PseudoAugment: Enabling Smart Checkout Adoption for New Classes Without Human Annotation

Nesteruk, Sergey; Illarionova, Svetlana; Zherebzov, Ilya; Traweek, Claire; Mikhailova, Nadezhda; Somov, Andrey; Oseledets, Ivan

doi:10.1109/access.2023.3296854

Cited by 4 publications

(1 citation statement)

References 71 publications

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“…When satellite images with higher spatial resolution are available, one can adjust an initial markup with lower spatial resolution to train a more precise CNN model without additional demands to annotated data [59]. A lack of well-annotated data can be reduced by advanced approaches that generate both images and their corresponding labels or that leverage classification markup instead of semantic segmentation to automatically refine the labels for a segmentation task [60,61]. Our approach can be also supplemented by super-resolution techniques [62].…”

Section: Discussionmentioning

confidence: 99%

Flood Extent and Volume Estimation Using Remote Sensing Data

Popandopulo,

Illarionova,

Shadrin

et al. 2023

Remote Sensing

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Floods are natural events that can have a significant impacts on the economy and society of affected regions. To mitigate their effects, it is crucial to conduct a rapid and accurate assessment of the damage and take measures to restore critical infrastructure as quickly as possible. Remote sensing monitoring using artificial intelligence is a promising tool for estimating the extent of flooded areas. However, monitoring flood events still presents some challenges due to varying weather conditions and cloud cover that can limit the use of visible satellite data. Additionally, satellite observations may not always correspond to the flood peak, and it is essential to estimate both the extent and volume of the flood. To address these challenges, we propose a methodology that combines multispectral and radar data and utilizes a deep neural network pipeline to analyze the available remote sensing observations for different dates. This approach allows us to estimate the depth of the flood and calculate its volume. Our study uses Sentinel-1, Sentinel-2 data, and Digital Elevation Model (DEM) measurements to provide accurate and reliable flood monitoring results. To validate the developed approach, we consider a flood event occurred in 2021 in Ushmun. As a result, we succeeded to evaluate the volume of that flood event at 0.0087 km3. Overall, our proposed methodology offers a simple yet effective approach to monitoring flood events using satellite data and deep neural networks. It has the potential to improve the accuracy and speed of flood damage assessments, which can aid in the timely response and recovery efforts in affected regions.

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