Album  III
Plate V
Ruhrgebiet
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. . . . . . . . . . fig 2-1, 2-2, 3-1, 4-1, 4-2, 5-3, 5-4, 6-2, 6-4
S P E C U L A T I V E
V I S U A L I S A T I O N

R E T R I E V E D
fig 2-1 . . . . 11-12-2018   

fig 2-2 . . . .11-12-2018
fig 3-1 . . . .09-06-2018
fig 4-1 . . . . 11-12-2018   
fig 4-2 . . . .11-12-2018
fig 5-3 . . . . 11-12-2018   
fig 5-4 . . . .11-12-2018
fig 6-2 . . . .09-06-2018
fig 6-4 . . . .09-06-2018

. . . . . . . . . . Applied Algorithm: Deep Neural Network | Style Transfer Gatys, L., Ecker, A., &  Bethge, M. (2016). Neural network based on 19-layer VGG network Simonyan, A., Zisserman, K. Bethge Computer Vision Lab deepart.io.
.
. . . . . . . . . Input Data: Natural Substance A III — 9 (Coal) &  Anthropogenic Substance A III — 12



. . . . . . . . . . fig 2-4, 3-2, 3-3, 5-5, 6-1, 6-3
I M A G E    C L A S S I F I E R    D A T A 
R E T R I E V E D
fig 2-4 . . . .  03-14-2021
fig 5-5 . . . .  03-14-2021
. . . . . . . . . . Model: Google Cloud’s Vision API • vision.googleapis.com
R E T R I E V E D
fig 3-2 . . . .  03-14-2021
fig 3-3 . . . .  03-14-2021
. . . . . . . . . . Microsoft Azure Cognitive Services • Computer Vision • Image Recognition • azure.microsoft.com
R E T R I E V E D
fig 6-1 . . . .  02-05-2020
fig 6-3 . . . .  02-05-2020
. . . . . . . . . . Model: Clarifi, General Visual Classifier • Version aa7f35c01e0642fda5 • created Mar 6, 2018 • portal.clarifai.com


. . . . . . . . . . fig 4-3, 5-2
N A T U R A L   S U B S T A N C E
C O L L E C T E D   
fig 4-3 . . . .  08-07-2018

fig 5-2 . . . .  08-07-2018
. . . . . . . . . .  Coal • Ruhrgebiet
51° 25’ 21.918” N • 7° 10’ 13.35” E



. . . . . . . . . . fig 2-3, 5-1
A N T H R O P O G E N I C   
S U B S T A N C E

C O L L E C T E D   
fig 2-3 . . . . 08-07-2018

fig 5-1 . . . . 08-07-2018
. . . . . . . . . . Ruhrgebiet
51° 24’ 21.9” N • 7° 10’ 13.35” E



. . . . . . . . . . fig X
P H O T O G R A P H I C   
D O C U M E N T A T I O N

C A P T U R E D   
. . . . . . . . . . Ruhrgebiet




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