Advancing perturbation space expansion based on information fusion for semi-supervised remote sensing image semantic segmentation DOI
Liang Zhou, Keyi Duan, Jinkun Dai

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102830 - 102830

Published: Dec. 1, 2024

Language: Английский

Toward agricultural cultivation parcels extraction in the complex mountainous areas using prior information and deep learning DOI
Jing Zhang, Tianjun Wu,

Jiancheng Luo

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2025, Volume and Issue: 63, P. 1 - 14

Published: Jan. 1, 2025

Language: Английский

Citations

2
Bioclimatic zonation and spatial-scale dependence of lacustrine microbial assemblages DOI
Shuren Wang, Qinglong L. Wu, Huabing Li

et al.

Science Bulletin, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
CMAB: A Multi-Attribute Building Dataset of China DOI Creative Commons
Yecheng Zhang, Huimin Zhao, Ying Long

et al.

Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)

Published: March 12, 2025

Rapidly acquiring three-dimensional (3D) building data, including geometric attributes like rooftop, height and orientations, as well indicative function, quality, age, is essential for accurate urban analysis, simulations, policy updates. Current datasets suffer from incomplete coverage of multi-attributes. This paper presents the first national-scale Multi-Attribute Building dataset (CMAB) with artificial intelligence, covering 3,667 spatial cities, 31 million buildings, 23.6 billion m² rooftops an F1-Score 89.93% in OCRNet-based extraction, totaling 363 m³ stock. We trained bootstrap aggregated XGBoost models city administrative classifications, incorporating morphology, location, function features. Using multi-source billions remote sensing images 60 street view (SVIs), we generated height, structure, style, quality each machine learning large multimodal models. Accuracy was validated through model benchmarks, existing similar products, manual SVI validation, mostly above 80%. Our results are crucial global SDGs planning.

Language: Английский

Citations

0
Fine-grained local climate zone classification using graph networks: A building-centric approach DOI
Siyu Li, Pengyuan Liu, Rudi Stouffs

et al.

Building and Environment, Journal Year: 2025, Volume and Issue: unknown, P. 112928 - 112928

Published: April 1, 2025

Language: Английский

Citations

0
Climate Change-based Urban Geographical Regions Planning: Sustainable Application Using Artificial Intelligence DOI
Khadiza Begum,

Srinivas Ambala,

Bathina Rajesh Kumar

et al.

Remote Sensing in Earth Systems Sciences, Journal Year: 2024, Volume and Issue: unknown

Published: Dec. 19, 2024

Language: Английский

Citations

0
Integrating remote sensing with OpenStreetMap data for comprehensive scene understanding through multi-modal self-supervised learning DOI
Lubin Bai, Xiuyuan Zhang, Haoyu Wang

et al.

Remote Sensing of Environment, Journal Year: 2024, Volume and Issue: 318, P. 114573 - 114573

Published: Dec. 23, 2024

Language: Английский

Citations

0
Advancing perturbation space expansion based on information fusion for semi-supervised remote sensing image semantic segmentation DOI
Liang Zhou, Keyi Duan, Jinkun Dai

et al.

Information Fusion, Journal Year: 2024, Volume and Issue: unknown, P. 102830 - 102830

Published: Dec. 1, 2024

Language: Английский

Citations

0