Access to detailed war impact assessments is crucial for humanitarian
organizations to effectively assist populations most affected by armed
conflicts. However, maintaining a comprehensive understanding of the situation
on the ground is challenging, especially in conflicts that cover vast
territories and extend over long periods. This study presents a scalable and
transferable method for estimating war-induced damage to buildings. We first
train a machine learning model to output pixel-wise probability of destruction
from Synthetic Aperture Radar (SAR) satellite image time series, leveraging
existing, manual damage assessments as ground truth and cloud-based geospatial
analysis tools for large-scale inference. We further post-process these
assessments using open building footprints to obtain a final damage estimate
per building. We introduce an accessible, open-source tool that allows users to
adjust the confidence interval based on their specific requirements and use
cases. Our approach enables humanitarian organizations and other actors to
rapidly screen large geographic regions for war impacts. We provide two
publicly accessible dashboards: a Ukraine Damage Explorer to dynamically view
our pre-computed estimates, and a Rapid Damage Mapping Tool to easily run our
method and produce custom maps.

通过使用合成孔径雷达卫星图像时间序列，结合现有的手动损伤评估和云端地理空间分析工具作为基本真实数据，本研究提出了一种可扩展且可复制的方法，用于估算战争导致的建筑损害，并通过使用开放式建筑物边界对这些评估进行后处理，使人道主义组织和其他相关方能够快速筛查大范围的地理区域中的战争影响。

在乌克兰使用 Sentinel-1 时间序列进行大规模战争破坏地图制作的开源工具

An Open-Source Tool for Mapping War Destruction at Scale in Ukraine  using Sentinel-1 Time Series

During natural and man-made disasters, people use social media platforms such
as Twitter to post textual and multime- dia content to report updates about
injured or dead people, infrastructure damage, and missing or found people
among other information types. Studies have revealed that this on- line
information, if processed timely and effectively, is ex- tremely useful for
humanitarian organizations to gain situational awareness and plan relief
operations. In addition to the analysis of textual content, recent studies have
shown that imagery content on social media can boost disaster response
significantly. Despite extensive research that mainly focuses on textual
content to extract useful information, limited work has focused on the use of
imagery content or the combination of both content types. One of the reasons is
the lack of labeled imagery data in this domain. Therefore, in this paper, we
aim to tackle this limitation by releasing a large multi-modal dataset
collected from Twitter during different natural disasters. We provide three
types of annotations, which are useful to address a number of crisis response
and management tasks for different humanitarian organizations.

通过收集自然灾害期间 Twitter 上的大量多模态数据集，为人道主义组织提供了有用的三种注释类型以解决危机响应和管理任务，特别关注社交媒体上的图像内容在减灾救灾中的作用。