an urban catalogue(仮)
– identifying a building’s ‘character’
So now we know about how machine learning can change every industry that it touches. What can it do for architecture? Can it learn relationships between buildings? What does doing that actually do for us?
Well, let’s consider this: buildings are rows in a dataset, and their corresponding features are the columns describing them.
What the dataset can look like
These columns are descriptions of each building, ranging from:
- number of floors, basements
- number of apartments and offices (this small study was previously done based on mixed use developments)
- gross floor area
- number of parkings
- floor area ratio
down to perhaps even more subtle features such as:
- percentage of floor that receives direct sunlight
- reflectivity of facade
- contribution to heat island effect
- building energy consumption footprint
- weather attributes where the building is located
- other correlated data that comes from having the building sitting in the same place, exposed to changing weather, land conditions, and socioeconomic trends.
The possibilities for hand-describing features are in fact quite endless, and similar in character to the features defined to tag songs (ala The Music Genome Project).
Right now it seems one way to make sense of a building is to hand-label these features based on traits that are familiar to architects (used by architects to describe a building).
Perhaps in time, with a sufficient size of the dataset to zoom out and get a feel for ‘what describes a building’, we can maybe move on up to trying different methods of gleaning features from raw data (photographs or the building, or raw data from sensors in the future of buildings fully connected with the cloud)
What can the Dataset Do
Initially, it can be clustered. Many clustering algorithms can be applied to a clean dataset to provide insights that come from merely referencing related buildings.
e.g. what sort of facades work well in this climate? oh here are some similar buildings from similar climates that use the same window detail and product supplier..
e.g. what are the most similar buildings in terms of (architect selects a few features out of the many)
They can also be trained in a regression model (or a simple neural network) to learn what the best features are for a new site, based on everything the dataset knows on all sites.
There are way more. They are an extremely attractive proposition. They are mouth-wateringly low hanging fruit.