Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery
- URL: http://arxiv.org/abs/2405.06323v1
- Date: Fri, 10 May 2024 08:50:08 GMT
- Title: Open Access Battle Damage Detection via Pixel-Wise T-Test on Sentinel-1 Imagery
- Authors: Ollie Ballinger,
- Abstract summary: 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.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: In the context of recent, highly destructive conflicts in Gaza and Ukraine, reliable estimates of building damage are essential for an informed public discourse, human rights monitoring, and humanitarian aid provision. Given the contentious nature of conflict damage assessment, these estimates must be fully reproducible, explainable, and derived from open access data. This paper introduces a new method for building damage detection-- the Pixel-Wise T-Test (PWTT)-- that satisfies these conditions. Using a combination of freely-available synthetic aperture radar imagery and statistical change detection, the 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. Despite being simple and lightweight, the algorithm achieves building-level accuracy statistics (AUC=0.88 across Ukraine, 0.81 in Gaza) rivalling state of the art methods that use deep learning and high resolution imagery. The workflow is open source and deployed entirely within the Google Earth Engine environment, allowing for the generation of interactive Battle Damage Dashboards for Ukraine and Gaza that update in near-real time, allowing the public and humanitarian practitioners to immediately get estimates of damaged buildings in a given area.
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) - An Open-Source Tool for Mapping War Destruction at Scale in Ukraine using Sentinel-1 Time Series [16.900687593159066]
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.
arXiv Detail & Related papers (2024-06-04T17:24:19Z) - QuickQuakeBuildings: Post-earthquake SAR-Optical Dataset for Quick Damaged-building Detection [5.886875818210989]
This letter presents the first dataset dedicated to detecting earthquake-damaged buildings from post-event very high resolution (VHR) Synthetic Aperture Radar (SAR) and optical imagery.
We deliver a dataset of coregistered building footprints and satellite image patches of both SAR and optical data, encompassing more than four thousand buildings.
arXiv Detail & Related papers (2023-12-11T18:19:36Z) - Causality-informed Rapid Post-hurricane Building Damage Detection in
Large Scale from InSAR Imagery [6.331801334141028]
Timely and accurate assessment of hurricane-induced building damage is crucial for effective post-hurricane response and recovery efforts.
Recently, remote sensing technologies provide large-scale optical or Interferometric Synthetic Aperture Radar (InSAR) imagery data immediately after a disastrous event.
These InSAR imageries often contain highly noisy and mixed signals induced by co-occurring or co-located building damage, flood, flood/wind-induced vegetation changes, as well as anthropogenic activities.
This paper introduces an approach for rapid post-hurricane building damage detection from InSAR imagery.
arXiv Detail & Related papers (2023-10-02T18:56:05Z) - 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) - A Multi-purpose Real Haze Benchmark with Quantifiable Haze Levels and
Ground Truth [61.90504318229845]
This paper introduces the first paired real image benchmark dataset with hazy and haze-free images, and in-situ haze density measurements.
This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene.
A subset of this dataset has been used for the Object Detection in Haze Track of CVPR UG2 2022 challenge.
arXiv Detail & Related papers (2022-06-13T19:14:06Z) - Rethinking Drone-Based Search and Rescue with Aerial Person Detection [79.76669658740902]
The visual inspection of aerial drone footage is an integral part of land search and rescue (SAR) operations today.
We propose a novel deep learning algorithm to automate this aerial person detection (APD) task.
We present the novel Aerial Inspection RetinaNet (AIR) algorithm as the combination of these contributions.
arXiv Detail & Related papers (2021-11-17T21:48:31Z) - 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) - ETH-XGaze: A Large Scale Dataset for Gaze Estimation under Extreme Head
Pose and Gaze Variation [52.5465548207648]
ETH-XGaze is a new gaze estimation dataset consisting of over one million high-resolution images of varying gaze under extreme head poses.
We show that our dataset can significantly improve the robustness of gaze estimation methods across different head poses and gaze angles.
arXiv Detail & Related papers (2020-07-31T04:15:53Z) - 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.