Search Results (31)

Solar exposure and shading critically influence outdoor thermal comfort in residential areas, yet quantitative links between spatial morphology and microclimate remain insufficiently explored in cold-region cities. This study proposes a novel morphological indicator, the Insolation Shadow Ratio (ISR), to quantify sunlight–shade dynamics and investigates its correlation with outdoor thermal comfort (UTCI) in Xi’an, China. Combining field observations, microclimate simulations, and statistical analysis, we quantified ISR and UTCI across three representative outdoor spaces in a residential area. Photographic analysis and spatial parameterization were employed to calculate hourly ISR values. Significant correlations were observed between ISR and UTCI values. The measured data showed the strongest correlation at summer solstice at site C (Spearman’s r = 0.883, p < 0.01). GAM analysis of seasonal peak correlation data revealed that an optimal UTCI comfort range of 9 °C to 26 °C, corresponding to ISR thresholds of 0.0202–0.8384, achieved the highest autumn correlation at site C (r = 0.686, p < 0.01), while effectively balancing shade cooling effects and solar accessibility. The ISR framework provides a quantifiable tool for designers to optimize outdoor thermal environments and, when enhanced by parametric modeling tools, enables them to proactively optimize thermal performance during early-stage residential planning, offering a data-driven pathway for climate-resilient outdoor space design. Full article

Background: Anatomically accurate illustrations are imperative in medical education, serving as crucial tools to facilitate comprehension of complex anatomical structures. While traditional illustration methods involving human artists remain the gold standard, the rapid advancement of Generative Artificial Intelligence (GAI) models presents a new opportunity to automate and accelerate this process. This study evaluated the potential of GAI models to produce craniofacial anatomy illustrations for educational purposes. Methods: Four GAI models, including Midjourney v6.0, DALL-E 3, Gemini Ultra 1.0, and Stable Diffusion 2.0 were used to generate 736 images across multiple views of surface anatomy, bones, muscles, blood vessels, and nerves of the cranium in both oil painting and realistic photograph styles. Four reviewers evaluated the images for anatomical detail, aesthetic quality, usability, and cost-effectiveness. Inter-rater reliability analysis assessed evaluation consistency. Results: Midjourney v6.0 scored highest for aesthetic quality and cost-effectiveness, and DALL-E 3 performed best for anatomical detail and usability. The inter-rater reliability analysis demonstrated a high level of agreement among reviewers (ICC = 0.858, 95% CI). However, all models showed significant flaws in depicting crucial anatomical details such as foramina, suture lines, muscular origins/insertions, and neurovascular structures. These limitations were further characterized by abstract depictions, mixing of layers, shadowing, abnormal muscle arrangements, and labeling errors. Conclusions: These findings highlight GAI’s potential for rapidly creating craniofacial anatomy illustrations but also its current limitations due to inadequate training data and incomplete understanding of complex anatomy. Refining these models through precise training data and expert feedback is vital. Ethical considerations, such as potential biases, copyright challenges, and the risks of propagating inaccurate information, must also be carefully navigated. Further refinement of GAI models and ethical safeguards are essential for safe use. Full article

Agrivoltaics (APVs) represent a growing technology in Europe that enables the co-location of energy and food production in the same field. Photosynthesis requires photosynthetic active radiation, which is reduced by the shadows cast on crops by APV panels. The design of the module rows, material, and field orientation significantly influences the radiation distribution on the ground. In this context, we introduce an innovative approach for the effective simulation of the shading effects of various APV designs. We performed an extensive sensitivity analysis of the photovoltaic (PV) geometry influence on the ground-incident radiation and crop growth of selected cultivars. Simulations (2013–2021) for three representative arable crops in eastern Austria (winter wheat, spring barley, and maize) and seven different APV designs that only limited to the shading effect showed that maize and spring barley experienced the greatest annual above-ground biomass and grain yield reduction (up to 25%), with significant differences between the APV design and the weather conditions. While spring barley had similar decreases within the years, maize was characterized by high variability. Winter wheat had only up to a 10% reduction due to shading and a reduced photosynthetic performance. Cold/humid/cloudy weather during the growing season had more negative yield effects under APVs than dry/hot periods, particularly for summer crops such as maize. The lowest grain yield decline was achieved for all three crops in the APV design in which the modules were oriented to the east at a height of 5 m and mounted on trackers with an inclination of +/−50°. This scenario also resulted in the highest land equivalent ratios (LERs), with values above 1.06. The correct use of a tracker on APV fields is crucial for optimizing agricultural yields and electricity production. Full article

Schmitt’s map was one of the outstanding survey products of the late 18th century, produced through Habsburg military mapping in the shadow of the Napoleonic Wars in the area of today’s southern Germany and some neighboring regions. The main geodetic basis for the map work was the series of surveys in Germany conducted by C.-F. Cassini de Thury in the 1760s. However, this was only a horizontal control for part of Schmitt’s map. The Cassini survey chains were linked in the 1790s by a complementary survey in the northern part of the map work: the Staržinsky-Sarret survey, which is the subject of this study. The authors have searched through the archive summary drafts of this survey. The georeferencing of the photographed sketches in the Cassini projection was feasible with surprisingly low error. By using the global SRTM elevation database, it was possible to identify the points/summits of the Staržinsky-Sarret survey between which visibility is possible. Thus, despite the fact that only one of the seven map sketches examined explicitly presents a triangulation structure, we present a possible triangulation pattern that could have been used to provide geodetic control in the northern part of the Schmitt map. The authors consider this survey as the basis for the assumption that georeferencing the Schmitt map in its own projection is possible in this area with relatively small residual errors. Full article

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R² = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha⁻¹, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R² = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels. Full article

Side-scan sonar is designed and used for a variety of survey work, in both military and civilian fields. These systems provide acoustic imageries that play a significant role in a variety of marine and inland applications. For this reason, it is extremely important that the recorded sonar image is characterized by high resolution, detail and sharpness. This article is mainly aimed at the demonstration of the impact of side-scan sonar resolution on the imaging quality. The article also presents the importance of acoustic shadow in the process of analyzing sonar data and identifying underwater objects. The real measurements were carried out using two independent survey systems: hull-mounted sonar and towed side-scan sonar. Six different shipwrecks lying in the Baltic Sea were selected as the objects of research. The results presented in the article also constitute evidence of how the sonar technology has changed over time. The survey findings show that by maintaining the appropriate operational conditions and meeting several requirements, it is possible to obtain photographic-quality sonar images, which may be crucial in the process of data interpretation and shipwreck identification. Full article

The physical and chemical characteristics of the microwave discharge in petroleum solvent during hydrogen production processes involving Ar, He, and CO₂ barbotage were studied. Gas chromatography, emission spectroscopy, high-speed photography, and shadow photography were used for diagnosis. The results demonstrated the dependence of hydrogen yield on the flow rates of Ar, He, and CO₂. The maximum yield values of hydrogen were 791 mL/min and 811 mL/min, while the maximum energy efficiency reached 135.6 NL/kWh and 162.2 NL/kWh in Nefras with Ar and He barbotage, respectively. The dynamics of discharge structure and the rotational and vibrational temperatures of C₂ molecules were studied. Full article

Building footprint (BFP) extraction focuses on the precise pixel-wise segmentation of buildings from aerial photographs such as satellite images. BFP extraction is an essential task in remote sensing and represents the foundation for many higher-level analysis tasks, such as disaster management, monitoring of city development, etc. Building footprint extraction is challenging because buildings can have different sizes, shapes, and appearances both in the same region and in different regions of the world. In addition, effects, such as occlusions, shadows, and bad lighting, have to also be considered and compensated. A rich body of work for BFP extraction has been presented in the literature, and promising research results have been reported on benchmarking datasets. Despite the comprehensive work performed, it is still unclear how robust and generalizable state-of-the-art methods are to different regions, cities, settlement structures, and densities. The purpose of this study is to close this gap by investigating questions on the practical applicability of BFP extraction. In particular, we evaluate the robustness and generalizability of state-of-the-art methods as well as their transfer learning capabilities. Therefore, we investigate in detail two of the most popular deep learning architectures for BFP extraction (i.e., SegNet, an encoder–decoder-based architecture and Mask R-CNN, an object detection architecture) and evaluate them with respect to different aspects on a proprietary high-resolution satellite image dataset as well as on publicly available datasets. Results show that both networks generalize well to new data, new cities, and across cities from different continents. They both benefit from increased training data, especially when this data is from the same distribution (data source) or of comparable resolution. Transfer learning from a data source with different recording parameters is not always beneficial. Full article

Plot-level photography is an attractive time-saving alternative to field measurements for vegetation monitoring. However, widespread adoption of this technique relies on efficient workflows for post-processing images and the accuracy of the resulting products. Here, we estimated relative vegetation cover using both traditional field sampling methods (point frame) and semi-automated classification of photographs (plot-level photography) across thirty 1 m² plots near Utqiaġvik, Alaska, from 2012 to 2021. Geographic object-based image analysis (GEOBIA) was applied to generate objects based on the three spectral bands (red, green, and blue) of the images. Five machine learning algorithms were then applied to classify the objects into vegetation groups, and random forest performed best (60.5% overall accuracy). Objects were reliably classified into the following classes: bryophytes, forbs, graminoids, litter, shadows, and standing dead. Deciduous shrubs and lichens were not reliably classified. Multinomial regression models were used to gauge if the cover estimates from plot-level photography could accurately predict the cover estimates from the point frame across space or time. Plot-level photography yielded useful estimates of vegetation cover for graminoids. However, the predictive performance varied both by vegetation class and whether it was being used to predict cover in new locations or change over time in previously sampled plots. These results suggest that plot-level photography may maximize the efficient use of time, funding, and available technology to monitor vegetation cover in the Arctic, but the accuracy of current semi-automated image analysis is not sufficient to detect small changes in cover. Full article

The task of CO₂ decomposition is one of the components of the problem associated with global warming. One of the promising directions of its solution is the use of low-temperature plasma. For these purposes, different types of discharges are used. Microwave discharge in liquid hydrocarbons has not been studied before for this problem. This paper presents the results of a study of microwave discharge products in liquid Nefras C2 80/120 (petroleum solvent, a mixture of light hydrocarbons with a boiling point from 33 to 205 °C) when CO₂ is introduced into the discharge zone, as well as the results of a study of the discharge by optical emission spectroscopy and shadow photography methods. The main gas products are H₂, C₂H₂, C₂H₄, CH₄, CO₂, and CO. No oxygen was found in the products. The mechanisms of CO₂ decomposition in the discharge are considered. The formation of H₂ occurs simultaneously with the decomposition of CO₂ in the discharge, with a volumetric rate of up to 475 mL/min and energy consumption of up to 81.4 NL/kWh. Full article

This paper studies the correlation between different macroscopic features of image regions and object properties with the Sauter diameter (D₃₂) of bubble size in flotation. Bubbles were sampled from the collection zone of a two-dimensional flotation cell using a McGill Bubble Size Analyzer, and photographed bubbles were processed using image analysis. The Sauter mean diameters were obtained under different experimental conditions using a semiautomated methodology, in which non-identifiable bubbles were manually characterized to estimate the bubble size distribution. For the same processed images, different image properties from their binary representation were studied in terms of their correlation with D₃₂. The median and variability of the shadow percentage, aspect ratio, power spectral density, perimeter, equivalent diameters, solidity, and circularity, among other image or object properties, were studied. These properties were then related to the measured D₃₂ values, from which four predictors were chosen to obtain a multivariable model that adequately described the Sauter diameter. After removing abnormal gas dispersion conditions, the multivariable linear model was able to represent D₃₂ values (99 datasets) for superficial gas rates in the range of 0.4–2.5 cm/s, for four types of frothers and surfactant concentrations ranging from 0 to 32 ppm. The model was tested with 72 independent datasets, showing the generalizability of the results. Thus, the approach proved to be applicable at the laboratory scale for D₃₂ = 1.3–6.7 mm. Full article

For millennia, the coastal heathlands of Western Europe were managed by regular burning cycles for improved grazing. In recent decades, this practice has generally been neglected. In Norway, the result is accumulation of degenerated heather and highly combustible Juniperus communis (juniper) encroachment, i.e., an increasing fire threat to a rising number of homes in the wildland–urban interface (WUI). In the present study, goats grazing a 1.2 ha site partly encroached by fire-prone juniper were studied in a living lab approach. Twelve wethers (castrated male goats) wearing solar-powered Nofence GPS collars were virtually fenced to protect villa gardens bordering the site during eight weeks from 18 April 2022. Besides some early operator errors, tree shadows and cloudy days limiting battery charging, the system worked well. Photographs taken prior to and after the grazing revealed that only 39% of the junipers had 90+% remaining foliage while for 41%, the foliage was reduced to below 10%, making them far less fire-prone. The goats were frequently visited by neighborhood residents of all ages, school classes, and kindergartens. Moreover, local newspapers and TV stations broadcasted the initiative. Although divided regarding debarked deciduous trees, the neighbors stated that the goats were well accepted. Despite some limited loss of garden plants, all interviewed residents bordering the site were positive about the goats “bringing life to the forest” and, if possible, welcomed them back for other grazing periods. Virtually fenced grazing goats may represent a sustainable solution for reducing fire hazards in the WUI also elsewhere. Full article

The vast digital archives collected by optical remote sensing observations over a long period of time can be used to determine changes in the land surface and this information can be very useful in a variety of applications. However, accurate change extraction requires highly accurate image-to-image registration, which is especially true when the target is urban areas in high-resolution remote sensing images. In this paper, we propose a new method for automatic registration between images that can be applied to noisy images such as old aerial photographs taken with analog film, in the case where changes in man-made objects such as buildings in urban areas are extracted from multitemporal high-resolution remote sensing images. The proposed method performs image-to-image registration by applying template matching to road masks extracted from images using a two-step deep learning model. We applied the proposed method to multitemporal images, including images taken more than 36 years before the reference image. As a result, the proposed method achieved registration accuracy at the subpixel level, which was more accurate than the conventional area-based and feature-based methods, even for image pairs with the most distant acquisition times. The proposed method is expected to provide more robust image-to-image registration for differences in sensor characteristics, acquisition time, resolution and color tone of two remote sensing images, as well as to temporal variations in vegetation and the effects of building shadows. These results were obtained with a road extraction model trained on images from a single area, single time period and single platform, demonstrating the high versatility of the model. Furthermore, the performance is expected to be improved and stabilized by using images from different areas, time periods and platforms for training. Full article

The article focuses on utilizing unmanned aerial vehicles (UAV) to capture and classify building façades of various forms of cultural sites and structures. We propose a Pareto-optimized deep learning algorithm for building detection and classification in a congested urban environment. Outdoor image processing becomes difficult in typical European metropolitan situations due to dynamically changing weather conditions as well as various objects obscuring perspectives (wires, overhangs, posts, other building parts, etc.), therefore, we also investigated the influence of such ambient “noise”. The approach was tested on 8768 UAV photographs shot at different angles and aimed at very different 611 buildings in the city of Vilnius (Wilno). The total accuracy was 98.41% in clear view settings, 88.11% in rain, and 82.95% when the picture was partially blocked by other objects and in the shadows. The algorithm’s robustness was also tested on the Harward UAV dataset containing images of buildings taken from above (roofs) while our approach was trained using images taken at an angle (façade still visible). Our approach was still able to achieve acceptable 88.6% accuracy in building detection, yet the network showed lower accuracy when assigning the correct façade class as images lacked necessary façade information. Full article

Since the 1970s, artificial reef (AR) deployment has been one of the common approaches to augment the local production of coastal fish in Malaysia. However, there is a lack of strategy to assess and evaluate the success of pre-deployed AR. Realizing the need for an effective qualitative and quantitative monitoring and evaluation of AR, this study proposes side-scan sonar coupled with scuba diving observation for the enhanced monitoring of pre-deployed Bioceramic Korean (BK) AR along the coast of Terengganu, Peninsular Malaysia. This method employs an integrated side-scan sonar with a data acquisition system for the detection of the precise location of the BK AR which were deployed at Tukun Telaga Batin off the Terengganu coastline in 2003 by the Fisheries Development Authority of Malaysia (FDAM). Following the determination of the exact location of three BK AR areas, a scuba diving team was sent to survey the physical conditions of the AR and assess the feasibility of the AR as an artificial habitat for marine life. The scan sonar images were compared with the captured photographs and videos acquired during the scuba diving session for a comprehensive value-added assessment. The collected data from the scuba diving session were also processed to measure the fish diversity, the fish biomass, and identify the fish community surrounding the AR. Based on the results, the BK AR remained in extremely good condition, physically intact, without significant breakage or damage, and with no major subsidence into the sediment after 19 years of deployment. A total of 108 BK AR modules were detected in three large groups using the mosaicked sonar images. Following cross-examination to validate the AR measurements using the AR’s shadows on the seafloor, the height of the BK AR consistently ranged from 1.7 m to 1.8 m. In addition, 34 species of fish from 14 families and 26 genera were identified, with most species classified as reef-associated (RA) fish with a biomass value of 1173.31 ± 1136.69 kg m⁻³, indicating the successfulness of the BK AR as fish-aggregating devices (FADs). The growth of hard corals on top of the bioceramic plate of the AR module further supported the diversified community of marine life surrounding the BK AR. In conclusion, the side-scan sonar coupled with scuba diving observation demonstrates an effective qualitative and quantitative technique for the monitoring and evaluation of pre-deployed AR. This strategy is recommended to be utilized to determine the success of pre-deployment AR. Full article