SEP 2018- MARCH 2019
POINT-CLOUD RADIOMETRIC DATA
TERRESTRIAL DATASETS OF LEVEE FAILURES FROM HURRICANE KATRINA BRIAN D. COLLINS, ROBERT KAYEN, DIANE MINASIAN, THOMAS REISS 15-09-2005 -
NEW ORLEANS, LOUISIANA, USA
30.037132’N, 29.968044’S, -90.124093’W, -89.932038’E
TERRESTRIAL & AIRBORNE LIDAR
MACHINE-LEARNING STATISTICAL MODEL
DIGITAL ANIMATION [RESOLUTION 8K]
Earth’s climate depends upon the solar climate that further depends upon the contingent climates of its galactic backdrop. Each interior climate depends upon an exterior climate, ad infinitum––generating coils. Cyclones from kuklōma ‘snake coils’ are energetic species of such coils. During cyclogenesis, thermal circulation induces pressure that drives warm air flowing poleward to rise and cold air flowing equator-ward to sink. The centre is a calm territory. It registers no remote-sensed echoes, with zero barometric pressure, termed as the eye.
Hurricane Katrina is a design-led research investigation. It presents two models of existential catastrophes centred around the terrestrial scanned point-clouds indexing the annihilation patterns of a tropical cyclone, retained across various surfaces in New Orleans as airborne particles:
(a) The cyclonopædic model presents a machine learning model of the point-clouds retrieved at the post-catastrophic New Orleans. It captures airborne signatures of an anthropogenic storm, absent from palaeoclimatic records. The anthropogenic cyclone is a synthetic force field constituting airborne particulates propelled through fossil burning into an agential swarm that terraforms territories into an ontological fog (mathematical indetermination). The statistical model classifies airborne particles in the point-clouds through probability distribution of unstructured data using semi-supervised machine learning algorithms. The classified data is represented through a unique radiometric visualisation technique, termed digital autoradiographs.
(b) The chronopaedic model presents a post-cinematographic temporal model of the post-disaster New Orleans. It is a synoptic examination into distinct hypothetical and empiric models of time that resolve the perception of catastrophic temporalities within a direction-dynamic model of time, alternate to the asymmetric model (à la thermodynamic entropy). It attempts to synthesise the infinitesimal temporalities of an existential catastrophe into a cinematic experience. Point-clouds are light speed numeric measurements, indexing topographic and airborne signatures. Such measurements commemorate catastrophes, where the disaster remains forever suspended––never dissipating, in a spatialised instance as a temporal autograph. The chronopædic model attempts to melt the temporal indices using machine learning (probabilistic roadmaps) to temporalise a timeless universe, catalysing dis-correlated experience in a post-cinematographic medium, a digital animation. The post-cinematic sequence, as such, provides perceptual and cinematic support to understand time in its unbound gradient and existential catastrophes as its local mixtures.