Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping
Green infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/2/464 |
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| author | Dino Dobrinić Mario Miler Damir Medak |
| author_facet | Dino Dobrinić Mario Miler Damir Medak |
| author_sort | Dino Dobrinić |
| collection | DOAJ |
| description | Green infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing on the various sensors and image data, as well as the application of machine learning and deep learning techniques for classification or segmentation tasks. After finding articles with relevant keywords, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) method was used as a general workflow, but some parts were automated (e.g., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. A shift towards deep learning methods can be observed in the mapping of GIs as 33 articles use various deep learning methods, while 22 articles use machine learning methods. In addition, this article presents a novel methodology for automated verification methods, demonstrating their potential effectiveness and highlighting areas for improvement. |
| format | Article |
| id | doaj-art-bae3e177de0545bdae3681121151476f |
| institution | Kabale University |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bae3e177de0545bdae3681121151476f2025-01-24T13:49:01ZengMDPI AGSensors1424-82202025-01-0125246410.3390/s25020464Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure MappingDino Dobrinić0Mario Miler1Damir Medak2Chair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10 000 Zagreb, CroatiaChair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10 000 Zagreb, CroatiaChair of Geoinformatics, Faculty of Geodesy, University of Zagreb, 10 000 Zagreb, CroatiaGreen infrastructure (GI) plays a crucial role in sustainable urban development, but effective mapping and analysis of such features requires a detailed understanding of the materials and state-of-the-art methods. This review presents the current landscape of green infrastructure mapping, focusing on the various sensors and image data, as well as the application of machine learning and deep learning techniques for classification or segmentation tasks. After finding articles with relevant keywords, the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyzes) method was used as a general workflow, but some parts were automated (e.g., screening) by using natural language processing and large language models. In total, this review analyzed 55 papers that included keywords related to GI mapping and provided materials and learning methods (i.e., machine or deep learning) essential for effective green infrastructure mapping. A shift towards deep learning methods can be observed in the mapping of GIs as 33 articles use various deep learning methods, while 22 articles use machine learning methods. In addition, this article presents a novel methodology for automated verification methods, demonstrating their potential effectiveness and highlighting areas for improvement.https://www.mdpi.com/1424-8220/25/2/464green infrastructuremachine learningdeep learningurban ecosystemgreen spaceScopus |
| spellingShingle | Dino Dobrinić Mario Miler Damir Medak Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping Sensors green infrastructure machine learning deep learning urban ecosystem green space Scopus |
| title | Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping |
| title_full | Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping |
| title_fullStr | Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping |
| title_full_unstemmed | Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping |
| title_short | Mapping the Green Urban: A Comprehensive Review of Materials and Learning Methods for Green Infrastructure Mapping |
| title_sort | mapping the green urban a comprehensive review of materials and learning methods for green infrastructure mapping |
| topic | green infrastructure machine learning deep learning urban ecosystem green space Scopus |
| url | https://www.mdpi.com/1424-8220/25/2/464 |
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