An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series
- URL: http://arxiv.org/abs/2406.02506v1
- Date: Tue, 4 Jun 2024 17:24:19 GMT
- Title: An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series
- Authors: Olivier Dietrich, Torben Peters, Vivien Sainte Fare Garnot, Valerie Sticher, Thao Ton-That Whelan, Konrad Schindler, Jan Dirk Wegner,
- Abstract summary: 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.
We then post-process these assessments using open building footprints to obtain a final damage estimate per building.
- Score: 16.900687593159066
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: 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.
Related papers
- Building Damage Assessment in Conflict Zones: A Deep Learning Approach Using Geospatial Sub-Meter Resolution Data [9.146968506196446]
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.
This is the first study to use sub-meter resolution imagery to assess building damage in combat zones.
arXiv Detail & Related papers (2024-10-07T07:26:38Z) - Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning [11.697034536189094]
We present an innovative algorithm that constructs Neyman stratified random sampling trees for binary classification.
Our findings demonstrate that our method surpasses both passive and conventional active learning techniques.
It effectively addresses the'sampling bias' challenge in traditional active learning strategies.
arXiv Detail & Related papers (2024-05-28T01:34:35Z) - Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery [0.0]
This paper introduces a new method for building damage detection.
The Pixel-Wise T-Test (PWTT) generates accurate conflict damage estimates across a wide area at regular time intervals.
Accuracy is assessed using an original dataset of over half a million labeled building footprints spanning 12 cities across Ukraine, Palestine, Syria, and Iraq.
arXiv Detail & Related papers (2024-05-10T08:50:08Z) - Building Coverage Estimation with Low-resolution Remote Sensing Imagery [65.95520230761544]
We propose a method for estimating building coverage using only publicly available low-resolution satellite imagery.
Our model achieves a coefficient of determination as high as 0.968 on predicting building coverage in regions of different levels of development around the world.
arXiv Detail & Related papers (2023-01-04T05:19:33Z) - Embedding Earth: Self-supervised contrastive pre-training for dense land
cover classification [61.44538721707377]
We present Embedding Earth a self-supervised contrastive pre-training method for leveraging the large availability of satellite imagery.
We observe significant improvements up to 25% absolute mIoU when pre-trained with our proposed method.
We find that learnt features can generalize between disparate regions opening up the possibility of using the proposed pre-training scheme.
arXiv Detail & Related papers (2022-03-11T16:14:14Z) - ADAPT: An Open-Source sUAS Payload for Real-Time Disaster Prediction and
Response with AI [55.41644538483948]
Small unmanned aircraft systems (sUAS) are becoming prominent components of many humanitarian assistance and disaster response operations.
We have developed the free and open-source ADAPT multi-mission payload for deploying real-time AI and computer vision onboard a sUAS.
We demonstrate the example mission of real-time, in-flight ice segmentation to monitor river ice state and provide timely predictions of catastrophic flooding events.
arXiv Detail & Related papers (2022-01-25T14:51:19Z) - Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting [91.69900691029908]
We advocate for predicting both the individual motions as well as the scene occupancy map.
We propose a Scene-Actor Graph Neural Network (SA-GNN) which preserves the relative spatial information of pedestrians.
On two large-scale real-world datasets, we showcase that our scene-occupancy predictions are more accurate and better calibrated than those from state-of-the-art motion forecasting methods.
arXiv Detail & Related papers (2021-01-07T06:08:21Z) - Assessing out-of-domain generalization for robust building damage
detection [78.6363825307044]
Building damage detection can be automated by applying computer vision techniques to satellite imagery.
Models must be robust to a shift in distribution between disaster imagery available for training and the images of the new event.
We argue that future work should focus on the OOD regime instead.
arXiv Detail & Related papers (2020-11-20T10:30:43Z) - Monitoring War Destruction from Space: A Machine Learning Approach [1.0149624140985478]
Existing data on building destruction in conflict zones rely on eyewitness reports or manual detection.
This article introduces an automated method of measuring destruction in high-resolution satellite images using deep learning techniques.
We apply this method to the Syrian civil war and the evolution of damage in major cities across the country.
arXiv Detail & Related papers (2020-10-12T19:01:20Z) - RescueNet: Joint Building Segmentation and Damage Assessment from
Satellite Imagery [83.49145695899388]
RescueNet is a unified model that can simultaneously segment buildings and assess the damage levels to individual buildings and can be trained end-to-end.
RescueNet is tested on the large scale and diverse xBD dataset and achieves significantly better building segmentation and damage classification performance than previous methods.
arXiv Detail & Related papers (2020-04-15T19:52:09Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.