Digital twins for green roofs and facade inspection using drones and multi-modal foundation models

Green roofs and facades are a nature-based way to improve the climate of buildings. However, they also add additional maintenance effort for keeping the plants healthy. We propose a novel automated digital twin approach to monitor the health of the plants using a combination of drones with Foundatio...

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Main Authors: Krause Nils Soeren, Ploennigs Joern
Format: Article
Language:English
Published: EDP Sciences 2025-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05001.pdf
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author Krause Nils Soeren
Ploennigs Joern
author_facet Krause Nils Soeren
Ploennigs Joern
author_sort Krause Nils Soeren
collection DOAJ
description Green roofs and facades are a nature-based way to improve the climate of buildings. However, they also add additional maintenance effort for keeping the plants healthy. We propose a novel automated digital twin approach to monitor the health of the plants using a combination of drones with Foundation Models to monitor and analyze the health of the plants. The process consists of three main components, a drone system for recording real-time data in an automated manner for different heights, a digital twin for storing and controlling the data, and a Multi-modal Foundation model (MMFM) for diagnosis of health issues and generating recommendations for solving the issues. This allows us to automatically identify and mitigate health issues of the plants and improve the maintenance of the miniature ecosystems of green roofs and facades to address climate change risk and the shortage of skilled workers.
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series E3S Web of Conferences
spelling doaj-art-0fbcfaa923ff4955ba1dfc5a4a556a792025-02-05T10:49:33ZengEDP SciencesE3S Web of Conferences2267-12422025-01-016080500110.1051/e3sconf/202560805001e3sconf_eenviro2024_05001Digital twins for green roofs and facade inspection using drones and multi-modal foundation modelsKrause Nils Soeren0Ploennigs Joern1AI for Sustainable Construction, University of RostockAI for Sustainable Construction, University of RostockGreen roofs and facades are a nature-based way to improve the climate of buildings. However, they also add additional maintenance effort for keeping the plants healthy. We propose a novel automated digital twin approach to monitor the health of the plants using a combination of drones with Foundation Models to monitor and analyze the health of the plants. The process consists of three main components, a drone system for recording real-time data in an automated manner for different heights, a digital twin for storing and controlling the data, and a Multi-modal Foundation model (MMFM) for diagnosis of health issues and generating recommendations for solving the issues. This allows us to automatically identify and mitigate health issues of the plants and improve the maintenance of the miniature ecosystems of green roofs and facades to address climate change risk and the shortage of skilled workers.https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05001.pdf
spellingShingle Krause Nils Soeren
Ploennigs Joern
Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
E3S Web of Conferences
title Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
title_full Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
title_fullStr Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
title_full_unstemmed Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
title_short Digital twins for green roofs and facade inspection using drones and multi-modal foundation models
title_sort digital twins for green roofs and facade inspection using drones and multi modal foundation models
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2025/08/e3sconf_eenviro2024_05001.pdf
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