
International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)
Опубликована: Дек. 23, 2024
Язык: Английский
International Journal of Digital Earth, Год журнала: 2024, Номер 17(1)
Опубликована: Дек. 23, 2024
Язык: Английский
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
31International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104353 - 104353
Опубликована: Янв. 5, 2025
Язык: Английский
Процитировано
2Applied Geography, Год журнала: 2025, Номер 178, С. 103572 - 103572
Опубликована: Фев. 27, 2025
Язык: Английский
Процитировано
1International 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
3International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 136, С. 104397 - 104397
Опубликована: Фев. 1, 2025
Язык: Английский
Процитировано
0International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2025, Номер 137, С. 104386 - 104386
Опубликована: Фев. 24, 2025
Язык: Английский
Процитировано
0Geocarto International, Год журнала: 2025, Номер 40(1)
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Information Fusion, Год журнала: 2025, Номер unknown, С. 103140 - 103140
Опубликована: Март 1, 2025
Язык: Английский
Процитировано
0Scientific Data, Год журнала: 2025, Номер 12(1)
Опубликована: Май 7, 2025
Язык: Английский
Процитировано
0International Journal of Geographical Information Science, Год журнала: 2025, Номер unknown, С. 1 - 24
Опубликована: Май 20, 2025
Язык: Английский
Процитировано
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