Unsupervised Urban Tree Biodiversity Mapping from Street Imagery
A Master of Science thesis in Machine Learning by Diaa Addeen Abuhani entitled, “Unsupervised Urban Tree Biodiversity Mapping from Street Imagery”, submitted in August 2025. Thesis advisor is Dr. Imran Zualkernan and thesis co-advisor is Dr. Martina Mazzarella. Soft copy is available (Thesis, Comple...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2025
|
| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/32512 |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513443678126080 |
|---|---|
| author | Abuhani, Diaa Addeen |
| author_facet | Abuhani, Diaa Addeen |
| author_role | author |
| dc.contributor.none.fl_str_mv | Zualkernan, Imran Mazzarella, Martina |
| dc.creator.none.fl_str_mv | Abuhani, Diaa Addeen |
| dc.date.none.fl_str_mv | 2025-11-26T06:44:35Z 2025-11-26T06:44:35Z 2025-08 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2025.39 https://hdl.handle.net/11073/32512 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Machine Learning (MSMLR) |
| dc.subject.none.fl_str_mv | Biodiversity Unsupervised Learning Long-tailed Problems Fine-grained |
| dc.title.none.fl_str_mv | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Machine Learning by Diaa Addeen Abuhani entitled, “Unsupervised Urban Tree Biodiversity Mapping from Street Imagery”, submitted in August 2025. Thesis advisor is Dr. Imran Zualkernan and thesis co-advisor is Dr. Martina Mazzarella. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_8df1c2e85f13dcd3e43b5f04485be846 |
| identifier_str_mv | 35.232-2025.39 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/32512 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Unsupervised Urban Tree Biodiversity Mapping from Street ImageryAbuhani, Diaa AddeenBiodiversityUnsupervised LearningLong-tailed ProblemsFine-grainedA Master of Science thesis in Machine Learning by Diaa Addeen Abuhani entitled, “Unsupervised Urban Tree Biodiversity Mapping from Street Imagery”, submitted in August 2025. Thesis advisor is Dr. Imran Zualkernan and thesis co-advisor is Dr. Martina Mazzarella. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Urban tree biodiversity plays a critical role in climate resilience, ecological stability, and livability. However, large-scale biodiversity assessments remain limited due to the need for taxonomic labels and expert-led field surveys. In this work, we introduce an unsupervised clustering framework that combines visual embeddings and spatial priors to assess urban tree biodiversity directly from street-level imagery without reliance on labeled data. Our method accurately captures key ecological indicators, particularly Shannon and Simpson entropies, and provides reasonable estimates of species richness across diverse urban contexts. By leveraging the inherent spatial distribution of trees alongside visual features, our approach remains robust to geographic variability and domain shifts. We validate our framework across multiple cities and demonstrate its capacity to recover genus-level biodiversity patterns that align with known ecological distributions. This work provides a scalable pathway for monitoring urban biodiversity and offers a step toward more generalizable, data-efficient ecological assessment tools in support of nature-based urban planning.College of EngineeringDepartment of Computer Science and EngineeringMaster of Science in Machine Learning (MSMLR)Zualkernan, ImranMazzarella, Martina2025-11-26T06:44:35Z2025-11-26T06:44:35Z2025-08info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2025.39https://hdl.handle.net/11073/32512en_USMaster of Science in Machine Learning (MSMLR)oai:repository.aus.edu:11073/325122025-11-26T11:39:47Z |
| spellingShingle | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery Abuhani, Diaa Addeen Biodiversity Unsupervised Learning Long-tailed Problems Fine-grained |
| status_str | publishedVersion |
| title | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| title_full | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| title_fullStr | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| title_full_unstemmed | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| title_short | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| title_sort | Unsupervised Urban Tree Biodiversity Mapping from Street Imagery |
| topic | Biodiversity Unsupervised Learning Long-tailed Problems Fine-grained |
| url | https://hdl.handle.net/11073/32512 |