GeoDeep's AI Detection on Maxar's Satellite Imagery

Hey, I wrote GeoDeep, what a cool writeup! Glad someone tried to run these on satellite imagery. I did most of my testing on drone derived orthophotos, so was curious to see how it would perform with lower resolution images. Some models like the car model seem like they did poorly. I suspect it's probably because the ground sampling distance (GSD) was in the 35-56 cm/px range, whereas the car model was trained for 10 cm/px. GeoDeep can downscale the input to match the model's input resolution, but can't (currently) upscale it.

Hey, love this write up, thank you.<p>Id love to read a little more about why onnx was chosen specifically, and what it brings to the game.<p>There is currently a gap in open and decent models which can be applied to high resolution orthographic imagery, specifically with regards to collection of additional community resilience artefacts such as solar panels and pools. Theres plenty of vendors who want to sell such data, but its so far out of reach of so many worldwide that an open source set of models would go a hell of a long way.<p>Thanks also for including your PC specs.

Cool post. I&#x27;d be interested in seeing models likes this deployed to the satellites themselves.<p>Typically, data gathered from satellites needs to wait for the satellite to do a pass over a dedicated ground station before it can be processed, which is probably somewhere in the US. If you move the processing from the ground station to the satellite, then you 1. Don&#x27;t have to transmit as much data, 2. Can transmit actionable intelligence much faster. It can be upwards of 90 minutes before a satellite passes over it&#x27;s ground station. If you could get that down to a few seconds, I could see some serious applications in disaster response.

I find the blog funny for its disclaimer for the workstation specs.<p>&gt; There is a heatsink on the SSD to help keep its temperature down.<p>Good to know!

Super interesting article and results shared by the Author, as always!<p>Quite surprised by the overall processing results especially on general detection of features.