It doesn’t matter which part of the world you are living now, very diverse tree species are planted around the urban area we live. Trees in the urban areas have many functions, for example, trees provide habitats for wildlife, clean air and water, provide significant health and social benefits, and also improve property value too. Wake up in a beautiful morning that birds are singing outside your apartment because you have many beautiful trees grow outside of your space. How awesome is that!
However, tree planting, survey, and species identification require an enormous amount of work that literally took generations and years of inputs and care. What if we could identify tree species from satellite imagery, how much faster and how well we could get tree species identified and also tell their geolocations as well.
A city has its own tree selection and planting plan, but homeowners have their own tree preference, which the identification work a bit complicated, though.
(Photo from Google Earth Pro June 2010 in Chicago area)
It’s hard to tell now how many tree species are planted in above image. But we could (zoom in and) tell these trees actually have a slightly different shape of tree crown, color, and texture. From here I only need to have a valid dataset basically tell me what tree I am looking at now, which is a tree survey and trees geolocation records from the city. I will be able to teach a computer to select similar features for the species I’m interested in identifying.
These are Green Ash trees (I marked as green dots here).
These are Littleleaf Linden, they are marked as orange dots.
Let me run a Caffe deep learning model (it’s one of the neural networks and also known as artificial intelligence model) for an image classification on these two species, and see if the computer could separate these two species from my training and test datasets.
Great news that the model could actually tell the differences between these two species. I run the model for 300 epochs (runs) from learning rate 0.01 to 0.001 on about 200 images for two species. 75% went to train the model and 25% for testing. The result is not bad that we have around 90% of accuracy (orange line) and less than 0.1 loss on the training dataset.
I threw a random test image to the model (a green ash screenshot in this case) and it tells the result.
I will be working on identifying other 20 trees species and their geolocations next time.
Let’s get some answer what trees are planted in Chicago area and how it related to the property value (an interesting question to ask), and also what ecological benefits and functions these tree are providing (leave this to urban ecologist if my cloud computer could identify the species)? Check my future work ;-).