Methods
The scene extent of the image data provides a generally comprehensive view of lower- to mid- Manhattan, and it is possible to extract the average light intensity of a building at night from these data. However, extracted information from the scene, such as ALAN intensity, is only useful for interpretability purposes if it can be mapped to building location or unique identifiers. For this reason, the imaging and photogrammetry portion of the project can be divided into three main parts: 1) projecting portions of the image onto individual building identifiers, 2) calculating average recorded brightness over those respective portions of the image corresponding to buildings, and 3) scaling the average brightness over each of these portions of the image, based on the projected building's surface area, to derive an estimate of the amount of light that particular building is emitting into the sky.
For 1), we followed the method outlined in (Schenck, 2005), using NYC 3D building model data as our source of information about the built environment, and including an approximation step to determine exacly how the camera was oriented in the 3D environment. Upon completing projection and calculating average brightness recorded at each building, we estimate overall light levels emitted from the building by the following formula: 
 
\( B = B_{recorded} \cdot n\_floors \cdot perimeter\)
Where \(B\) is overall brightness, \(B_{recorded}\) is the average brightness over all pixels recorded by the camera for that building, \(n\_floors\) is the number of floors that building has (as reported by the NYC MapPLUTO dataset), and \(perimeter\) is the perimeter of the building's footprint (again derived from the NYC MapPLUTO dataset).