Fine-Tuned AlexNet for Roof Shape Classification in Uzbekistan: a Transfer Learning Approach

Yuldashev, S.U., Юлдашев, С.У.

Ҳисоблаш ва амалий математика муаммолари · 2025-yil

Annotatsiya

This study investigates the classification of rooftop shapes using convolutional neural networks (CNNs), with a particular focus on regional adaptation through transfer learning. Initial training of the AlexNet architecture utilizes a publicly available Zenodo dataset comprising satellite imagery of flat, gabled, and hipped roofs. To address generalizability constraints in specific geographic contexts, a custom dataset of Uzbekistan rooftops, sourced from OpenStreetMap and high-resolution Mapbox Static Images API tiles, enables fine-tuning. Experimental outcomes reveal enhanced classification accuracy for flat, gabled, and hipped roofs, underscoring the efficacy of transfer learning in mitigating domain shift due to architectural and environmental variations. Integration of open-source geospatial tools with transfer learning offers a replicable framework for addressing geographic bias in rooftop shape classification, adaptable to other underrepresented regions.

Maqola ma’lumotlari
MualliflarYuldashev, S.U., Юлдашев, С.У.
JurnalҲисоблаш ва амалий математика муаммолари
Nashr sanasi2025-07-27
Son3
Betlar133-146
TilIngliz
DOI10.71310/pcam.3_67.2025.12

Kalit so‘zlar

roof shape classification, convolutional neural networks, transfer learning, AlexNet, satellite imagery, классификация форм крыш, сверточные нейронные сети, трансферное обучение, AlexNet, спутниковые снимки

Ilmiy soha

Ҳисоблаш ва амалий математика муаммолари jurnalidan boshqa maqolalar

Ҳисоблаш ва амалий математика муаммолари — barcha maqolalar