War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

Aimaiti, Yusupujiang; Sanon, Christina; Koch, Magaly; Baise, Laurie G.; Moaveni, Babak

doi:10.3390/rs14246239

Cited by 22 publications

(5 citation statements)

References 46 publications

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“…Moreover, the classification of damaged degree also has a certain degree of subjectivity in this study; the accuracy of damage results obtained by inversion of the empirical model obtained by visual interpretation and statistics still has great room for improvement. In fact, the humanitarian disaster survey results released by UNITAR are a valuable reference, and we also note that these data have been used as an assessment basis in relevant studies [20,22]. Figure 15 shows the damaged buildings result of Mariupol interpreted and released by UNITAR according to the worldview-2 image on 14 March 2022 [51].…”

Section: Discussionmentioning

confidence: 99%

“…The advantage of all-sky and all-weather makes up for the deficiency of optical sensors affected by observation conditions [19]. Aimaiti, Y et al used the change of SAR intensity and texture analysis of optical images to detect the damage level of buildings in the Kiev area during the Russia-Ukraine conflict, achieving a detection accuracy of 58% [20]. Washaya P et al used Sentinel-1 to monitor the coherent changes of natural and man-made disasters occurring in Syria, Iran and other regions, and combined land use type data and coherent map standard deviation to reveal the changes in building damage at street level [21].…”

Section: Introductionmentioning

confidence: 99%

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Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR

et al. 2023

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Modern armed conflicts can cause serious humanitarian disasters, and remote sensing technology is critical in monitoring war crimes and assessing post-war damage. In this study, a constrained energy minimization algorithm incorporating the feature bands (IFB-CEM) is designed to detect urban burning areas in optical images. Due to the difficulty of obtaining the ground survey data of the battlefield, the dual-polarization normalized coherence index (DPNCI) is designed based on the multi-temporal synthetic aperture radar (SAR) image, and the quantitative inversion and evaluation of the destruction of urban architecture are combined with the public images on the Internet. The results show that the burning area is widely distributed in the armed conflict region, and the distribution is most concentrated around the Azovstal steel and iron works. The burning area reached its peak around 22 March, and its change is consistent with the conflict process in time and space. About 79.2% of the buildings in the city were severely damaged or completely destroyed, and there was a significant correlation with burning exposure. The results of this study show that publicly available medium-resolution remote sensing data and Internet information have the ability to respond quickly to the damage assessment of armed conflict and can provide preliminary reference information for dealing with humanitarian disasters.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR

et al. 2023

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“…C HANGE detection (CD) based on remote sensing (RS) images refers to using images of one temporal phase as a benchmark to detect the differences in other registered reference images [1]. This technology is widely used in many applications, such as land use CD [2], disaster damage extraction [3], ecosystem monitoring [4], and battle damage assessment [5], [6].…”

Section: Introductionmentioning

confidence: 99%

Remote-Sensing Image Change Detection Based on Adjacent-Level Feature Fusion and Dense Skip Connections

Huang,

Xu,

Zhang

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, the application of deep learning in remote sensing (RS) image change detection (CD) has been deepening, especially in the challenges faced in lightweight model design, that is, how to effectively balance model complexity and detection accuracy. Although some simplified models have achieved compression in existing research, there are shortcomings in maintaining detailed information and improving detection performance. On the other hand, large network architectures limit their actual deployment in resource-limited environments. This paper innovatively proposes a lightweight CD network called MFCF-Net (Multi-level Feature Cross-Fusing Network) to address this issue. MFCF-Net uses a lightweight convolutional neural network for feature extraction and introduces the Adjacent Layer Enhancement Module (ALEM) in the feature extraction stage to significantly enhance the network's ability to extract high-dimensional features while only moderately increasing parameters. Furthermore, MFCF-Net integrates dense skip connections and cross-attention mechanisms in the decoder to efficiently propagate contextual information and accurately capture and fuse global spatial features, especially for detecting small changes. Experimental results show that on four public RS CD datasets, MFCF-Net achieves excellent detection performance and has a smaller model size (with parameters as low as 1.35M). Particularly on the WHU-CD dataset, compared with the current state-of-the-art method, MFCF-Net achieves significant improvements, with an F1-score increase of 1.4 % and an IoU improvement of 2.45%.

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“…Synthetic aperture radar (SAR) plays an important role in earth observation because of its all-time and all-weather characteristics. In recent years, high-resolution (HR) spaceborne SAR sensors (e.g., TerraSAR-X [1], Cosmo-SkyMed [2], and GaoFen-3 [3]) have acquired a large number of SAR images with meter or submeter resolutions, which are widely applied to urban information extraction, such as road detection [4,5], building footprint extraction [6], 3D reconstruction [1,3], building change detection [7], and damage assessment [2,8]. Building extraction is a key step of urban information analysis, which is helpful for urban planning and disaster monitoring.…”

Section: Introductionmentioning

confidence: 99%

Individual High-Rise Building Extraction from Single High-Resolution SAR Image Based on Part Model

Liu,

Li,

Hong

et al. 2024

Remote Sensing

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Building extraction plays an important role in urban information analysis, which is helpful for urban planning and disaster monitoring. With the improvement of SAR resolution, rich detailed information in urban areas is revealed, but the discretized features also pose challenges for object detection. This paper addresses the problem of individual high-rise building extraction based on single high-resolution SAR image. Different from previous methods that require building facades to be presented in specific appearances, the proposed method is suitable for extraction of various types of high-rise buildings. After analyzing the SAR images of many types of high-rise buildings, we establish a unified high-rise building part model, on the basis of a scattering mechanism of building structures, to describe the facade characteristics of high-rise buildings, including a facade regularity part, facade bright line part, double bounce part, and their spatial topological relationships. A three-level high-rise building extraction framework, named HRBE-PM, is proposed based on the high-rise building part model. At the pixel level, a modified spot filter is used to extract bright spots and bright lines of different scales simultaneously to obtain salient features. At the structure level, building parts are generated based on the salient features according to semantic information. At the object level, spatial topological information between parts is introduced to generate building hypotheses. We define two indicators, completeness and compactness, to comprehensively evaluate each building hypothesis and select the optimal ones. After postprocessing, the final high-rise building extraction results are obtained. Experiments on two TerraSAR-X images show that the high-rise building extraction precision rate of the HRBE-PM method is above 85.29%, the recall rate is above 82.95%, and the F1-score is above 0.87. The results indicate that the HRBE-PM method can accurately extract individual high-rise buildings higher than 24 m in most dense scenes, regardless of building types.

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War Related Building Damage Assessment in Kyiv, Ukraine, Using Sentinel-1 Radar and Sentinel-2 Optical Images

Cited by 22 publications

References 46 publications

Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR

Monitoring Urban Change in Conflict from the Perspective of Optical and SAR Satellites: The Case of Mariupol, a City in the Conflict between RUS and UKR

Remote-Sensing Image Change Detection Based on Adjacent-Level Feature Fusion and Dense Skip Connections

Individual High-Rise Building Extraction from Single High-Resolution SAR Image Based on Part Model

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