Simon Weckert
Work About




Maps from Space


Installation, 2017


Introduction
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 satellites.

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- understandings. 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 sensing”. 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 visitor.
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 knowledge from. 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 right numbers.












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A project by Simon Weckert
UdK Berlin, 2017