arXiv Machine Learning By Htet Yamin Ko Ko

Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data

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arXiv:2607. 22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such information underpins the assessment of ecosystem services provided by green infrastructure, particularly for urban heat island (UHI) mitigation.

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arXiv AI
Jul 9

CarbonCLIP: Enhance Carbon Prediction from Satellite Imagery via Integrated Street-View Semantics and Temporal Context Training

arXiv:2607. 07292v1 Announce Type: cross Abstract: Accurately estimating urban carbon emissions is critical for sustainable urban planning, yet many existing approaches remain difficult to apply consistently across cities due to data-source heterogeneity and the lack of fine-grained semantic-temporal context in remote sensing data.

By Zeru Yang, Fang-Ying Gong, Steve H. L. Yim, Chau Yuen
arXiv Machine Learning
Jun 19

Exploring the potential of AlphaEarth and TESSERA embeddings for Fine-scale Local Climate Zone Mapping: A case study across five cities in Switzerland

arXiv:2606. 20034v1 Announce Type: new Abstract: Understanding urban spatial morphology is critical for climate modeling, risk assessment, and sustainable urban design, and Local Climate Zone (LCZ) mapping provides the basic framework for this.

By Htet Yamin Ko Ko, Clement Atzberger