Integrating metro passenger flow data to improve the classification of urban functional regions using a heterogeneous graph neural network DOI Creative Commons
Pengxin Zhang, Min Yang, Yong Wang

и другие.

International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)

Опубликована: Дек. 23, 2024

Язык: Английский

Deep learning for urban land use category classification: A review and experimental assessment DOI Creative Commons
Ziming Li, Бин Чэн, Shengbiao Wu

и другие.

Remote Sensing of Environment, Год журнала: 2024, Номер 311, С. 114290 - 114290

Опубликована: Июль 14, 2024

Mapping the distribution, pattern, and composition of urban land use categories plays a valuable role in understanding environmental dynamics facilitating sustainable development. Decades effort mapping have accumulated series approaches products. New trends characterized by open big data advanced artificial intelligence, especially deep learning, offer unprecedented opportunities for patterns from regional to global scales. Combined with large amounts geospatial data, learning has potential promote higher levels scale, accuracy, efficiency, automation. Here, we comprehensively review advances based research practices aspects sources, classification units, approaches. More specifically, delving into different settings on learning-based mapping, design eight experiments Shenzhen, China investigate their impacts performance terms sample, model. For each investigated setting, provide quantitative evaluations discussed inform more convincing comparisons. Based historical retrospection experimental evaluation, identify prevailing limitations challenges suggest prospective directions that could further facilitate exploitation techniques using remote sensing other spatial across various

Язык: Английский

Процитировано

31
A graph-based multimodal data fusion framework for identifying urban functional zone DOI Creative Commons
Tao Yuan,

Wanzeng Liu,

Jun Chen

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104353 - 104353

Опубликована: Янв. 5, 2025

Язык: Английский

Процитировано

2
The impact of sub-pixel scale urban function on urban heat island: Insights derived from its decomposition DOI
Qingfeng Guan, Yajun Li, W. J. Huang

и другие.

Applied Geography, Год журнала: 2025, Номер 178, С. 103572 - 103572

Опубликована: Фев. 27, 2025

Язык: Английский

Процитировано

1
DCAI-CLUD: a data-centric framework for the construction of land-use datasets DOI

Hao Wu,

Zhangwei Jiang, Anning Dong

и другие.

International Journal of Geographical Information Science, Год журнала: 2024, Номер 38(11), С. 2379 - 2402

Опубликована: Авг. 5, 2024

A high-quality land-use dataset is crucial for constructing a high-performance classification model. Due to the complexity and spatial heterogeneity of land-use, construction process inefficient costly. This challenge affects quality datasets, consequently impacting model's performance. The emerging field Data-Centric Artificial Intelligence (DCAI) expected deliver techniques optimization, offering promising solution problem. Therefore, this study proposes data-centric framework named DCAI-CLUD datasets. Based on framework, accuracy rate data labeling are improved by 5.93 28.97%. Gini index proportion samples with non-mixed categories enhanced 3.27 8.52%. overall (OA) Kappa model significantly 27.87 58.08%. first introduce DCAI into geographic information remote sensing verify its effectiveness. proposed can effectively improve efficiency synchronously optimize we constructed multi-source major cities in China CN-MSLU-100K.

Язык: Английский

Процитировано

3
Multi-spatial urban function modeling: A multi-modal deep network approach for transfer and multi-task learning DOI Creative Commons
Zhaoya Gong, Chenglong Wang, Bin Liu

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104397 - 104397

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
Fine-grained building function recognition with street-view images and GIS map data via geometry-aware semi-supervised learning DOI Creative Commons
Weijia Li, Jinhua Yu, Dairong Chen

и другие.

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 137, С. 104386 - 104386

Опубликована: Фев. 24, 2025

Язык: Английский

Процитировано

0
A method of urban high-precision DEM construction based on point cloud model DOI Creative Commons
Weibo Zeng, Xiaoxiao Zhang,

Shuangxi Gu

и другие.

Geocarto International, Год журнала: 2025, Номер 40(1)

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
Urban region function classification via fusing optical imagery and social media data: A spatio-temporal Transformer interaction approach DOI
Ruiyang Sun, Xin Su, Qiangqiang Yuan

и другие.

Information Fusion, Год журнала: 2025, Номер unknown, С. 103140 - 103140

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0
MSLU-100K: A Large Multi-Source Dataset for Land Use Analysis in Major Chinese Cities DOI Creative Commons
Yao Yao, Yini Ma, Ronghui Gao

и другие.

Scientific Data, Год журнала: 2025, Номер 12(1)

Опубликована: Май 7, 2025

Язык: Английский

Процитировано

0
LandGPT: a multimodal large language model for parcel-level land use classification with multi-source data DOI
Ge Zhu,

Mi Tang,

Yueheng Ma

и другие.

International Journal of Geographical Information Science, Год журнала: 2025, Номер unknown, С. 1 - 24

Опубликована: Май 20, 2025

Язык: Английский

Процитировано

0