This talk will cover our particular use case and the workflow we used. This talk will cover our particular use case and the workflow we used. At addresscloud we have used OpenStreetMap building data in our main application for the UK but we've recently found the need to get this data on a global basis. Our main goal was to extract the building outlines that have been captured in OSM and push these into our datastore to use in our applications. We didn't want to do this for a whole country as the amount of data and time to upload could be costly. We looked at a few different solutions but wanted something that was easy to set up and use so went with the python library OSMnx. OSMnx is a Python package to retrieve, model, analyse, and visualise street networks and other geometries from OpenStreetMap. In a single line of code, OSMnx lets you download spatial geometries, place boundaries, buildings footprints, or points of interest as a GeoDataFrame. This talk will look at the different options available in terms of downloading the data .i.e by city name, polygon, bounding box, or point/address plus distance. It will then show how we added geopandas into the workflow to export the data for use in our wider ecosystem. Finally we will look at the whole workflow and show how easily you can use OSMnx and geopandas in real world applications. Example code snippets will be shown for people to get an idea of how they can make use of OSMnx. Geospatial technologist who loves working with data and creating workflows with open source tools and scripting in python, nodejs and bash. The talk presents AROSICS and SpecHomo, two open-source and easy-to-use Python packages for automated and robust geometric and spectral fusion of multi-sensor, multi-spectral satellite images.

Road accidents constitute a major problem in our societies around the world. The World Health Organization(WHO) estimated that 1.25 million deaths were related to road traffic injuries in the year 2010. For the year 2016, the USA alone had recorded 37, 461 motor vehicle crash-related deaths, averaging around 102 people per day. In Europe, the statistics also indicate that each minute, there are 50 road deaths recorded in the year 2017. Can machine learning help us understand the causes and the factors that affect car crash severity?

This work is supported by Anaconda Inc., the Data Driven Discovery Initiative from the Moore Foundation, and NASA SBIR NNX16CG43P This work is a collaboration with Joris Van den Bossche. This blogpost builds on Joris's EuroSciPy talk (slides) on the same topic. You can also see Joris' blogpost on this same topic. Python's Geospatial stack is slow. Dask gives an additional 3-4x on a multi-core laptop.