Accurate assessment of post-disaster damage is essential for prioritizing emergency response, yet current practices rely heavily on manual interpretation of satellite imagery.This approach is time-consuming, subjective, and difficult to scale during large-area disasters. Although recent deep-learning models for semantic segmentation and change detection have improved automation, many of them still struggle to capture subtle structural variations and often perform poorly when dealing with highly imbalanced datasets, where undamaged buildings dominate. This thesis introduces Satellite-to-Street:Disaster Impact Estimator, a deep-learning framework that produces detailed, pixel-level damage maps by analyzing pre and post-disaster satellite images together. The model is built on a modified dual-input U-Net architecture that strengthens feature fusion between both images, allowing it to detect not only small, localized changes but also broader contextual patterns across the scene. To address the imbalance between damage categories, a class-aware weighted loss function is used, which helps the model better recognize major and destroyed structures. A consistent preprocessing pipeline is employed to align image pairs, standardize resolutions, and prepare the dataset for training. Experiments conducted on publicly available disaster datasets show that the proposed framework achieves better classification of damaged regions compared to conventional segmentation networks.The generated damage maps provide faster and objective method for analyzing disaster impact, working alongside expert judgment rather than replacing it. In addition to identifying which areas are damaged, the system is capable of distinguishing different levels of severity, ranging from slight impact to complete destruction. This provides a more detailed and practical understanding of how the disaster has affected each region.

翻译：准确评估灾后损害对于确定应急响应优先级至关重要，然而当前实践主要依赖于对卫星影像的人工判读。这种方法耗时、主观，且在大范围灾害中难以扩展。尽管近期用于语义分割和变化检测的深度学习模型提升了自动化水平，但许多模型仍难以捕捉细微的结构变化，且在处理未受损建筑物占主导的高度不平衡数据集时表现不佳。本论文提出卫星到街道：灾害影响评估系统，这是一个通过联合分析灾前与灾后卫星图像来生成详细像素级损害图的深度学习框架。该模型基于改进的双输入U-Net架构构建，增强了双幅图像间的特征融合，使其不仅能检测局部细微变化，还能识别场景中更广泛的上下文模式。为应对损害类别间的不平衡问题，模型采用了类别感知加权损失函数，有助于更好地识别严重受损及完全毁坏的结构。研究采用了一致的预处理流程来对齐图像对、标准化分辨率并准备训练数据集。在公开可用的灾害数据集上进行的实验表明，与传统分割网络相比，所提框架在受损区域分类方面表现更优。生成的损害图为分析灾害影响提供了更快速、客观的方法，可作为专家判断的辅助工具而非替代。除了识别受损区域，该系统还能区分从轻微影响到完全损毁的不同严重程度等级，从而为理解灾害对各区域的具体影响提供更详细且实用的信息。