Thematic quality assessment of land surface geospatial data based on confusion matrices: A matrix set for research on measures and procedures,Geoscience Data Journal

当前位置： X-MOL 学术 › Geosci. Data J. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

Thematic quality assessment of land surface geospatial data based on confusion matrices: A matrix set for research on measures and procedures
Geoscience Data Journal ( IF 3.3 ) Pub Date : 2021-03-16 , DOI: 10.1002/gdj3.116
Francisco J. Ariza‐López ₁ , José L. García‐Balboa ₁ , María V. Alba‐Fernández ₂ , José Rodríguez‐Avi ₂

Affiliation

The confusion matrix has long been adopted as the ‘de facto’ and ‘de jure’ standard method of reporting on the thematic accuracy assessment of any land surface geospatial dataset. This type of data supports decision-making in many different fields, so suitable quality is therefore essential in order to take the best decisions. Nevertheless, the creation and exploitation of the confusion matrix remains as an open topic with issues related to sampling design, quantitative indices derived from the matrix, statistical hypotheses that could be applied, etc. In connection with the latter, a confusion matrix dataset would be useful for a researcher in this matter. We have developed such a dataset retrieving confusion matrices from the literature, mainly research articles published in scientific journals included in WoS. We have collected almost 200 matrices in a database. This allows us to access the complete matrices and query different interesting properties of them and of the project where they were developed such as matrix size, sample size, location, year of data capture, labels of the classes, quality indices used, and extension and location of the project (where available).

中文翻译：

基于混淆矩阵的地表地理空间数据专题质量评估：用于研究措施和程序的矩阵集

混淆矩阵长期以来一直被采用作为报告任何陆面地理空间数据集的主题准确性评估的“事实上的”和“法律上的”标准方法。这种类型的数据支持许多不同领域的决策，因此适当的质量对于做出最佳决策至关重要。尽管如此，混淆矩阵的创建和利用仍然是一个开放主题，涉及与抽样设计、从矩阵得出的定量指标、可以应用的统计假设等相关的问题。与后者相关，混淆矩阵数据集将是对这方面的研究人员有用。我们开发了这样一个数据集，从文献中检索混淆矩阵，主要是发表在 WoS 中的科学期刊上的研究文章。我们在数据库中收集了近 200 个矩阵。这使我们能够访问完整的矩阵并查询它们以及开发它们的项目的不同有趣属性，例如矩阵大小、样本大小、位置、数据捕获年份、类别标签、使用的质量指标以及扩展和项目的位置（如果有）。

更新日期：2021-03-16

点击分享查看原文

点击收藏

公开下载

阅读更多本刊最新论文本刊介绍/投稿指南11