Abstract

Norilsk is a city northwest of Krasnoyarsk Territory, Russia, on the east side of the lower Yenisei River. Norilsk is the famous nickel capital of Russia. Norilsk Nickel Industry is the world’s largest metal nickel company, which significantly impacts nickel prices worldwide. Norilsk is also one of the most polluted cities in Russia. Heavy metal waste and dust such as copper, nickel, cadmium, and lead are the most important sources of pollution. You can even see the red rivers on the site from the high-resolution satellite monitoring data. Their appearance is disturbing. Part of the pollution in the red waters comes from the illegal discharge of industrial heavy metal waste, and the other part comes from the diesel leakage caused by the accidental damage of the diesel storage tank of the local thermal power plant in 2020. With the rain and ebb and flow, toxic substances in the river such as nickel, iron, SO2 compounds, and other heavy metals are washed to the shore. This situation has brought a great burden to the land. The area within 4 kilometers of Norilsk is characterized by high levels of heavy metals, scarce trees, and disturbed organic mineralization. This area corresponds to the fifth level of environmental quality loss.

In heavily polluted and frigid land, the biological intelligence of fungi as primary producers has become an essential step in the area's metabolism. This project aims to design a set of synthetic landscapes capable of self-organization, self-repair, and self-regulation. A biological computer can automatically collect, store, and evaluate geographic information to form a programmed landscape. Make the landscape have the function of a biological computer and enhance the role of organisms in the metabolic system of the landscape.

Mycelium can absorb and store heavy metals and efficiently digest organic pollutants. Its mycorrhizal network topology can transmit and exchange environmental and soil information on a wide scale through electrical signals. And machine learning is an instrument of knowledge magnification that helps to perceive features, patterns, and correlations. Intelligent algorithms will simulate biological intelligence and cognitive capabilities as a new perspective to evaluate the site's value and transform its signals, patterns, and behaviors into new forms of synthetic landscapes.

Using the styleGAN algorithm, the dynamic transformation of industrial land and natural landscape topography such as wetlands, hills, and rivers is realized. The algorithm stores a wealth of geographic information and records the process of their changes. By reading and decoding the collected geographic information database, the GAN network can analyze and understand various ecological status quo to further state assessment, value assessment, and development forecast here.

Finally, as an ecological sensor, the biological computer exhibits a sensitive perception of the environment. The collaboration of biological computers and machine learning forms a synthetic landscape. It is highly resilient to changes in the surrounding environment and can effectively respond to the environment. The synthetic landscape system is observing and protecting our ecosystem, monitoring water bodies, pollution, and climate change, becoming an indispensable part of our ecosystem.

Geographic Information and Non-Human Intelligence Dataset

The styleGAN algorithm realizes the landscape dynamic transformation process. The algorithm stores a wealth of geographic features and records the operation of their changes. The StyleGAN algorithm can also collect and store geographic information data. As a large-scale ecological sensor, it aims to re-describe its natural characteristics by capturing the effects of these transformations in the catalogue of high-resolution mathematical drawings.
Their trans-scalar exchanges with humans and machine learning systems have considerably changed their nature.

The mycelium will be used as a biosensor and artificial intelligence algorithm to cooperate as a new perspective to evaluate the site's value。 and transform its signals, patterns, and behaviors into new forms of synthetic landscapes.

According to the synthetic landscape generation algorithm, data and images will be used to generate the terrain. By combining the local wetland features with the datascape, the algorithm will obtain synthetic landscapes with site characteristics. Color is the output of the biosensor, showing the change of site pollution and biological index over time.

Neo Interacting With Data

The model shows a synthetic landscape formed by the combination of datascape and geomorphic geographic features. And through AR technology, a new way of interacting with data can be realized.

At the same time, this new way of interaction produces a new perspective on ecological issues. And even a new way of interaction between humans and non-humans. Deanthropocentrism denies the duality between humans and nature.

The understanding of the connotation of the landscape can also be transformed from seeing the landscape as another non-human in duality to a decentralized network where humans and landscapes are regarded as one.

Bio-Sensor
RC16, G_02

Tutors:
Maria Kuptsova,
Vadim Smakhtin,
Artem Konevskikh

Group Members:
Zhiyue Gan, SN:19090242
Liangchen Zhu, SN:20085531
Ning Zhou, SN:19119612