Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Artificial intelligence (1)
- Computational design (1)
- Cultural topography (1)
- Deep learning (DL)-based semantic segmentation (1)
- Greenness measures (1)
-
- Landscape analysis and planning (1)
- Landscape architecture (1)
- Landscape design (1)
- Landscape planning (1)
- Machine learning (1)
- Mental models (1)
- Optimization (1)
- Professional culture (1)
- SLR (1)
- SLR practitioners (1)
- Sea-level rise (1)
- Survey (1)
- System dynamics (1)
- Technology (1)
- Urban green space (UGS) (1)
- Publication
- Publication Type
Articles 1 - 3 of 3
Full-Text Articles in Architecture
Artificial Intelligence In Landscape Architecture: A Survey Of Theory, Culture, And Practice, Phillip J. Fernberg
Artificial Intelligence In Landscape Architecture: A Survey Of Theory, Culture, And Practice, Phillip J. Fernberg
All Graduate Theses and Dissertations, Fall 2023 to Present
This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects' attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective …
A Review On Recent Deep Learning-Based Semantic Segmentation For Urban Greenness Measurement, Doo Hong Lee, Hye Yeon Park, Joonwhoan Lee
A Review On Recent Deep Learning-Based Semantic Segmentation For Urban Greenness Measurement, Doo Hong Lee, Hye Yeon Park, Joonwhoan Lee
Landscape Architecture and Environmental Planning Student Research
Accurate urban green space (UGS) measurement has become crucial for landscape analysis. This paper reviews the recent technological breakthroughs in deep learning (DL)-based semantic segmentation, emphasizing efficient landscape analysis, and integrating greenness measurements. It explores quantitative greenness measures applied through semantic segmentation, categorized into the plan view- and the perspective view-based methods, like the Land Class Classification (LCC) with green objects and the Green View Index (GVI) based on street photographs. This review navigates from traditional to modern DL-based semantic segmentation models, illuminating the evolution of the urban greenness measures and segmentation tasks for advanced landscape analysis. It also presents …
Slr Practitioner Needs, Daniella Hirschfeld, Kelli Archie, Emilio Mateo, James C. Arnott, Julie A. Vano
Slr Practitioner Needs, Daniella Hirschfeld, Kelli Archie, Emilio Mateo, James C. Arnott, Julie A. Vano
Browse all Datasets
As sea levels continue to rise, practitioners at the local and regional scale are under increased pressure to reduce risks to people and property posed by the threats of sea-level rise (SLR) and associated impacts. To achieve this, a transdisciplinary approach that integrates data-driven research with local knowledge and community engagement is necessary. As such, it is imperative the science community understands the needs of practitioners. However, there has been little qualitative assessment of adaptation practice in coastal areas, especially with a focus on the needs of practitioners in making use of current SLR science. Our mixed-methods approach began with …