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Papers by Nazar Sotiriadi

Research paper thumbnail of ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks

Doklady. Mathematics, Dec 1, 2023

Research paper thumbnail of Long-Term Drought Prediction Using Deep Neural Networks Based on Geospatial Weather Data

Research paper thumbnail of Flood Extent and Volume Estimation Using Remote Sensing Data

Remote Sensing, Sep 10, 2023

Floods are natural events that can have a significant impacts on the economy and society of affec... more 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 km 3 . 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.

Research paper thumbnail of Assessing the Risk of Permafrost Degradation with Physics-Informed Machine Learning

arXiv (Cornell University), Oct 2, 2023

Global warming accelerates permafrost degradation, impacting the reliability of critical infrastr... more Global warming accelerates permafrost degradation, impacting the reliability of critical infrastructure used by more than five million people daily. Furthermore, permafrost thaw produces substantial methane emissions, further accelerating global warming and climate change and putting more than eight billion people at additional risk. To mitigate the upcoming risk, policymakers and stakeholders must be given an accurate prediction of the thaw development. Unfortunately, comprehensive physics-based permafrost models require location-specific fine-tuning that is challenging in practice. Models of intermediate complexity require few input parameters but have relatively low accuracy. The performance of pure data-driven models is low as well as the observational data is sparse and limited. In this work, we designed a physicsinformed machine-learning approach for permafrost thaw prediction. The method uses a heat equation to regularize data-driven approach trained over permafrost monitoring data and climate projections. The latter leads to higher precision and better numerical stability allowing for reliable decision-making

Research paper thumbnail of Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

IEEE access, 2024

The United Nations has identified improving food security and reducing hunger as essential compon... more The United Nations has identified improving food security and reducing hunger as essential components of its sustainable development goals. As of 2022, approximately 735 million people worldwide are experiencing hunger and malnutrition, with numerous fatalities reported. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages and subsequent social and political conflicts. To address this issue, we have developed a machine learning-based approach to predict the risk of substantial land suitability degradation and changes in irrigation patterns. Our study focuses on Central Eurasia, a region burdened with economic and social challenges. This study is among the first to employ interpretable machine learning methods to assess the impact of climate change on agricultural land suitability under various carbon emission scenarios. The feature importance analysis reveals specific climate and terrain characteristics that may influence land suitability. The efficacy of our model is demonstrated through its performance metrics, achieving an accuracy of 86% and a mean average precision of 72% in a multi-class land suitability classification task. Tackling the most vulnerable regions in Eastern Europe and Northern Asia offers policymakers valuable insights for making informed decisions and preventing a humanitarian crisis, such as supplying additional water and fertilizers. This study highlights the potential of machine learning in addressing global challenges, particularly in reducing hunger and malnutrition.

Research paper thumbnail of Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

arXiv (Cornell University), Oct 23, 2023

Research paper thumbnail of Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

Remote Sensing

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manu... more Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenge...

Research paper thumbnail of ESGify: Automated Classification of Environmental, Social, and Corporate Governance Risks

Doklady. Mathematics, Dec 1, 2023

Research paper thumbnail of Long-Term Drought Prediction Using Deep Neural Networks Based on Geospatial Weather Data

Research paper thumbnail of Flood Extent and Volume Estimation Using Remote Sensing Data

Remote Sensing, Sep 10, 2023

Floods are natural events that can have a significant impacts on the economy and society of affec... more 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 km 3 . 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.

Research paper thumbnail of Assessing the Risk of Permafrost Degradation with Physics-Informed Machine Learning

arXiv (Cornell University), Oct 2, 2023

Global warming accelerates permafrost degradation, impacting the reliability of critical infrastr... more Global warming accelerates permafrost degradation, impacting the reliability of critical infrastructure used by more than five million people daily. Furthermore, permafrost thaw produces substantial methane emissions, further accelerating global warming and climate change and putting more than eight billion people at additional risk. To mitigate the upcoming risk, policymakers and stakeholders must be given an accurate prediction of the thaw development. Unfortunately, comprehensive physics-based permafrost models require location-specific fine-tuning that is challenging in practice. Models of intermediate complexity require few input parameters but have relatively low accuracy. The performance of pure data-driven models is low as well as the observational data is sparse and limited. In this work, we designed a physicsinformed machine-learning approach for permafrost thaw prediction. The method uses a heat equation to regularize data-driven approach trained over permafrost monitoring data and climate projections. The latter leads to higher precision and better numerical stability allowing for reliable decision-making

Research paper thumbnail of Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

IEEE access, 2024

The United Nations has identified improving food security and reducing hunger as essential compon... more The United Nations has identified improving food security and reducing hunger as essential components of its sustainable development goals. As of 2022, approximately 735 million people worldwide are experiencing hunger and malnutrition, with numerous fatalities reported. Climate change significantly impacts agricultural land suitability, potentially leading to severe food shortages and subsequent social and political conflicts. To address this issue, we have developed a machine learning-based approach to predict the risk of substantial land suitability degradation and changes in irrigation patterns. Our study focuses on Central Eurasia, a region burdened with economic and social challenges. This study is among the first to employ interpretable machine learning methods to assess the impact of climate change on agricultural land suitability under various carbon emission scenarios. The feature importance analysis reveals specific climate and terrain characteristics that may influence land suitability. The efficacy of our model is demonstrated through its performance metrics, achieving an accuracy of 86% and a mean average precision of 72% in a multi-class land suitability classification task. Tackling the most vulnerable regions in Eastern Europe and Northern Asia offers policymakers valuable insights for making informed decisions and preventing a humanitarian crisis, such as supplying additional water and fertilizers. This study highlights the potential of machine learning in addressing global challenges, particularly in reducing hunger and malnutrition.

Research paper thumbnail of Climate Change Impact on Agricultural Land Suitability: An Interpretable Machine Learning-Based Eurasia Case Study

arXiv (Cornell University), Oct 23, 2023

Research paper thumbnail of Benchmark for Building Segmentation on Up-Scaled Sentinel-2 Imagery

Remote Sensing

Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manu... more Currently, we can solve a wide range of tasks using computer vision algorithms, which reduce manual labor and enable rapid analysis of the environment. The remote sensing domain provides vast amounts of satellite data, but it also poses challenges associated with processing this data. Baseline solutions with intermediate results are available for various tasks, such as forest species classification, infrastructure recognition, and emergency situation analysis using satellite data. Despite these advances, two major issues with high-performing artificial intelligence algorithms remain in the current decade. The first issue relates to the availability of data. To train a robust algorithm, a reasonable amount of well-annotated training data is required. The second issue is the availability of satellite data, which is another concern. Even though there are a number of data providers, high-resolution and up-to-date imagery is extremely expensive. This paper aims to address these challenge...