Geoint is live! Today is International GIS Day, and we’re taking the opportunity to soft-launch a service that, in just a few minutes, shows whether your idea can be realised using AI trained on laser data: ml.geoint.se

If you are bored, you can watch my Docent lecture today at 11:00: www.slu.se/kalender/202... AI-powered maps and other problems.

Vacation from my research role gives me more time for side-projects

I am looking for a phd student to join my research group in Umeå! www.slu.se/en/about-slu...

My landscape cubes now have a modelled groundwater level. Excited to see what my 3D-CNN can learn from this.

I’ve released a prototype of a tool that trains a machine learning model on open geographical data using your own training data. Upload a point shapefile where the first column contains the values you want to predict. It's free to experiment with, and no data is stored. geoint.konvergens.se

I have now added the color from IR imagery into my "landscapecubes". Now working to add groundwater under the DEM surface and then I need to figure out a way to encode logic in my 3D-CNN. For example geographical location or runoff data.

I am running some deep-learning models on voxel cubes of remote-sensing data instead of images to map soil moisture. It works but is quite demanding. So far, I have built a proof of concept in Tensorflow using 3D convolutions. Do you have any good advice for me?

When extracting streams from high-resolution digital elevation models, road culverts can be a pain. I explored an approach where a residual attention UNet was used to identify and breach road culverts, utilising ALS data and orthophotos. It works but produces a fair amount of false positives.