Machine Learning in Landscape Architecture: A Comprehensive Review of Advancements, Applications, and Future Directions

Abstract

As a key AI technology, Machine learning (ML) has witnessed growing adoption in landscape architecture through advanced algorithms and computational techniques. Despite this progress, a critical gap persists in systematically analyzing ML’s transformative impacts and emerging opportunities through an application-driven lens. This study integrates bibliometric analysis with a systematic literature review to synthesize methodological advancements and domain-specific applications. After systematically reviewing the applications of machine learning in the field of landscape architecture, five categories were identified: simulation and prediction, layout generation, image post-processing, management and evaluation, and text analysis. Furthermore, this paper proposes strategic implementation frameworks for ML integration while establishing methodological benchmarks for intelligent design systems.