Accurate and detailed geographical information digitizing human activity patterns plays an essential role in response to natural disasters. Volunteered geographical information, in particular OpenStreetMap (OSM), shows great potential in providing the knowledge of human settlements to support humanitarian aid, while the availability and quality of OSM remains a major concern. The majority of existing works in assessing OSM data quality focus on either extrinsic or intrinsic analysis, which is insufficient to fulfill the humanitarian mapping scenario to a certain degree. This paper aims to explore OSM missing built-up areas from an integrative perspective of social sensing and remote sensing. First, applying hierarchical DBSCAN clustering algorithm, the clusters of geo-tagged tweets are generated as proxies of human active regions. Then a deep learning based model fine-tuned on existing OSM data is proposed to further map the missing built-up areas. Hit by Cyclone Idai and Kenneth in 2019, the Republic of Mozambique is selected as the study area to evaluate the proposed method at a national scale. As a result, 13 OSM missing built-up areas are identified and mapped with an over 90% overall accuracy, being competitive compared to state-of-the-art products, which confirms the effectiveness of the proposed method.

将人类活动模式数字化的准确，详细的地理信息在应对自然灾害方面发挥着至关重要的作用。自愿提供的地理信息，特别是OpenStreetMap（OSM），在提供人类住区知识以支持人道主义援助方面显示出巨大潜力，而OSM的可用性和质量仍然是一个主要问题。现有的评估OSM数据质量的大部分工作都集中在外部分析或内在分析上，这在一定程度上不足以实现人道主义制图方案。本文旨在从社会感测和遥感的综合角度探索OSM缺失的堆积区。首先，应用分层DBSCAN聚类算法，生成带有地理标记的推文的聚类作为人类活动区域的代理。然后，提出了一种基于深度学习的模型，该模型在现有OSM数据上进行了微调，以进一步映射缺失的堆积区域。莫桑比克共和国于2019年受到飓风伊代（Idai）和肯尼斯（Kenneth）的袭击，被选为研究区域，以在全国范围内评估拟议的方法。结果，识别并映射了13个OSM缺失的堆积区域，总体精度超过90％，与最先进的产品相比具有竞争力，这证实了所提出方法的有效性。