Find libraries/research on how to extract vegetation data

[kavya-oop]

[kavya-oop]

Using light absorption spectrum from satellite images to extract vegetation data: https://www.sciencedirect.com/science/article/pii/S2212017312006196
Article on how to do this in python: https://developers.planet.com/docs/planetschool/calculate-an-ndvi-in-python/

[jxchen21]

detectree: https://github.com/martibosch/detectree
Image Segmentation for classifying tree vs non-tree pixels. Probably isn't really directly applicable to this project, though, since it only finds the trees and doesn't estimate their height.

Open-Canopy: https://github.com/fajwel/Open-Canopy
This tool is intended for monitoring forest health and resources, but it does provide estimations of tree height from satellite imagery, which is essentially what we need. However, this is applied to large forests, obviously with many more trees than a typical city would probably have, so I'm not entirely sure if it would work in our desired application (it's also 360 GB apparently?)

TreeCountSegHeight: https://github.com/sizhuoli/TreeCountSegHeight (Paper: https://pmc.ncbi.nlm.nih.gov/articles/PMC10096914/)
Another tool for modeling tree height from satellite imagery. This one uses deep learning, and the paper on its development explicitly states that the model considers trees outside forests, suggesting that it may be useful for our desired application to trees in cities. Furthermore, the paper states that the model required only marginal adjustments when applied to data from significantly different environments (Finland vs Denmark), which suggests that it may be useful for our mapping utility if we hope to expand it in any significant manner moving forward.