Information Discovery from Big Earth Observation Data Archives by Learning from Volunteered Geographic Information (IDEAL-VGI)

在过去的十年中，大量的遥感图像被获取，导致大量的地球观测数据档案，从中挖掘和检索有用的信息是一项挑战。自愿地理信息（VGI），如OpenStreetMap (OSM)，可以提供丰富的几何和语义信息，超越土地使用标签，这对于从大型EO数据档案中获取和提取观测地球的重要信息非常有益。然而，OSM中用户提供的标签可能是嘈杂的、不完整的和冗余的。IDEAL-VGI项目通过关注以下主要挑战来解决非常重要的科学和实践问题：1)VGI用于土地利用分类，即：缺乏利用不同尺度下存在的丰富语义信息的框架，以及OSM派生的土地利用类别的不确定性；2)大EO数据，即：RS图像表征，索引和从大量档案中搜索。为此，我们将开发创新的方法，这将大大提高理论和现有工具的先进水平。特别是，将开发新的方法，旨在：1)识别不同OSM衍生特征的重要性，不确定性和质量；2)改进质量评估方法，促进OSM的相关语义内容和支持互补VGI数据流的集成；3)在RS图像分类框架下开发机器学习/深度学习算法，实现OSM标签的自动细化和分配；4)开发考虑OSM标签及其不确定信息的RS图像分类、搜索和检索方法；5)在比较两种分类方法的基础上，改进OSM语义土地利用描述和遥感图像分类；6)充分利用VGI生成准确的标注数据集，提高标注准确率，有助于训练数据集更具说服力。理想-VGI将有助于优先计划中指出的以下研究领域：1)VGI的信息检索和分析（VGI的机器学习和算法解释以及VGI的质量评估和不确定性分析）；2)积极参与，社会背景和隐私意识（基于VGI数据的信息管理和决策分析）。

During the last decade, huge amount of remote sensing (RS) images have been acquired, leading to massive Earth Observation (EO) data archives from which mining and retrieving useful information are challenging. Volunteered Geographic Information (VGI) such as OpenStreetMap (OSM) can offer rich geometric and semantic information that goes beyond land use tags, which can be very beneficial for accessing and extracting vital information for observing Earth from big EO data archives. However, user-provided tags within OSM can be noisy, incomplete and redundant. The IDEAL-VGI project addresses very important scientific and practical problems by focusing on the main challenges of: 1) VGI for land use classification which are: a missing framework to exploit the rich semantic information present at different scales and the uncertainty of OSM derived land use classes: and 2) Big EO data, which are: RS image characterization, indexing and search from massive archives. To this end, we will develop innovative methods, which can significantly improve the state-of-the-art both in the theory and in the tools currently available. In particular, novel methods will be developed, aiming to: 1) identification of the importance, uncertainty and quality of different OSM derived features; 2) enhancing methods for better assessment of quality to promote relevant semantic content of OSM and integration of supporting complementary VGI data streams; 3) developing machine learning/deep learning algorithms in the framework of RS image classification for automatic OSM tag refinement and assignment; 4) developing RS image classification, search and retrieval methods that consider OSM tags with their uncertainty information; 5) improve both OSM semantic land use description as well as remote sensing image classification based on a comparison between the two classification approaches; 6) make full use of VGI to generate accurate annotated data sets and improve accuracy of labelling, which should contribute to more convincing training data sets. The IDEAL-VGI will contribute to the following research domains indicated in the priority programme: 1) Information Retrieval and Analysis of VGI (machine learning and algorithmic interpretation for VGI and quality assessment and uncertainty analysis of VGI): and 2) Active Participation, Social Context and Privacy Awareness (information management and decision analysis based on VGI data).