ABSTRACT

Updating an authoritative Land Use and Land Cover (LULC) database requires many resources. Volunteered geographic information (VGI) involves citizens in the collection of data about their spatial environment. There is a growing interest in using existing VGI to update authoritative databases. This paper presents a framework aimed at integrating multi-source VGI based on a data fusion technique, in order to update an authoritative land use database. Each VGI data source is considered to be an independent source of information, which is fused together using Dempster-Shafer Theory (DST). The framework is tested in the updating of the authoritative land use data produced by the French National Mapping Agency. Four data sets were collected from several in-situ and remote campaigns run between 2018 and 2020 by contributors with varying profiles. The data fusion approach achieved an overall accuracy of 85.6% for the 144 features having at least two contributions when the confidence threshold was set to 0.05. Despite the heterogeneity and limited amount of VGI used, the results are promising, with 99% of the LU polygons updated or enriched. These results show the potential of using multi-source VGI to update or enrich authoritative LU data and potentially LULC data more generally.

摘要

更新权威的土地使用和土地覆盖（LULC）数据库需要大量资源。自愿性地理信息（VGI）使公民参与有关其空间环境的数据收集。使用现有的VGI更新权威数据库的兴趣日益浓厚。本文提出了一个旨在基于数据融合技术集成多源VGI的框架，以更新权威的土地利用数据库。每个VGI数据源都被视为独立的信息源，使用Dempster-Shafer理论（DST）将其融合在一起。在更新法国国家制图局提供的权威土地使用数据时，对该框架进行了测试。从几个现场收集了四个数据集远程活动则在2018年至2020年之间由配置文件各异的贡献者进行。当置信度阈值设置为0.05时，对于至少具有两个贡献的144个特征，数据融合方法的总体准确度达到85.6％。尽管使用了VGI的异构性和数量有限，但结果令人鼓舞，有99％的LU多边形得到了更新或丰富。这些结果表明，使用多源VGI来更广泛地更新或丰富权威性LU数据以及潜在的LULC数据的潜力。