Awesome

Convolutional Neural Network (ConvNet) trained on open data from Land Information New Zealand (LINZ). Specifically, we train the ConvNet on aerial photography to detect building outlines.

Getting started

Quickstart

Launch Binder

Installation

git clone https://github.com/weiji14/nz_convnet.git
cd nz_convnet
conda env create -f environment.yml

Running the jupyter notebook

source activate nz_convnet
python -m ipykernel install --user  #to install conda env properly
jupyter kernelspec list --json      #see if kernel is installed
jupyter notebook

Where the data goes

Prediction

In near realtime!

source activate nz_convnet
python predict.py

#You can also set 2 integer parameters:
#  1st argument - output pixel size e.g. 256, 512, 1024 (default: 256)
#  2nd argument - prediction threshold e.g from least accurate 0% accept anything to 100% won't output much (default: 50)
python predict.py 512 50 

Live testing on imagery of Karori, Wellington.

To a raster geotiff (which you can vectorize to a polygon)

Once you clone the repository, open the jupyter notebook and follow the instructions to run 'Part 5 - Save Results'. You will need to have some geotiffs inside the data/test folder, and you may need to tweak the (img_height, img_width) parameter.

There might need to be some fiddling on your part to get this parameter right, so that the input RGB image will be tiled perfectly. The algorithm will create a prediction on each tile, and join in back together, so if it is not tiled perfectly due to the user setting, it will raise an error.

Below is a visualization in QGIS 3.0 of a sample test image and the predicted raster mask output

Mask was styled using singleband pseudocolor, linear interpolation, with the OrRd color ramp in equal-interval mode. Opacity set to 0% for values 0, 0.25 and 0.5, and 50% for values 0.75 and 1.0. GIF was recorded using Peek

More output examples

Sample outputs on cross validation dataset plotted with matplotlib inside the jupyter notebook environment. Left is input RGB image, Middle is ConvNet model output, Right is the Mask.

Data sources used to train the keras model

Using freely available data from LINZ Data Service. As there is a 3.5GB limit, we resort to using region crops using the 'Set a crop' tool on the top right. Not ideal but it ensures a little bit of reproducibility.

Training data

Images

• Manual tile selection selects tiles manually from the tiles table, e.g. here

Mask

• NZ Building Outlines (Pilot) in shapefile format.

Test data