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From Palm Oil Plantation to Rainforest

Project details

Programme
Cluster RC18
Year 1

E-tourism infrastructure investigates the environmental implications of the palm oil trade. The project focuses on Sumatra, where the students develop an e-tourism infrastructure at the edge between the tropical forest and the oil palm plantation. The idea of an e-tourism infrastructure is to explore the possibility of an urban digital twin to raise awareness of environmental developments in remote areas.

Students

Southeast Asia's biodiverse rainforests are vital for global ecosystems. With 44% of the region's rainforest, Indonesia faces alarming deforestation, mainly driven by activities like oil palm plantations, logging, and coal mining.

Global Forest Cover and Deforestation

Southeast Asia's biodiverse rainforests are vital for global ecosystems. With 44% of the region's rainforest, Indonesia faces alarming deforestation, mainly driven by activities like oil palm plantations, logging, and coal mining.

Indonesia's palm oil industry is primarily in Sumatra, with 80% of production. A design site with good accessibility, existing factories, dense plantations, and abundant rainforest was chosen.

Palm Oil Industry Layout in Indonesia

Indonesia's palm oil industry is primarily in Sumatra, with 80% of production. A design site with good accessibility, existing factories, dense plantations, and abundant rainforest was chosen.

K-means clustering through geospatial data was used to identify rainforest damage caused by oil palm plantations. This resulted in a dataset comprising 288 split blocks for detailed site analysis.

K-Means Clustering and Geospatial Data

K-means clustering through geospatial data was used to identify rainforest damage caused by oil palm plantations. This resulted in a dataset comprising 288 split blocks for detailed site analysis.

The researchers employed K-means machine learning to analyse potential palm oil plantation encroachment factors. From Cluster 3, four 1 km x 1 km areas (Sites 79, 80, 91, and 92) were initially selected. 91 was ultimately chosen as the site.

Site Selection Based on K-Means Algorithm

The researchers employed K-means machine learning to analyse potential palm oil plantation encroachment factors. From Cluster 3, four 1 km x 1 km areas (Sites 79, 80, 91, and 92) were initially selected. 91 was ultimately chosen as the site.

The team integrated biodiversity integrity, forest carbon emissions, and forest landscape integrity data. They applied the HOGIMAGE algorithm to identify conflict zones between plantations and rainforests, serving as the design boundary space.

Space Boundary

The team integrated biodiversity integrity, forest carbon emissions, and forest landscape integrity data. They applied the HOGIMAGE algorithm to identify conflict zones between plantations and rainforests, serving as the design boundary space.

Students combined surface data (runoff, elevation, insolation, and NDVI) and used Wallacei, a multi-objective optimisation genetic algorithm, to identify key conflict zones between palm plantations and rainforests within the boundary space.

Identification of Borders

Students combined surface data (runoff, elevation, insolation, and NDVI) and used Wallacei, a multi-objective optimisation genetic algorithm, to identify key conflict zones between palm plantations and rainforests within the boundary space.

Global palm oil demand and value make boycotting unrealistic. The conflict arises from competing interests, blurring boundaries. The palm oil value chain was studied for solutions to prevent further encroachment.

Reprogramming the Site

Global palm oil demand and value make boycotting unrealistic. The conflict arises from competing interests, blurring boundaries. The palm oil value chain was studied for solutions to prevent further encroachment.

L-system growth algorithm was used for diverse spatial forms. The focus is on architectural richness, site suitability, and redefining palm oil production to promote rainforest conservation and consumer awareness.

Cluster Model Growth Based on L-system Algorithm

L-system growth algorithm was used for diverse spatial forms. The focus is on architectural richness, site suitability, and redefining palm oil production to promote rainforest conservation and consumer awareness.

The image plan depicts the creation of a new production space on the boundary, utilising L-system growth algorithms and incorporating realistic elements to establish an innovative design paradigm.

Refinement of Spatial Functions

The image plan depicts the creation of a new production space on the boundary, utilising L-system growth algorithms and incorporating realistic elements to establish an innovative design paradigm.

The architectural cluster maximises the boundary space, bridging the rainforest and oil palm plantation. It provides diverse online and offline spaces for people, connecting nature to urban agriculture.

Masterplan

The architectural cluster maximises the boundary space, bridging the rainforest and oil palm plantation. It provides diverse online and offline spaces for people, connecting nature to urban agriculture.

Bird's Eye View Render

People worldwide, from London to New York and Shanghai, can observe the rainforest, oil palm plantation, and architectural space in real-time using VR devices connected to drones within the venue, regardless of their location.

Linking of IoT and VR

People from as far away as London, New York and Shanghai can see what's happening inside the stunning rainforest, in the palm oil plantation, and in the interior space, anytime, anywhere, via virtual reality devices connected to drones.

Edge to the Tropical Forest

Boundary Spaces

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B-Pro Show 2023
26 September – 6 October
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