The increasing frequency and intensity of natural disasters call for rapid and accurate damage assessment. In response, disaster benchmark datasets from high-resolution satellite imagery have been constructed to develop methods for detecting damaged areas. However, these methods face significant challenges when applied to previously unseen regions due to the limited geographical and disaster-type diversity in the existing datasets. We introduce DAVI (Disaster Assessment with VIsion foundation model), a novel approach that addresses domain disparities and detects structural damage at the building level without requiring ground-truth labels for target regions. DAVI combines task-specific knowledge from a model trained on source regions with task-agnostic knowledge from an image segmentation model to generate pseudo labels indicating potential damage in target regions. It then utilizes a two-stage refinement process, which operate at both pixel and image levels, to accurately identify changes in disaster-affected areas. Our evaluation, including a case study on the 2023 Türkiye earthquake, demonstrates that our model achieves exceptional performance across diverse terrains (e.g., North America, Asia, and the Middle East) and disaster types (e.g., wildfires, hurricanes, and tsunamis). This confirms its robustness in disaster assessment without dependence on ground-truth labels and highlights its practical applicability.

International aid is a critical mechanism for promoting economic growth and well-being in developing nations, supporting progress toward the Sustainable Development Goals (SDGs). However, tracking aid contributions remains challenging due to labor-intensive data management, incomplete records, and the heterogeneous nature of aid data. Recognizing the urgency of this challenge, we partnered with government agencies to develop an AI model that complements manual classification and mitigates human bias in subjective interpretation. By integrating SDG-specific semantics and leveraging prior knowledge from language models, our approach enhances classification accuracy and accommodates the diversity of aid projects. When applied to a comprehensive dataset spanning multiple years, our model can reveal hidden trends in the temporal evolution of international development cooperation. Expert interviews further suggest how these insights can empower policymakers with data-driven decision-making tools, ultimately improving aid effectiveness and supporting progress toward SDGs.

While advances in deep learning have enabled novel applications in various fields, face recognition in open-set scenarios remains a complex task, owing to the challenges posed by the extensive volume of low-quality face images. We introduce a new approach for recognizing faces in unconstrained open-set settings by leveraging uncertainty-aware embeddings through contrastive learning. Our model, called UCFace, effectively regulates the contribution of each face image based on the face uncertainty derived from image quality as an inverse proxy. Face embeddings are reinterpreted as a probabilistic distribution within the embedding space, where the degree of sharpness (i.e., distribution concentration) reflects the underlying uncertainty and probability density is used as a similarity metric to facilitate contrastive learning. Experiments on a wide range of face datasets, including those with high, mixed, and real-world low-resolution face images, demonstrate that UCFace enhances open-set face recognition performance by integrating the aspect of uncertainty.