates of each photograph

(+ merging regions of interest)

~2500 ROIs imported into map.geo.admin.ch

time management
- pick a project that's realistic, a week is short!
- making some choice may be better than making the best
applied machine learning
- consider the difficulty of the classification task; you may not always need a model trained on ImageNet
- poor performance is likely an issue of the data, rather than the model; check (and retrain) missclassifications!
- data augmentation FTW!
geo data: coordinate transformations are a pain

simple supervised-ML methods are suitable for detecting regions of interest in aerial photographs
(likely) every single design choice in this project can be improved
give it a try! code & some doc (MIT license): https://github.com/jakobj/xdetect

Thanks to everyone involved for making this possible!

It was awesome!