Research Article

Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends

Number: 13 August 24, 2026
TR EN

Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends

Abstract

Background and Aims This study examines the role of machine learning (ML) in scientific production within landscape architecture, with a particular focus on its integration with Geographic Information Systems (GIS). Despite the growing adoption of data-driven approaches, the scope and nature of GIS–ML integration remain insufficiently explored. Accordingly, the study aims to identify current trends, reveal methodological patterns, and determine research gaps at this intersection. Methods A two-stage analytical framework was adopted. In the first stage, a bibliometric analysis was conducted on ML-related publications indexed in the Web of Science (WoS) database to examine general research trends. In the second stage, studies involving GIS-integrated ML applications were systematically evaluated through content analysis based on predefined coding criteria. Results The findings indicate that ML plays a central methodological role in most studies and is predominantly used for prediction and modeling purposes. In contrast, GIS integration remains limited and is mainly associated with planning and decision-support applications. The dominance of prediction and spatial distribution maps suggests that GIS–ML integration is largely approached from a predictive perspective. Conclusion The results highlight a significant research gap in the comprehensive integration of GIS and ML, particularly in landscape design. The study also provides a conceptual and methodological framework for future research and emphasizes the transformative potential of AI-driven approaches in reshaping GIS–ML integration.

Keywords

References

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Details

Primary Language

English

Subjects

Forestry Sciences (Other)

Journal Section

Research Article

Publication Date

August 24, 2026

Submission Date

April 23, 2026

Acceptance Date

July 7, 2026

Published in Issue

Year 2026 Number: 13

APA
Yiğit Uzunali, Ş., & Uzunali, A. (2026). Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends. Anadolu Orman Araştırmaları Dergisi, 13, 1936541. https://doi.org/10.53516/ajfr.1936541
AMA
1.Yiğit Uzunali Ş, Uzunali A. Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends. AJFR. 2026;(13):1936541. doi:10.53516/ajfr.1936541
Chicago
Yiğit Uzunali, Şeyma, and Alper Uzunali. 2026. “Geographic Information Systems (GIS)-Based Machine Learning Research in Landscape Architecture: Scientific Analysis and Trends”. Anadolu Orman Araştırmaları Dergisi, nos. 13: 1936541. https://doi.org/10.53516/ajfr.1936541.
EndNote
Yiğit Uzunali Ş, Uzunali A (August 1, 2026) Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends. Anadolu Orman Araştırmaları Dergisi 13 1936541.
IEEE
[1]Ş. Yiğit Uzunali and A. Uzunali, “Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends”, AJFR, no. 13, p. 1936541, Aug. 2026, doi: 10.53516/ajfr.1936541.
ISNAD
Yiğit Uzunali, Şeyma - Uzunali, Alper. “Geographic Information Systems (GIS)-Based Machine Learning Research in Landscape Architecture: Scientific Analysis and Trends”. Anadolu Orman Araştırmaları Dergisi. 13 (August 1, 2026): 1936541. https://doi.org/10.53516/ajfr.1936541.
JAMA
1.Yiğit Uzunali Ş, Uzunali A. Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends. AJFR. 2026;:1936541.
MLA
Yiğit Uzunali, Şeyma, and Alper Uzunali. “Geographic Information Systems (GIS)-Based Machine Learning Research in Landscape Architecture: Scientific Analysis and Trends”. Anadolu Orman Araştırmaları Dergisi, no. 13, Aug. 2026, p. 1936541, doi:10.53516/ajfr.1936541.
Vancouver
1.Şeyma Yiğit Uzunali, Alper Uzunali. Geographic information systems (GIS)-based machine learning research in landscape architecture: Scientific analysis and trends. AJFR. 2026 Aug. 1;(13):1936541. doi:10.53516/ajfr.1936541