Very High Resolution (VHR) geospatial image analysis is crucial for humanitarian assistance in both natural and anthropogenic crises, as it allows to rapidly identify the most critical areas that need support. Nonetheless, manually inspecting large areas is time-consuming and requires domain expertise. Thanks to their accuracy, generalization capabilities, and highly parallelizable workload, Deep Neural Networks (DNNs) provide an excellent way to automate this task. Nevertheless, there is a scarcity of VHR data pertaining to conflict situations, and consequently, of studies on the effectiveness of DNNs in those scenarios. Motivated by this, our work extensively studies the applicability of a collection of state-of-the-art Convolutional Neural Networks (CNNs) originally developed for natural disasters damage assessment in a war scenario. To this end, we build an annotated dataset with pre- and post-conflict images of the Ukrainian city of Mariupol. We then explore the transferability of the CNN models in both zero-shot and learning scenarios, demonstrating their potential and limitations. To the best of our knowledge, this is the first study to use sub-meter resolution imagery to assess building damage in combat zones.

本研究解决了冲突区域高分辨率地理空间数据匮乏的问题，提出利用深度卷积神经网络（CNN）自动评估建筑损害的新方法。通过分析乌克兰马里乌波尔战区的前后冲突图像数据，我们首次展示了亚米分辨率影像在战斗zones的应用潜力，揭示了现有模型的转移性及其局限性，具有重要的人道主义意义。

冲突区建筑损害评估：基于深度学习的地理空间亚米分辨率数据方法