论文概览 |《IJGIS》2024.11 Vol.38 issue11

本次给大家整理的是《International Journal of Geographical Information Science》杂志2024年第38卷第11期的论文的题目和摘要，一共包括9篇SCI论文！

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论文1

A review of crowdsourced geographic information for land-use and land-cover mapping: current progress and challenges

通过众包地理信息进行土地利用和土地覆盖制图的综述：当前进展与挑战
 

【摘要】The emergence of crowdsourced geographic information (CGI) has markedly accelerated the evolution of land-use and land-cover (LULC) mapping. This approach taps into the collective power of the public to share spatial information, providing a relevant data source for producing LULC maps. Through the analysis of 262 papers published from 2012 to 2023, this work provides a comprehensive overview of the field, including prominent researchers, key areas of study, major CGI data sources, mapping methods, and the scope of LULC research. Additionally, it evaluates the pros and cons of various data sources and mapping methods. The findings reveal that while applying CGI with LULC labels is a common way by using spatial analysis, it is limited by incomplete CGI coverage and other data quality issues. In contrast, extracting semantic features from CGI for LULC interpretation often requires integrating multiple CGI datasets and remote sensing imagery, alongside advanced methods such as ensemble and deep learning. The paper also delves into the challenges posed by the quality of CGI data in LULC mapping and explores the promising potential of introducing large language models to overcome these hurdles.
 

【摘要翻译】众包地理信息（CGI）的出现显著加速了土地利用和土地覆盖（LULC）制图的发展。这种方法利用公众分享空间信息的集体力量，为生产LULC地图提供了相关的数据来源。通过分析2012年至2023年间发表的262篇论文，本研究全面概述了该领域，包括杰出研究者、主要研究领域、主要CGI数据来源、制图方法以及LULC研究的范围。此外，还评估了各种数据来源和制图方法的优缺点。研究结果表明，虽然使用LULC标签应用CGI是通过空间分析的常见方法，但它受到CGI覆盖不完整和其他数据质量问题的限制。相比之下，从CGI中提取语义特征以进行LULC解释通常需要整合多个CGI数据集和遥感影像，并结合集成学习和深度学习等先进方法。论文还探讨了CGI数据在LULC制图中的质量挑战，并探讨引入大型语言模型以克服这些障碍的潜在前景。
 

【doi】https://doi.org/10.1080/13658816.2024.2353695
 

【作者信息】Hao Wu, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Yan Li, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Anqi Lin, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉，linanqi@mails.ccnu.edu.cnHongchao Fan,挪威科技大学，土木与环境工程系，挪威特隆赫姆，TorgardenKaixuan Fan, 湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Junyang Xie，湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉Wenting Luo，湖北省地理过程分析与模拟重点实验室，华中师范大学，中国武汉；城市与环境科学学院，华中师范大学，中国武汉

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论文2

Spatial ensemble learning for predicting the potential geographical distribution of invasive species

用于预测入侵物种潜在地理分布的空间集成学习
 

【摘要】Understanding the geographical distribution of invasive species is beneficial for preventing and controlling biological invasions. A global model is often constructed with existing species distribution models (SDMs) to describe the relationships between environmental characteristics and species distributions. Because of the spatial variations in environmental characteristics, it may be difficult for a single SDM to obtain an accurate result in any given location or area. Therefore, a spatial ensemble learning method for predicting the potential geographical distribution of invasive species is presented in this study. The method mainly includes two types of learners: one learner is a base learner used to predict the geographical distribution of invasive species, and the other learner is a spatial ensemble learner for combining predictions from different base learners. In this research, spatial ensemble learning is used to predict the geographical distribution of Erigeron annuus in the Yangtze River Economic Belt, China. The kappa coefficient and AUC (area under the receiver operating characteristic curve) obtained with the spatial ensemble learner are 0.88 and 0.94, respectively, and these values are greater than those obtained using three base learners and other ensemble strategies. This demonstrates the feasibility and effectiveness of spatial ensemble learning.
 

【摘要翻译】理解入侵物种的地理分布有助于防止和控制生物入侵。通常构建一个全球模型，利用现有的物种分布模型（SDMs）来描述环境特征与物种分布之间的关系。由于环境特征的空间变化，单一的物种分布模型在任何给定地点或区域内可能难以获得准确的结果。因此，本研