Fascinating work and inspiring application of the underlying DINOv3 image segmentation model!
The blog post and paper [1] describe a promising approach to solving related problems at previously impossible scale and quality: I am currently exploring methods to better represent seasonal land cover changes that would improve wind power generation forecasting and this paper provides a great starting point.
I hope DINOv3 can inspire more work like this - and I would encourage any curious mind to play with that model! I was amazed by its capability to distinguish between fine object details. For example, in a photo of a bicycle, the patch embeddings cleanly separated the background from the individual spokes of the wheel.
This is really cool, I wonder how old the satellite data they used is, it’s a bit unclear
This is an important question.
The tree outside of house is not 9 feet tall per. I have a 2 story house and it easily towers 10 feet higher than my house.
Additionally, there are several Royal Palms that are close to 50ft and they show as being only 15 feet.
Related: Just the other day I used USGS 3DEP LiDAR data + Claude Code to get a sense for the number of trees on my property. Diffing terrain map and canopy map gives tree elevation. It was a fun project to explore, primarily because I set CC loose and said "here is the bounding box of my property, pad it by 50 feet and then go absolutely nuts against government datasets gathering as much open data as you can" - it figured out the rest. Dug into soil maps, historical satellite imagery, and lidar data.
I think they were buying carbon offsets at some point and trying to validate that the countries and organizations that were selling the carbon offset were not cutting down those trees, effectively profiting twice.
Presumably the smart ones just sell their promise-not-to-cut-down-my-forest multiple times. Laundered through completely trustworthy NGOs, so nothing can actually be audited properly.
Fascinating work and inspiring application of the underlying DINOv3 image segmentation model!
The blog post and paper [1] describe a promising approach to solving related problems at previously impossible scale and quality: I am currently exploring methods to better represent seasonal land cover changes that would improve wind power generation forecasting and this paper provides a great starting point.
I hope DINOv3 can inspire more work like this - and I would encourage any curious mind to play with that model! I was amazed by its capability to distinguish between fine object details. For example, in a photo of a bicycle, the patch embeddings cleanly separated the background from the individual spokes of the wheel.
[1] https://arxiv.org/abs/2603.06382
This is really cool, I wonder how old the satellite data they used is, it’s a bit unclear
This is an important question.
The tree outside of house is not 9 feet tall per. I have a 2 story house and it easily towers 10 feet higher than my house.
Additionally, there are several Royal Palms that are close to 50ft and they show as being only 15 feet.
Related: Just the other day I used USGS 3DEP LiDAR data + Claude Code to get a sense for the number of trees on my property. Diffing terrain map and canopy map gives tree elevation. It was a fun project to explore, primarily because I set CC loose and said "here is the bounding box of my property, pad it by 50 feet and then go absolutely nuts against government datasets gathering as much open data as you can" - it figured out the rest. Dug into soil maps, historical satellite imagery, and lidar data.
Here are the visuals re: trees - https://i.imgur.com/R0W4q4O.png
why does meta map canopy heights?
I think they were buying carbon offsets at some point and trying to validate that the countries and organizations that were selling the carbon offset were not cutting down those trees, effectively profiting twice.
Presumably the smart ones just sell their promise-not-to-cut-down-my-forest multiple times. Laundered through completely trustworthy NGOs, so nothing can actually be audited properly.