Aviation dentistry: New horizon, new challenge

After the innovations in aviation at the beginning of the 20th century, many in-flight pathologic and physiologic conditions were reported. Changing atmospheric pressure, especially if it is rapid, can cause discomfort and damage to the oral cavities and maxillofacial areas unless the pressure within these cavities containing gas is able to equilibrate with the external air pressure. Out of these conditions - barodontalgia (pain due to gas entrapment) and barotrauma (pressure induced tooth fracture, restoration and tooth fracture) was most frequently seen to occur. Due to partial pressurization of aeroplanes’ cabins and improvement in dental techniques and oral health awareness, prevalence of flight-related oral manifestations has declined. It is important for the dental practitioners to be familiar with these facts and not to neglect dental education with respect to aviation. Aircrew patients as well as air passengers often make it challenging for the dentist to treat several flight-related conditions. Correct diagnosis should be made before these conditions lead to serious complications. With thorough practice and experience, the aircrew are now able to avoid, or treat, these pressure related problem

Multi-sensor remote sensing analysis of coal fire induced land subsidence in Jharia Coalfields, Jharkhand, India

The subsidence in coal mines induced by surface and subsurface fires leading to roof collapse, infrastructure loss, and loss of lives is a prominent concern. In the study, satellite imagery from thermal and microwave remote sensing data is used to deduce the effect of coal fires on subsidence in the Jharia Coalfields, India. The Thermal Infrared data acquired from the Landsat-8 (band 10) is used to derive the temperature anomaly maps. Persistent Scatterer Interferometry analysis was performed on sixty Sentinel-1, C-band images, the results are corrected for atmospheric error using Generic Atmospheric Correction Online Service for InSAR (GACOS) atmospheric modelling data and decomposed into vertical displacement values to quantify subsidence. A zone-wise analysis of the hazard patterns in the coalfields was carried out. Coal fire maps, subsidence velocity maps, and land cover maps were integrated to investigate the impact of the hazards on the mines and their surroundings. Maximum subsidence of approximately 20 cm/yr. and temperature anomaly of up to 25 °C has been observed. The findings exhibit a strong positive correlation between the subsidence velocity and temperature anomaly in the study area. Kusunda, Keshalpur, and Bararee collieries are identified as the most critically affected zones. The subsidence phenomenon in some collieries is extending towards the settlements and transportation networks and needs urgent intervention. © 2021 The Author

Automatic Flood Detection from Sentinel-1 Data Using a Nested UNet Model and a NASA Benchmark Dataset

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results

Tracking hidden crisis in India's capital from space: implications of unsustainable groundwater use.

Funder: Helmholtz-Zentrum Potsdam Deutsches GeoForschungsZentrum - GFZNational Capital Region (NCR, Delhi) in India is one of the fastest-growing metropolitan cities which is facing a severe water crisis due to increasing water demand. The over-extraction of groundwater, particularly from its unconsolidated alluvial deposits makes the region prone to subsidence. In this study, we investigated the effects of plummeting groundwater levels on land surface elevations in Delhi NCR using Sentinel-1 datasets acquired during the years 2014-2020. Our analysis reveals two distinct subsidence features in the study area with rates exceeding 11 cm/year in Kapashera-an urban village near IGI airport Delhi, and 3 cm/year in Faridabad throughout the study period. The subsidence in these two areas are accelerating and follows the depleting groundwater trend. The third region, Dwarka shows a shift from subsidence to uplift during the years which can be attributed to the strict government policies to regulate groundwater use and incentivizing rainwater harvesting. Further analysis using a classified risk map based on hazard risk and vulnerability approach highlights an approximate area of 100 square kilometers to be subjected to the highest risk level of ground movement, demanding urgent attention. The findings of this study are highly relevant for government agencies to formulate new policies against the over-exploitation of groundwater and to facilitate a sustainable and resilient groundwater management system in Delhi NCR

Deep learning, remote sensing and visual analytics to support automatic flood detection

Floods can have devastating consequences on people, infrastructure, and the ecosystem. Satellite imagery has proven to be an efficient instrument in supporting disaster management authorities during flood events. In contrast to optical remote sensing technology, Synthetic Aperture Radar (SAR) can penetrate clouds, and authorities can use SAR images even during cloudy circumstances. A challenge with SAR is the accurate classification and segmentation of flooded areas from SAR imagery. Recent advancements in deep learning algorithms have demonstrated the potential of deep learning for image segmentation demonstrated. Our research adopted deep learning algorithms to classify and segment flooded areas in SAR imagery. We used UNet and Feature Pyramid Network (FPN), both based on EfficientNet-B7 implementation, to detect flooded areas in SAR imaginary of Nebraska, North Alabama, Bangladesh, Red River North, and Florence. We evaluated both deep learning methods' predictive accuracy and will present the evaluation results at the conference. In the next step of our research, we develop an XAI toolbox to support the interpretation of detected flooded areas and algorithmic decisions of the deep learning methods through interactive visualizations

Automatic flood detection from Sentinel-1 data using a nested UNet model and a NASA benchmark dataset

During flood events near real-time, synthetic aperture radar (SAR) satellite imagery has proven to be an efficient management tool for disaster management authorities. However, one of the challenges is accurate classification and segmentation of flooded water. A common method of SAR-based flood mapping is binary segmentation by thresholding, but this method is limited due to the effects of backscatter, geographical area, and surface characterstics. Recent advancements in deep learning algorithms for image segmentation have demonstrated excellent potential for improving flood detection. In this paper, we present a deep learning approach with a nested UNet architecture based on a backbone of EfficientNet-B7 by leveraging a publicly available Sentinel‑1 dataset provided jointly by NASA and the IEEE GRSS Committee. The performance of the nested UNet model was compared with several other UNet-based convolutional neural network architectures. The models were trained on flood events from Nebraska and North Alabama in the USA, Bangladesh, and Florence, Italy. Finally, the generalization capacity of the trained nested UNet model was compared to the other architectures by testing on Sentinel‑1 data from flood events of varied geographical regions such as Spain, India, and Vietnam. The impact of using different polarization band combinations of input data on the segmentation capabilities of the nested UNet and other models is also evaluated using Shapley scores. The results of these experiments show that the UNet model architectures perform comparably to the UNet++ with EfficientNet-B7 backbone for both the NASA dataset as well as the other test cases. Therefore, it can be inferred that these models can be trained on certain flood events provided in the dataset and used for flood detection in other geographical areas, thus proving the transferability of these models. However, the effect of polarization still varies across different test cases from around the world in terms of performance; the model trained with the combinations of individual bands, VV and VH, and polarization ratios gives the best results

Unlocking the Full Potential of Sentinel-1 for Flood Detection in Arid Regions

Climate change has intensified flooding in arid and semi-arid regions, presenting a major challenge for flood monitoring and mapping. While satellites, particularly Synthetic Aperture Radar (SAR), allow synoptically observing flood extents, accurately differentiating between sandy terrains and water for arid region flooding remains an open challenge. Current global flood mapping products exclude arid areas from their analyses due to the sand and water confusion, resulting in a critical lack of observations which impedes response and recovery in these vulnerable regions. This paper explores the full potential of Sentinel-1 SAR to improve near-real-time flood mapping in arid and semi-arid regions. By investigating the impact of various parameters such as polarization, temporal information, and interferometric coherence, the most important information sources for detecting arid floods were identified. Using three distinct arid flood events in Iran, Pakistan, and Turkmenistan, different scenarios were constructed and tested using RF to evaluate the effectiveness of each feature. Permutation feature importance analysis was additionally conducted to identify key elements that reduce computational costs and enable a faster response during emergencies. Fusing VV coherence and amplitude information in pre-flood and post-flood imagery provedThe authors gratefully acknowledge the EPSRC and BP for funding this research through the EPSRC Centre for Doctoral Training in Future Infrastructure and Built Environment: Resilience in a Changing World (Grant number: EP/S02302X/1) and to the Helmholtz Artificial Intelligence Cooperation Unit for partial funding under the project “AI for Near Real-Time Satellite-based Flood Response” (AI4FLOOD), Helmholtz Information and Data Science Academy. Dr. Sivasakthy Selvakumaran was supported by the Isaac Newton Trust. Additionally, we acknowledge ESA Copernicus for granting access to the Sentinel-1 and Sentinel-2 datasets, which were instrumental in this research

Improving SAR-based flood detection in arid regions using texture features

Flood monitoring in arid regions is challenging using Synthetic Aperture Radar (SAR) due to the similar backscatter of water and dry sand in surrounding areas. Since textural information is abundant in SAR images, this study investigates the added value of texture in SAR-based flood detection by providing it as auxiliary information for flood delineation. Results show that texture enhanced SAR images in VH polarization substantially underpredicts the flooded area, so adding texture does not improve the classification accuracy. However, using both polarization (VV and VH) produce ca. 26% higher overall accuracy for flood detection in arid regions

Artificial Intelligence for flood analysis: first results from the AI4Flood project

Floods are one of the most frequent and the costliest natural disasters. Accurate and rapid mapping of flooded areas becomes more crucial when floods strike densely populated cities. A cost-effective and widely used tool for near real-time flood monitoring is satellite remote sensing. Water can be easily discriminated from other land covers in optical satellite imagery owing to the spectral behavior in visible and infrared ranges of wavelength. However, the optical sensors’ major limitation is their inability to penetrate clouds, resulting in images with missing information and restricting their further use for flood monitoring. In the last decade, Synthetic Aperture Radar (SAR) has played a significant role in operational services for flood management and has been used by agencies worldwide. SAR is an active imaging technique that overcomes problems of optical sensors and provides day and night cloud-free images. The specular reflection from smooth water surfaces makes the water appear black in SAR images. Although SAR amplitude has been widely used operationally in flood detection and monitoring, it is subjected to overestimation of flooded areas, particularly in the arid and semi-arid regions. This is due to the similarity between the radar backscatter over sand and open water surfaces. Interferometric coherence and polarimetric information can overcome this problem by providing complementary information and further refining flood events. Also, advanced machine learning and deep learning approaches have demonstrated the potential to learn from the current data and improve the classification accuracy as well as reduce response time and model development cost. In this study, we present preliminary results of our research obtained within the AI4Flood project (AI for Near Real Time Satellite-based Flood Response), funded within the framework of the 2019 call of Helmholtz AI Projects. The main goal of the project is to improve existing satellite-based emergency mapping methods based on SAR data by training, testing, and validating novel machine learning algorithms that incorporate information from amplitude and coherence of SAR data together with polarimetric decomposition for the semantic segmentation of water bodies in case of flood situations. As the first case study, we focus on southern Iran and present the results obtained for the January 2020 flood event in the arid region of Sistan and Baluchestan. Here we exploit a year-long time series of amplitude, interferometric coherence, and polarimetric decomposition products, such as Entropy (H), and Alpha (α) derived from multi-temporal Sentinel-1 SAR data. In addition, optical imagery from Sentinel-2 was also considered for visualization and validation purposes. We observed that in some areas, the backscatter variations were not high enough to determine the changes during the flood; however, a clear drop-off in coherence was noticed. Similarly, the information stored in H, and α provides complementary information that can be used to detect flooded areas, which are otherwise not possible just by using SAR amplitude. This information, along with Sentinel-2 optical imagery, will be used to train, validate, and test Convolutional Neural Networks (CNNs) to segment permanent and flood water. The aim of the AI4Flood project is to provide a machine learning framework to better detect the flooded areas by using information derived from freely available optical and SAR imagery

Perioperative anaesthetic concerns in transgender patients: Indian perspective

Medical care of transgender patients is not only legally bound but also ethically required. Globally, 0.5%–0.9% of the adult population exhibits a gender different from their birth sex, but there is a dearth of transgender-friendly hospitals stemming from ignorance to disdain for this marginalised community. With gradually increasing acceptance of the transgender patients in the society, healthcare professionals must gear up to deal with issues specific and unique to this group of population. These concerns remain important to understand for an optimal perioperative care. The medical concerns transcend international boundaries, whereas legal, social, economic and psychological concerns vary from place to place. There is a need for modification of curriculum and training for healthcare personnel to foster sensitivity and empathy in patient dealing, to allow for an unbiased optimal healthcare. Such patients require a thorough assessment in a comfortable environment considering their specific needs. A plan for perioperative care needs to be done and discussed with the patient and the perioperative care team as well. There is scarce literature with regard to perioperative care in the transgender patients and hence requires more research