To understand our global progress for sustainable development and disaster risk reduction in many developing economies, two recent major initiatives - the Uniform African Exposure Dataset of the Global Earthquake Model (GEM) Foundation and the Modelling Exposure through Earth Observation Routines (METEOR) Project - implemented classical spatial disaggregation techniques to generate large-scale mapping of urban morphology using the information from various satellite imagery and its derivatives, geospatial datasets of the built environment, and subnational census statistics. However, the local discrepancy with well-validated census statistics and the propagated model uncertainties remain a challenge in such coarse-to-fine-grained mapping problems, specifically constrained by weak and conditional label supervision. Therefore, we present Deep Conditional Census-Constrained Clustering (DeepC4), a novel deep learning-based spatial disaggregation approach that incorporates local census statistics as cluster-level constraints while considering multiple conditional label relationships in a joint multitask learning of the patterns of satellite imagery. To demonstrate, compared to GEM and METEOR, we enhanced the quality of Rwandan maps of urban morphology, specifically building exposure and physical vulnerability, at the third-level administrative unit from the 2022 census. As the world approaches the conclusion of many global frameworks in 2030, our work offers a new deep learning-based mapping technique that explicitly encodes well-validated census and experts' belief systems to achieve an explainable and interpretable auditing of existing coarse-grained derived information at large scales.

翻译：为理解许多发展中经济体在可持续发展和降低灾害风险方面的全球进展，两项近期重大倡议——全球地震模型（GEM）基金会的统一非洲暴露数据集和通过地球观测例程建模暴露（METEOR）项目——实施了经典的空间分解技术，利用各类卫星影像及其衍生产品、建成环境的地理空间数据集以及次国家级人口普查统计数据，生成了大规模的城市形态地图。然而，在此类从粗粒度到细粒度的制图问题中，与经过充分验证的人口普查统计数据存在的局部差异以及传播的模型不确定性仍然构成挑战，尤其受到弱监督和条件标签监督的限制。为此，我们提出了深度条件人口普查约束聚类（DeepC4），这是一种新颖的基于深度学习的空间分解方法。该方法将本地人口普查统计数据作为聚类级约束纳入，同时在卫星影像模式的联合多任务学习中考虑多重条件标签关系。为验证效果，与GEM和METEOR相比，我们基于2022年人口普查数据，在第三级行政单元层面提升了卢旺达城市形态地图（特别是建筑暴露度和物理脆弱性）的质量。随着世界在2030年临近多项全球框架的收官阶段，我们的工作提供了一种新的基于深度学习的制图技术，该技术显式编码了经过充分验证的人口普查数据和专家信念体系，旨在实现对现有大规模粗粒度衍生信息进行可解释、可理解的审计。