Theoretical Framework and Practical Pathways for AI Empowered Campus Environment Optimization Design in Private Schools

DOI

10.2991/978-94-6239-783-5_26How to use a DOI?

Keywords

Artificial Intelligence; Campus Environment Optimization Design; Semantic Segmentation; Landscape Design; Smart Campus

Abstract

Campus environments at private schools tend toward formulaic design where aesthetics stay uniform and decisions lean on experience rather than data. This paper proposes a three layer model of perception, diagnosis, and decision, validated through fieldwork at Kunming Vocational University of Science and Technology. The perception layer feeds street view images into a semantic segmentation model to extract quantifiable landscape indicators, supplemented by behavioral data from existing campus systems. The diagnosis layer scores sites along four dimensions: green coverage rate, spatial morphology, cultural visibility, and functional facilities. The decision layer converts scores into concrete renovation proposals through rule matching and generative design. Case validation shows the framework operates under typical private institution resource constraints, moving landscape optimization from experiential judgment toward data driven decision making.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Zhaoxin Wang
AU  - Qingqing Yang
AU  - Yuyang Wang
PY  - 2026
DA  - 2026/09/30
TI  - Theoretical Framework and Practical Pathways for AI Empowered Campus Environment Optimization Design in Private Schools
BT  - Proceedings of the 2026 2nd International Conference on Resilient City and Safety Engineering (ICRCSE 2026)
PB  - Atlantis Press
SP  - 248
EP  - 255
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6239-783-5_26
DO  - 10.2991/978-94-6239-783-5_26
ID  - Wang2026
ER  -