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Comprehensive management of urban drainage network infrastructure is essential for sustaining the operation of these systems despite stresses from component deterioration, urban densificati...

In the event of a flood, being able to build accurate flood level maps is essential for supporting emergency plan operations. In order to build such maps, it is important to collect observations from the disaster area. Social media platforms can be useful sources of information in this case, as people located in the flood area tend to share text and pictures depicting the current situation. Developing an effective and fully automatized method able to retrieve data from social media and extract useful information in real-time is crucial for a quick and proper response to these catastrophic events. In this paper, we propose a method to quantify flood-water from images gathered from social media. If no prior information about the zone where the picture was taken is available, one possible way to estimate the flood level consists of assessing how much the objects appearing in the image are submerged in water. There are various factors that make this task difficult: i) the precise size of the objects appearing in the image might not be known; ii) flood-water appearing in different zones of the image scene might have different height; iii) objects may be only partially visible as they can be submerged in water. In order to solve these problems, we propose a method that first locates selected classes of objects whose sizes are approximately known, then, it leverages this property to estimate the water level. To prove the validity of this approach, we first build a flood-water image dataset, then we use it to train a deep learning model. We finally show the ability of our trained model to recognize objects and at the same time predict correctly flood-water level., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Introduction: The association between nocturia and hypertension has been widely reported, yet remains poorly characterized, precluding a more refined understanding of blood pressure as it relates to the clinical urology setting. We synthesized current evidence on the relationship between nocturia and hypertension as a function of nocturia severity, age, sex, race, and diuretic use. Methods: We searched PubMed, EMBASE, and Cochrane databases for studies published up to May 2020. Random effects meta-analyses were performed to identify the pooled odds ratio (OR) for nocturia given the presence of hypertension. Meta-regression and subgroup analyses were performed to identify differences across demographic factors. We applied the Quality Assessment Tool for Observational Cohort and Cross-Sectional Studies to evaluate the quality of evidence. Results: Of 1530 identified studies, 23 met the criteria for inclusion. The overall pooled OR for the association of hypertension with nocturia was 1.26 (95% CI 1.22-1.29, p Conclusions: There exists an increased odds of nocturia in hypertensive individuals. This association is stronger following a higher nocturia cutoff, in females, and in some racial subgroups, but unrelated to age and diuretic utilization.

Physically-based models are important tools for evaluating the hydraulic behaviour of urban drainage systems and, more specifically, assessing flood risk. While it is well known that such models should be calibrated and validated with monitoring data, overland runoff information is seldom available for this purpose. This study investigates the potential of using surveillance camera footage to measure surface flow velocity thanks to an LSPIV-based method called Surface Structure Image Velocimetry (SSIV). Seven real-scale experiments conducted in a specialized flood training facility were used to test the SSIV method under varied and challenging conditions. SSIV performance was evaluated by benchmarking bulk (mean) velocity against that measured by a conventional sensor array. In the best conditions tested, SSIV and conventional flow sensors differed by only 1.7% (0.1% standard deviation). While the method proved sensitive to light conditions, our results suggest that infrared lighting could be used to increase measurement consistency. Our study concludes that for measuring overland flow velocity in urban areas, surveillance and traffic cameras can be considered as a low-maintenance and easy-to-install alternative to conventional sensor systems.

The data sets described in this paper provide a basis for developing and testing new methods for monitoring and modelling urban pluvial flash floods. Pluvial flash floods are a growing hazard to property and inhabitants' well-being in urban areas. However, the lack of appropriate data collection methods is often cited as an impediment for reliable flood modelling, thereby hindering the improvement of flood risk mapping and early warning systems. The potential of surveillance infrastructure and social media is starting to draw attention for this purpose. In the floodX project, 22 controlled urban flash floods were generated in a flood response training facility and monitored with state-of-the-art sensors as well as standard surveillance cameras. With these data, it is possible to explore the use of video data and computer vision for urban flood monitoring and modelling. The floodX project stands out as the largest documented flood experiment of its kind, providing both conventional measurements and video data in parallel and at high temporal resolution. The data set used in this paper is available at https://doi.org/10.5281/zenodo.830513.

Urban pluvial flood models need to be calibrated with data from actual flood events in order to validate and improve model performance. Due to the lack of conventional sensor solutions, alternative sources of data such as citizen science, social media, and surveillance cameras have been proposed in literature. Some of the methods proposed boast high scalability but without an on-site survey, they can only provide proxy measurements for physical flooding variables (such as water level). In this study, the potential value of such proxy measurements was evaluated by calibrating an urban pluvial flood model with data from experimental flood events conducted in a 25 × 25 m facility, monitored with surveillance cameras and conventional sensors in parallel. Both ideal proxy data and actual image-based proxy measurements with noise were tested, and the effects of measurement location and measurement noise were investigated separately. The results with error-free proxy data confirm the theoretic potential of such measurements, as in half of the calibration configurations tested, ideal proxy data increases model performance by at least 70% compared to sensor data. However, image-based proxy data can contain complex correlated errors, which have a complex and predominantly negative effect on performance.

OBJECTIVES: We examined factors associated with adherence to 2.3 g/d sodium intake in a population of inner-City patients from specialty clinics where sodium restriction is advised. METHODS: A random sample of 109 patients from an inner-city CKD (37), dialysis (23), medicine/diabetes (18) and transplant clinic (31) were studied. Dietary intake was assessed by 24-hour food recall, analyzed using ASA24 software and used to calculate Healthy Eating Index (HEI). Nutritional literacy was assessed via the Newest Vital Signs toolkit. Depression and anxiety were assessed using PHQ-9 and PSS scales. RESULTS: There were 41 (38%) men and 67 (62%) women with 89 black, 5 white, 3 Hispanic and 14 other. 71 (65%) pts were foreign born (time in the US 35.9 ± 15.3 yrs). 43/81 (53%) pts made 2.3 g/d (HiNa) (69.1 ± 11.4 vs 58.4 ± 15.9, P 2.3 g sodium/day. 2. Pts who ate low sodium were older, ate fewer calories overall, and scored lower for depression, anxiety, and food insecurity. 3. Pts who ate low sodium ate a higher percentage of calories from carbohydrates and less B vitamins, cured meat and grains but did not differ for fat, fat soluble vitamins, vit C, vegetables, eggs, dairy or added sugar. 3. The contribution of depression/anxiety, stress and food insecurity to higher sodium intake is unclear, but warrants further study. FUNDING SOURCES: none.

In many countries, urban flooding due to local, intense rainfall is expected to become more frequent because of climate change and urbanization. Cities trying to adapt to this growing risk are challenged by a chronic lack of surface flooding data that are needed for flood risk assessment and planning. In this work, we propose a new approach that exploits existing surveillance camera systems to provide qualitative flood level trend information at scale. The approach uses a deep convolutional neural network (DCNN) to detect floodwater in surveillance footage and a novel qualitative flood index (namely, the static observer flooding index – SOFI) as a proxy for water level fluctuations visible from a surveillance camera's viewpoint. To demonstrate the approach, we trained the DCNN on 1218 flooding images collected from the Internet and applied it to six surveillance videos representing different flooding and lighting conditions. The SOFI signal obtained from the videos had a 75 % correlation to the actual water level fluctuation on average. By retraining the DCNN with a few frames from a given video, the correlation is increased to 85 % on average. The results confirm that the approach is versatile, with the potential to be applied to a variety of surveillance camera models and flooding situations without the need for on-site camera calibration. Thanks to this flexibility, this approach could be a cheap and highly scalable alternative to conventional sensing methods., Hydrology and Earth System Sciences, 23 (11), ISSN:1027-5606, ISSN:1607-7938

Precise and detailed digital elevation models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM, such as airplane light detection and ranging (lidar) DEMs and point and contour maps, remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of unmanned aerial vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, 14 UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch, and (iv) weather conditions. In addition, we compared the best-quality UAV DEM to a conventional lidar-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to lidar-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g. buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional lidar-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local, and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.

The promise of collecting and utilizing large amounts of data has never been greater in the history of urban water management (UWM). This paper reviews several data-driven approaches which play a key role in bringing forward a sea change. It critically investigates whether data-driven UWM offers a promising foundation for addressing current challenges and supporting fundamental changes in UWM. We discuss the examples of better rain-data management, urban pluvial flood-risk management and forecasting, drinking water and sewer network operation and management, integrated design and management, increasing water productivity, wastewater-based epidemiology and on-site water and wastewater treatment. The accumulated evidence from literature points toward a future UWM that offers significant potential benefits thanks to increased collection and utilization of data. The findings show that data-driven UWM allows us to develop and apply novel methods, to optimize the efficiency of the current network-based approach, and to extend functionality of today's systems. However, generic challenges related to data-driven approaches (e.g., data processing, data availability, data quality, data costs) and the specific challenges of data-driven UWM need to be addressed, namely data access and ownership, current engineering practices and the difficulty of assessing the cost benefits of data-driven UWM.

Vorgestellt an der Aqua Urbanica 2018.

Sewer and drainage infrastructure are often not as well catalogued as they should be, considering the immense investment they represent. In this work, we present a fully automatic framework for localizing sewer inlets from image clouds captured from an unmanned aerial vehicle (UAV). The framework exploits the high image overlap of UAV imaging surveys with a multiview approach to improve detection performance. The framework uses a Viola–Jones classifier trained to detect sewer inlets in aerial images with a ground sampling distance of 3–3.5 cm/pixel. The detections are then projected into three-dimensional space where they are clustered and reclassified to discard false positives. The method is evaluated by cross-validating results from an image cloud of 252 UAV images captured over a 0.57-km2 study area with 228 sewer inlets. Compared to an equivalent single-view detector, the multiview approach improves both recall and precision, increasing average precision from 0.65 to 0.73. The source code and case study data are publicly available for reuse.

OBJECTIVES: We examined prevalence and impact of food insecurity and use of SNAP benefits in a cohort of inner-city patients with diabetes. METHODS: 57 patients with diabetes from the CKD, Transplant, Dialysis, Diabetes, and Internal Medicine Clinics at an inner-city hospital were interviewed by face-to-face technique. Food security was assessed using the U.S. Adult Food Security Survey Module and scored from 1–4 (1–2 is food insecure, 3 is at risk). 24-hour food recall was evaluated using ASA24 software. Stress was assessed using the PSS stress scale. RESULTS: Mean age was 67.3 ± 10.87 yrs, with 21 men (36%), 36 women (64%), 82% black or African American, 61% high school education or less and 81% had household income

OBJECTIVES: We examined the association between social determinants including food insecurity and social support in a minority, ethnic population with CKD. METHODS: A face-to-face survey was conducted in patients from an inner-city CKD (37), dialysis (23) and transplant clinic (31), including questionnaires to assess social support (MOS survey, scored 1–5, with 1 being poor support), perceived stress (PSS), and food security using the short form food security scale, with a score of 1–4 (very low to adequate). Patients were divided into adequate food security (score of 4) and inadequate food security (score of

OBJECTIVES: Adherence to the DASH (Dietary Approaches to Stop Hypertension) diet is associated with slower progression of kidney disease and decreased cardiovascular risk. We evaluated the association between knowledge of the DASH diet, DASH scores and nutrient intake in an inner-City population. METHODS: A random sample of patients from CKD (37), medicine/diabetes (18) and transplant clinic (31) was studied using 24-hour food recall, with nutrient intake analyzed by ASA24 software used to calculate DASH score. Patients were asked to respond to the question “Do you know what the DASH diet is?” RESULTS: Mean age was 63.8 ± 14.1yrs; 50% (43) had diabetes; 85% (73) had hypertension. 45% (39) reported income

Precise and detailed Digital Elevation Models (DEMs) are essential to accurately predict overland flow in urban areas. Unfortunately, traditional sources of DEM remain a bottleneck for detailed and reliable overland flow models, because the resulting DEMs are too coarse to provide DEMs of sufficient detail to inform urban overland flows. Interestingly, technological developments of Unmanned Aerial Vehicles (UAVs) suggest that they have matured enough to be a competitive alternative to satellites or airplanes. However, this has not been tested so far. In this this study we therefore evaluated whether DEMs generated from UAV imagery are suitable for urban drainage overland flow modelling. Specifically, fourteen UAV flights were conducted to assess the influence of four different flight parameters on the quality of generated DEMs: (i) flight altitude, (ii) image overlapping, (iii) camera pitch and (iv) weather conditions. In addition, we compared the best quality UAV DEM to a conventional Light Detection and Ranging (LiDAR)-based DEM. To evaluate both the quality of the UAV DEMs and the comparison to LiDAR-based DEMs, we performed regression analysis on several qualitative and quantitative metrics, such as elevation accuracy, quality of object representation (e.g., buildings, walls and trees) in the DEM, which were specifically tailored to assess overland flow modelling performance, using the flight parameters as explanatory variables. Our results suggested that, first, as expected, flight altitude influenced the DEM quality most, where lower flights produce better DEMs; in a similar fashion, overcast weather conditions are preferable, but weather conditions and other factors influence DEM quality much less. Second, we found that for urban overland flow modelling, the UAV DEMs performed competitively in comparison to a traditional LiDAR-based DEM. An important advantage of using UAVs to generate DEMs in urban areas is their flexibility that enables more frequent, local and affordable elevation data updates, allowing, for example, to capture different tree foliage conditions.

In 2004, pilots reported 46 laser illumination events to the Federal Aviation Administration (FAA), with the number increasing to approximately 3,600 in 2011. Since that time, the number of reported laser incidents has ranged from 3,500 to 4,000. Previous studies indicate the potential for flight crewmember distraction from bright laser light being introduced to the cockpit. Compositional variations of the photoresponsive nanocomposite coatings were applied to an aircraft windscreen using a modified liquid dispersion/heating curing process. The attenuating effects of the deposited films on laser light intensity were evaluated using an optical power meter and the resultant laser intensity data through treated and untreated windscreens was collected. Data revealed a reduction in laser intensity (36–88%) in the presence of the engineered photoresponsive nanocomposite films. Results lend support of the view that the addition of transparent laser attenuating films applied to aircraft windscreens may improve flight safety, and reduce the risk from distraction or disruption of flight crewmembers’ vision.