An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series
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.
Code (1)
Tasks
HumanitarianTime SeriesSimilar Papers 제목 키워드 기반
SCALE: A Scalable Language Engineering Toolkit
In this paper we present SCALE, a new Python toolkit that contains two extensions to n-gram language models. The first extension is a novel technique to model compound words called Semantic Head Mapping (SHM). The second…
Language ModelingLanguage ModellingDestruction from sky: Weakly supervised approach for destruction detection in satellite imagery
Natural and man-made disasters cause huge damage to built infrastructures and results in loss of human lives. The rehabilitation efforts and rescue operations are hampered by the non-availability of accurate and timely i…
Change DetectionColmap-PCD: An Open-source Tool for Fine Image-to-point cloud Registration
State-of-the-art techniques for monocular camera reconstruction predominantly rely on the Structure from Motion (SfM) pipeline. However, such methods often yield reconstruction outcomes that lack crucial scale informatio…
Image to Point Cloud RegistrationPoint Cloud RegistrationDisasterNets: Embedding Machine Learning in Disaster Mapping
Disaster mapping is a critical task that often requires on-site experts and is time-consuming. To address this, a comprehensive framework is presented for fast and accurate recognition of disasters using machine learning…
AttributeChange DetectionDomain AdaptationScene RecognitionThe Cultural Mapping and Pattern Analysis (CMAP) Visualization Toolkit: Open Source Text Analysis for Qualitative and Computational Social Science
The CMAP (cultural mapping and pattern analysis) visualization toolkit introduced in this paper is an open-source suite for analyzing and visualizing text data - from qualitative fieldnotes and in-depth interview transcr…