data[c]tombs

Non Architecture | Remote Sensing | Computational Design | 2018

This project presents a machine learning algorithmic design that parasites on the photonic numogram of St Cecilia Concert Hall at The University of Edinburgh, retrieved via Light Detection and Ranging [LIDAR]. The data is sampled and encapsulated in an n-dimensional voxel space, and then used as a seeding scaffold for emitting a high resolution discrete gaseous fluid dynamic simulation. In which the agents are constrained by a custom made algorithm that directs the propagation and behaviour of each element per frame, based on topologic information and driven by the vectorial intent. The original points of the LiDAR scan are ray-traced into high-density calcified spheres, and the simulated data is enmeshed into vermicular discrete entropic entities. The result is a propagation of a data contagion that infects the point cloud and starts impelling itself through the intricate surfaces scan points of St-Cecilia. In return turning every point of the point cloud into discrete linear elements and thus giving the chalk-out-original-data-cliffs of LiDAR, a series of high-resolution scorch-punctures and intestinally entropic iridium body parts.