The project deals with the process of “map making with satellite imagery” and questions the producer of these two mediums and their ability for
limitation/interpretation of data and influence/transmission/acceptance of power.
It intended to show the paradox of “pattern recognition” in which “satellite
imagery are produced for maps” and “maps are produced for satellite imagery” in a
circulation with different transformational processes, where machine learning like “neural
networks” are taking over the main work for the huge amount of incoming datastream from
The map is not the territory ..... but another version of reality. (Korzybski 1933) It is as much a
reality of the world in a particular, codified form, that also has its own reality as an object with a
materiality, a temporality and as a thing with a meaning that is as evident as the visceral reality of
the world itself. Maps represent knowledge space, shown from a bird’s eye view. They visualize the
invisible by radically homogenizing something that is not homogenous by a medium consisting of
line, point and surface. Maps have the potential as an instrument of power for some intentions. They
substitute political and military power in a way that represents the state borders between territories
and they can repeat,legitimate and construct the differences of classes and social self-
As maps can not be realized without interpretation,generalization and simplification like the symbol
“tree” for a forest or a “red circle” for a city. It is the same for data which is used for “remote
Data is always translated such that they might be presented. The images, lists, graphs, and
maps that represent those data are all interpretations, and there is no such thing as neutral data. Data
is always collected for a specific purpose, by a combination of people, technology, money,
commerce, and government. The phase "data visualization," in that sense, is a bit redundant because
data is already a visualization.
Machine learning is used nowadays in the process of map making in geographic informational
systems in short “GIS” to observe the earth surface and detect abnormalities in real time and
identify changes in short timeframes for e.g. industrial trends, mine activities, gas & oil mining
area, urban trends, agriculture crop yield and so on. These systems are based on a model, where a
predefined knowledge is used to generate new knowledge which opens the topic of the “paradox of
pattern recognition”. To identify patterns means, the pattern itself must be predefined in some ways,
to be identified, for example to identify cloud formations for weather forecast means to classify
thousands of clouds.
Neural networks are based on this self feeding circuit systems where predefined knowledge as
“training data” is used in the process of decision making which creates new knowledge for training
data. As more work is getting computerized with the help of machine learning, these neural
networks have the power for deep society influences and economic decisions.
It can be ascertained that people started to build there own privacy protections against satellite
imagery or that farms should be “machine readable” to use the service in the field of remote
sensing. This creates the effect of restructuring the landscape, with the aim of being machine
readable or like McLuhan describes is with the way “we shape our tools and thereafter our tools
shape us”. With the wrong use of these technologies, it opens a door for the owner of the training-
data for power influence or to force social attitudes.
Therefore the accuracy of a neural network depends entirely on where the training-data is coming
from, how old and how big it is, as well as how much variation is involved in this dataset.
The machine showes a self-training recursive system:
1. where satellite imagery is shown on a screen, out of a random GPS coordinate, selected by the
2. A neural network (A) is analyzing the satellite imagery for different variables like infrastructure,
agriculture land use, buildings and so on.
3. A map is drawn by a pen plotter from the outcome of this network (A).
4. The drawn map is analyzed by a second neural network (B) to generate a new satellite imagery.
5. Neural network (A) is analyzing the new Satellite imagery again.
6. And a new map is drawn by a pen plotter.
7. and so on ....
Every new generated data is written directly into training-dataset were the network is getting the
The visitor will realize that the map and the satellite imagery is constantly changing over time
because of the different pattern recognitions of each networks. The end of the process is reached, if
the map and the satellite imagery are completely black or white or other
visitors selecting a new GPS coordinate, to start from there. The unpredictable decision making of
the network is an interesting part of the artwork, because the end is completely open. If it can start
with lots of streets and buildings and after 100 generations, it could be a green park or a mountain
terrain. It is difficult to understand why the network made this decision, but it gives the visitor the
possibility to have a prediction into a dystopia or utopian future. What will happen if our landscapes
are shaped by machines?
Consequently, the statement of the artwork could be how technology is shaping our landscapes with
the use of machine learning. It is also pointing out the fact that we are highly focused on the numbers and tent to see them as objective, unambiguous and interpretation free. In doing so, a
blindness arises against the processes that data generates and the assumption that numbers speak for
themselves. Not only the collection of data provides an interpretative scope, but also processing in
computing processes allows for further interpretations. Thus, numbers are viewed as the world
itself, forgetting that the numbers are only representing a model of the world. This model, however,
means that people adapt their behavior to the model's expectations and concentrate on delivering the