Land use and land cover (LULC) mapping is often undertaken by national mapping agencies, where these LULC products are used for different types of monitoring and reporting applications. Updating of LULC databases is often done on a multi-year cycle due to the high costs involved, so changes are only detected when mapping exercises are repeated. Consequently, the information on LULC can quickly become outdated and hence may be incorrect in some areas. In the current era of big data and Earth observation, change detection algorithms can be used to identify changes in urban areas, which can then be used to automatically update LULC databases on a more continuous basis. However, the change detection algorithm must be validated before the changes can be committed to authoritative databases such as those produced by national mapping agencies. This paper outlines a change detection algorithm for identifying construction sites, which represent ongoing changes in LU, developed in the framework of the LandSense project. We then use volunteered geographic information (VGI) captured through the use of mapathons from a range of different groups of contributors to validate these changes. In total, 105 contributors were involved in the mapathons, producing a total of 2778 observations. The 105 contributors were grouped according to six different user-profiles and were analyzed to understand the impact of the experience of the users on the accuracy assessment. Overall, the results show that the change detection algorithm is able to identify changes in residential land use to an adequate level of accuracy (85%) but changes in infrastructure and industrial sites had lower accuracies (57% and 75 %, respectively), requiring further improvements. In terms of user profiles, the experts in LULC from local authorities, researchers in LULC at the French national mapping agency (IGN), and first-year students with a basic knowledge of geographic information systems had the highest overall accuracies (86.2%, 93.2%, and 85.2%, respectively). Differences in how the users approach the task also emerged, e.g., local authorities used knowledge and context to try to identify types of change while those with no knowledge of LULC (i.e., normal citizens) were quicker to choose ‘Unknown’ when the visual interpretation of a class was more difficult.

中文翻译：

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使用自动变更检测和VGI源识别和验证城市土地用途变更

土地使用和土地覆被（LULC）制图通常由国家制图机构进行，这些LULC产品用于不同类型的监视和报告应用程序。由于涉及高昂的成本，LULC数据库的更新通常需要数年的时间，因此只有在重复进行制图时才能检测到变化。因此，有关LULC的信息很快就会过时，因此在某些地区可能不正确。在当前的大数据和地球观测时代，可以使用变化检测算法来识别城市区域中的变化，然后可以使用这些算法自动更连续地更新LULC数据库。但是，必须先验证变更检测算法，然后才能将变更提交给权威数据库，例如由国家测绘局制作的数据库。本文概述了一种变化检测算法，用于识别在LandSense项目框架中开发的施工现场，这些施工现场代表着LU的持续变化。然后，我们使用通过使用来自一系列不同贡献者组的mapathon捕获的自愿性地理信息（VGI）来验证这些更改。总共有105位贡献者参与了这次马拉松比赛，总共产生了2778次观测。根据六个不同的用户资料对105个贡献者进行了分组，并进行了分析以了解用户体验对准确性评估的影响。总体而言，结果表明，变化检测算法能够以足够的准确度（85％）识别住宅用地变化，但基础设施和工业用地的变化准确度较低（57％和75％，分别），需要进一步改进。在用户资料方面，来自地方当局的LULC专家，法国国家地图局（IGN）的LULC研究人员以及具有地理信息系统基础知识的一年级学生总体准确性最高（86.2％，93.2） ％和85.2％）。用户如何完成任务的方式也出现了差异，例如，地方当局使用知识和上下文来尝试识别变化的类型，而对LULC不了解的人（即正常公民）在进行视觉解释时可以更快地选择“未知”一堂课比较困难。和具有地理信息系统基本知识的一年级学生的总体准确率最高（分别为86.2％，93.2％和85.2％）。用户如何完成任务的方式也出现了差异，例如，地方当局使用知识和上下文来尝试识别变化的类型，而对LULC不了解的人（即正常公民）在进行视觉解释时可以更快地选择“未知”一堂课比较困难。和具有地理信息系统基本知识的一年级学生的总体准确率最高（分别为86.2％，93.2％和85.2％）。用户如何完成任务的方式也出现了差异，例如，地方当局使用知识和上下文来尝试识别变化的类型，而对LULC不了解的人（即正常公民）在进行视觉解释时可以更快地选择“未知”一堂课比较困难。