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The article presents the results of seismoacoustic studies carried out in Petrozavodsk Bay of Lake Onega. The goal was to study a near-surface geological section. Seismoacoustic studies were done by the reflection method in a modified ultra-high-resolution seismic survey. The data were office processed and interpreted: five seismic complexes were identified and correlated with information on the lake's structure, culminating in maps of all five identified reflectors covering all of Petrozavodsk Bay. It was possible to estimate the abundance and thickness of sediments corresponding to each complex. [ABSTRACT FROM AUTHOR]

The use of satellite imagery to find, count and monitor whales in remote and hard to access areas has shown some promise, but few satellite studies have, as yet, provided substantial conservation outcomes. Recent studies have shown the ability of very high-resolution satellites to detect and count previously surveyed populations of belugas and narwhals in Canada. Here we describe the detection of a large aggregation of a poorly surveyed population of belugas in the southern Kara Sea, Russia, in a region where Soviet whaling is known to have had a heavy toll on belugas. We counted over 1,100 surface belugas using very highresolution satellite imagery. As only an unknown portion of the belugas can be seen on the surface, accurately converting the surface count to an abundance estimate will need further study, but using the analog of aerial surveys we estimate that this aggregation is between -1,150-2,870 individuals. Although the species is not currently considered endangered, concern over belugas future population trends is increasing, as the species is reliant on Arctic sea ice, which is rapidly declining due to climate change. This study shows the utility of satellite imagery to discover and monitor new and little-known cetacean populations. [ABSTRACT FROM AUTHOR]

Accurate coastline position is of fundamental importance for many applications concerning coastal zone monitoring, management, and planning. For example, coastal erosion phenomena require a careful and continuous monitoring due to the dynamic nature of the coastline which can undergo sudden and significant changes in position and shape over time. Various techniques allow acquiring the coastline, among these the use of multispectral optical sensors operating from satellites is one of the most widespread. With the advent of high and very high geometric resolution (VHR) satellites, it is possible to obtain images with a pixel size of less than 1 m that allow extracting accurate coastlines. The purpose of this article is to define a fast approach to investigate the degree of accuracy of one of the most popular techniques for the automatic extraction of the coastline, based on the Normalized Difference Water Index (NDWI) use. In this study, the coastline is achieved from VHR Pléiades imagery (2 m for multispectral and 0.5 m for panchromatic). Therefore, NDWI is obtained and processed both from initial images and pan-sharpened images. The resulting coastlines are submitted to smoothing and their accuracy is therefore evaluated. For this purpose, a reference coastline is manually achieved from panchromatic image. Two different methods are proposed for coastline accuracy evaluation, both based on the geometrical analysis of the polygons generated by the intersection between the extracted coastline and the reference one. This study demonstrates that the proposed methods permit to easily evaluate the accuracy of the extracted coastline; in addition, the results confirm the effectiveness of NDWI and highlight the limited benefits of pan-sharpened images for this index application. [ABSTRACT FROM AUTHOR]

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands. [ABSTRACT FROM AUTHOR]

Mapping underwater aquatic vegetation (UVeg) is crucial for understanding the dynamics of freshwater ecosystems. The advancement of artificial intelligence (AI) techniques has shown great potential in improving the accuracy and efficiency of UVeg mapping using remote sensing data. This paper presents a comparative study of the performance of classical and modern AI tools, including logistic regression, random forest, and a visual-prompt-tuned foundational model, the Segment Anything model (SAM), for mapping UVeg by analyzing air- and space-borne images in the few-shot learning regime, i.e., using limited annotations. The findings demonstrate the effectiveness of the SAM foundation model in air-borne imagery ( G S D = 3–6 cm) with an F 1 score of 86.5 % ± 4.1 % when trained with as few as 40 positive/negative pairs of pixels, compared to 54.0 % ± 9.2 % using the random forest model and 42.8 % ± 6.2 % using logistic regression models. However, adapting SAM to space-borne images (WorldView-2 and Sentinel-2) remains challenging, and could not outperform classical pixel-wise random forest and logistic regression methods in our task. The findings presented provide valuable insights into the strengths and limitations of AI models for UVeg mapping, aiding researchers and practitioners in selecting the most suitable tools for their specific applications. [ABSTRACT FROM AUTHOR]

Remote sensing provides crucial support for building damage assessment in the wake of hurricanes. This article proposes a coupled deep learning-based model for damage assessment that leverages a large very high-resolution satellite images dataset and a flexibility of building footprint source. Convolutional Neural Networks were used to generate building footprints from pre-hurricane satellite imagery and conduct a classification of incurred damage. We emphasize the advantages of multiclass classification in comparison with traditional binary classification of damage and propose resolving dataset imbalances due to unequal damage impact distribution with a focal loss function. We also investigate differences between relying on learned features using a deep learning approach for damage classification versus a commonly used shallow machine learning classifier, Support Vector Machines, that requires manual feature engineering. The proposed model leads to an 86.3% overall accuracy of damage classification for a case event of Hurricane Michael and an 11% overall accuracy improvement from the Support Vector Machines classifier, suggesting better applicability of such an open-source deep learning-based workflow in disaster management and recovery. Furthermore, the findings can be integrated into emergency response frameworks for automated damage assessment and prioritization of relief efforts.

Our objective was to develop a method for the assessment of forest area and structural variables for cases in which the availability of representative ground reference data is poor and these data are not collected from the whole area of interest. We implemented two independent approaches to the estimation of the forest variables of a European boreal forest: (i) the computation of wall-to-wall estimates using moderate- to low-resolution VIIRS imagery from the Suomi NPP mission; and (ii) the visual interpretation of plots of samples from very high resolution (VHR) satellite data obtained via a two-stage design. Our focus was on the statistical comparison of forest resources at a country or larger level. The study area was boreal forest ranging from Norway to the Ural Mountains in Russia. We computed a seamless mosaic from 111 VIIRS images. From the mosaic, we computed predictions for the forest area, growing stock volume, height of the dominating tree layer, proportion of conifers and broadleaved trees, site fertility class, and leaf area index. The reference data for the VIIRS imagery were national forest inventory (NFI)-based raster maps from Finland. The first stage sample of VHR data included 42 images; of these, a second stage sample of 2690 plots was visually interpreted for the same variables. The forest area prediction from VIIRS for the whole study area was 1.2% higher than the VHR-based result. All other structural variable predictions using VIIRS fitted within the 95% confidence intervals computed from the VHR sample except for estimates of the main tree species groups, which were outside the limits. A comparison of VIIRS-based forest area estimates using Finnish and Swedish NFI data indicated overestimations of 10.0% points and 4.6% points, whereas the total growing stock volumes were overestimated by 8% and underestimated by 3.4%, respectively. The correlation coefficients between the VIIRS and VHR image predictions at the 42 VHR image locations varied from 0.70 to 0.85. The VIIRS maps strongly averaged the local predictions due to their coarse spatial resolutions. Based on our findings, the approach using two independent estimations yielded similar figures for the central forest variables for the European boreal forest. A model computed using reference data from a small part of the area of interest can provide satisfactory predictions for a much larger area with a similar biome. Therefore, our concept is applicable to the estimation and overall mapping of a forest area and central structural variables at regional to national levels. [ABSTRACT FROM AUTHOR]

Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index. [ABSTRACT FROM AUTHOR]

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study. [ABSTRACT FROM AUTHOR]

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment. [ABSTRACT FROM AUTHOR]

Shadow detection provides worthwhile information for remote sensing applications, e.g. building height estimation. Shadow areas are formed in the opposite side of the sunlight radiation to tall objects, and thus, solar illumination angle is required to find probable shadow areas. In recent years, Very High Resolution (VHR) imagery provides more detailed data from objects including shadow areas. In this regard, the motivation of this paper is to propose a reliable feature, Shadow Low Gradient Direction (SLGD), to automatically determine shadow and solar illumination direction in VHR data. The proposed feature is based on inherent spatial feature of fine-resolution shadow areas. Therefore, it can facilitate shadow-based operations, especially when the solar illumination information is not available in remote sensing metadata. Shadow intensity is supposed to be dependent on two factors, including the surface material and sunlight illumination, which is analyzed by directional gradient values in low gradient magnitude areas. This feature considers the sunlight illumination and ignores the material differences. The method is fully implemented on the Google Earth Engine cloud computing platform, and is evaluated on VHR data with 0.3m resolution. Finally, SLGD performance is evaluated in determining shadow direction and compared in refining shadow maps.

We present the results of very high-resolution seismic profiling (VHR) and underwater electrical resistivity tomography (ERT) measurements in the Petrozavodsk Bay of Lake Onega, which were carried out to research the structure of Quaternary sediments. According to the VHR, five seismic sequences were identified during the seismic stratigraphy analysis, and a three-layer geoelectric section was obtained using ERT. The next step was an accomplishment of 2D ERT inversion based on reflectors from the VHR data. The joint interpretation of geophysical data was completed on the basis of comparative and cluster analyses with reference to the well and known geological data. During the research new information was obtained about the physical properties of the bottom sediments. The results indicate the need to apply such an approach to the interpretation of geophysical data and the prospects for the joint application of VHR and ERT acquisitions. [ABSTRACT FROM AUTHOR]

Dual‐specificity phosphatase 3 (DUSP3), also known as Vaccinia H1‐related phosphatase, is a protein tyrosine phosphatase that typically performs its major role in the regulation of multiple cellular functions through the dephosphorylation of its diverse and constantly expanding range of substrates. Many of the substrates described so far as well as alterations in the expression or the activity of DUSP3 itself are associated with the development and progression of various types of neoplasms, indicating that DUSP3 may be an important player in oncogenesis and a promising therapeutic target. This review focuses exclusively on DUSP3's contribution to either benign or malignant melanocytic oncogenesis, as many of the established culprit pathways and mechanisms constitute DUSP3's regulatory targets, attempting to synthesize the current knowledge on the matter. The spectrum of the DUSP3 interactions analysed in this review covers substrates implicated in cellular growth, cell cycle, proliferation, survival, apoptosis, genomic stability/repair, adhesion and migration of tumor melanocytes. Furthermore, the speculations raised, based on the evidence to date, may be considered a fundament for potential research regarding the oncogenesis, evolution, management and therapeutics of melanocytic tumors. [ABSTRACT FROM AUTHOR]

The satellite-based techniques for the monitoring of extreme ice features (EIFs) in the Canadian Arctic were investigated and demonstrated using synthetic aperture radar (SAR) and electro-optical data sources. The main EIF types include large ice islands and ice-island fragments, multiyear hummock fields (MYHF) and other EIFs, such as fragments of MYHF and large, newly formed hummock fields. The main objectives for the paper included demonstration of various satellite capabilities over specific regions in the Canadian Arctic to assess their utility to detect and characterize EIFs. Stereo pairs of very-high-resolution (VHR) imagery provided detailed measurements of sea ice topography and were used as validation information for evaluation of the applied techniques. Single-pass interferometric SAR (InSAR) data were used to extract ice topography including hummocks and ice islands. Shape from shading and height from shadow techniques enable us to extract ice topography relying on a single image. A new method for identification of EIFs in sea ice based on the thermal infrared band of Landsat 8 was introduced. The performance of the methods for ice feature height estimation was evaluated by comparing with a stereo or InSAR digital elevation models (DEMs). Full polarimetric RADARSAT-2 data were demonstrated to be useful for identification of ice islands.

Shallow coral reefs ensure a wide portfolio of ecosystem services, from fish provisioning to tourism, that support more than 500 million people worldwide. The protection and sustainable management of these pivotal ecosystems require fine-scale but large-extent mapping of their 3D composition. The sub-metre spaceborne imagery can neatly produce such an expected product using multispectral stereo-imagery. We built the first 3D land-sea coral reefscape mapping using the 0.3 m superspectral WorldView-3 stereo-imagery. An array of 13 land use/land cover and sea use/sea cover habitats were classified using sea-, ground- and air-truth data. The satellite-derived topography and bathymetry reached vertical accuracies of 1.11 and 0.89 m, respectively. The value added of the eight mid-infrared (MIR) channels specific to the WorldView-3 was quantified using the classification overall accuracy (OA). With no topobathymetry, the best combination included the eight-band optical (visible + near-infrared) and the MIR8, which boosted the basic blue-green-red OA by 9.58%. The classes that most benefited from this MIR information were the land use “roof” and land cover “soil” classes. The addition of the satellite-derived topobathymetry to the optical+MIR1 produced the best full combination, increasing the basic OA by 9.73%, and reinforcing the “roof” and “soil” distinction.

Land Use/Land Cover (LULC) mapping is the first step in monitoring urban sprawl and its environmental, economic and societal impacts. While satellite imagery and vegetation indices are commonly used for LULC mapping, the limited resolution of these images can hamper object recognition for Geographic Object-Based Image Analysis (GEOBIA). In this study, we utilize very high-resolution (VHR) optical imagery with a resolution of 50 cm to improve object recognition for GEOBIA LULC classification. We focused on the city of Nice, France, and identified ten LULC classes using a Random Forest classifier in Google Earth Engine. We investigate the impact of adding Gray-Level Co-Occurrence Matrix (GLCM) texture information and spectral indices with their temporal components, such as maximum value, standard deviation, phase and amplitude from the multi-spectral and multi-temporal Sentinel-2 imagery. This work focuses on identifying which input features result in the highest increase in accuracy. The results show that adding a single VHR image improves the classification accuracy from 62.62% to 67.05%, especially when the spectral indices and temporal analysis are not included. The impact of the GLCM is similar but smaller than the VHR image. Overall, the inclusion of temporal analysis improves the classification accuracy to 74.30%. The blue band of the VHR image had the largest impact on the classification, followed by the amplitude of the green-red vegetation index and the phase of the normalized multi-band drought index.

In recent years, the demand for very high geometric resolution satellite images has increased significantly. The pan-sharpening techniques, which are part of the data fusion techniques, enable the increase in the geometric resolution of multispectral images using panchromatic imagery of the same scene. However, it is not trivial to choose a suitable pan-sharpening algorithm: there are several, but none of these is universally recognized as the best for any type of sensor, in addition to the fact that they can provide different results with regard to the investigated scene. This article focuses on the latter aspect: analyzing pan-sharpening algorithms in relation to different land covers. A dataset of GeoEye-1 images is selected from which four study areas (frames) are extracted: one natural, one rural, one urban and one semi-urban. The type of study area is determined considering the quantity of vegetation included in it based on the normalized difference vegetation index (NDVI). Nine pan-sharpening methods are applied to each frame and the resulting pan-sharpened images are compared by means of spectral and spatial quality indicators. Multicriteria analysis permits to define the best performing method related to each specific area as well as the most suitable one, considering the co-presence of different land covers in the analyzed scene. Brovey transformation fast supplies the best results among the methods analyzed in this study.

Abstract Satellites Over Seals (SOS), a project initiated in late 2016, is a crowdsourced method to determine factors behind the presence/absence patterns and to ultimately determine the global population of the Weddell seal (Leptonychotes weddellii). An iconic species, the Weddell seal is proposed to be part of the Antarctic Research and Monitoring Program required in the newly designated Ross Sea Region Marine Protected Area. This species is easy to detect via satellite imagery, due to its large size (3–4 m long, 1 m wide) and its dark color contrasting with the Antarctic coastal fast ice, where it aggregates on during breeding season. Using very high‐resolution satellite imagery (VHR; 0.31–0.60 m resolution) and the online platform Tomnod, we used VHR images from November 2010 and 2011 to cover the entirety of available fast ice around Antarctica. Before correcting for time of day or date, we searched for the presence/absence to identify a subset of where abundance estimates should be concentrated. More than 325 000 citizen scientists searched 790 VHR images, covering 268 611 km2 of fast ice, to determine the locations of seals. Algorithms ranked searchers to the degree their votes corresponded with others, a measure of searcher relative quality that we used to filter out unreliable searchers. Seal presence was detected on only 0.55% of available maps (total n = 1 116 058) within fast ice, revealing a sparse, irregular distribution. The rate of false‐negative detections was 1.7%, though false positives were high (67%), highlighting the importance of training for image interpretation to ensure differentiation between seals and landscape features (such as large rocks, ice chunks or depressions/holes in the ice). This approach not only allowed us to assess image resolution and quality, but also training, outreach and the effectiveness of this platform for introducing citizen scientists to the ecology of the Southern Ocean.

Urban tree canopy (UTC) area is an important index for evaluating the urban ecological environment; the very high resolution (VHR) images are essential for improving urban tree canopy survey efficiency. However, the traditional image classification methods often show low robustness when extracting complex objects from VHR images, with insufficient feature learning, object edge blur and noise. Our objective was to develop a repeatable method—superpixel-enhanced deep neural forests (SDNF)—to detect the UTC distribution from VHR images. Eight data expansion methods was used to construct the UTC training sample sets, four sample size gradients were set to test the optimal sample size selection of SDNF method, and the best training times with the shortest model convergence and time-consumption was selected. The accuracy performance of SDNF was tested by three indexes: F1 score (F1), intersection over union (IoU) and overall accuracy (OA). To compare the detection accuracy of SDNF, the random forest (RF) was used to conduct a control experiment with synchronization. Compared with the RF model, SDNF always performed better in OA under the same training sample size. SDNF had more epoch times than RF, converged at the 200 and 160 epoch, respectively. When SDNF and RF are kept in a convergence state, the training accuracy is 95.16% and 83.16%, and the verification accuracy is 94.87% and 87.73%, respectively. The OA of SDNF improved 10.00%, reaching 89.00% compared with the RF model. This study proves the effectiveness of SDNF in UTC detection based on VHR images. It can provide a more accurate solution for UTC detection in urban environmental monitoring, urban forest resource survey, and national forest city assessment.

The thermal behavior of two copper alloys, with 0.2 and 0.5 mass % of Mg, was analyzed after severe plastic deformation processing by Equal Channel Angular Pressing (ECAP). Both alloys were forced to be passed through a 90º inner angle ECAP die at room temperature up to 16 passes following route Bc. The thermal stability was analyzed in terms of the recrystallization kinetics by using the Various Heating Rates method, derived from the Johnson–Mehl–Avrami–Kolmogorov equation, after analyzing the Differential Scanning Calorimetry peaks for both alloys at any pass of ECAP. The calculated recrystallization parameters included the activation energies (E (kJ mol−1) ECuMg02 = 42.6 ± 19.8 ECuMg05 = 52.7 ± 13 kJ mol−1) and the kinetic exponent n which took an average value of ≈ 1.75, irrespective of the considered alloy. [ABSTRACT FROM AUTHOR]

On board the IKONOS satellite there are sensors operating in the panchromatic and multispectral range: the geometric resolution of the acquired images is higher in the first case (1 m) than in the second one (4 m); on the contrary, panchromatic images have lower spectral resolution than the latter. Pan-sharpening methods allow to reduce the pixel dimensions of the multispectral images to comply with the panchromatic resolution. In this way, it is possible to obtain enhanced detailed data in both geometric and spectral resolution. This work aims to compare the results obtained from the application of eight different pan-sharpening methods, which are totally carried out by using the raster calculator in QGIS: Multiplicative, Simple Mean, Brovey Transformation, Brovey Transformation Fast, Intensity Hue Saturation (IHS), IHS Fast, Gram-Schmidt, and Gram-Schmidt Fast. Each resulting dataset is compared with the original one to evaluate the performance of each method by the following quality indices: Correlation Coefficient (CC), Universal Image Quality Index (UIQI), Relative Average Spectral Error (RASE), Erreur Relative Global Adimensionnelle de Synthèse (ERGAS), Spatial Correlation Coefficient (SCC) and Spatial ERGAS (SERGAS); however, this is a difficult task because the quality of the fused image depends on the considered datasets. Finally, a comparison the various between methods is carried out. [ABSTRACT FROM AUTHOR]

Vaccinia H1-related phosphatase (VHR) is a dual-specific phosphatase that is a promising potential target for the treatment of many human diseases. In this work, we have proposed a series of 6-(4-chlorophenyl)-N-aryl-4-(trichloromethyl)-4H-1,3,5-oxadiazin-2-amines as potential VHR inhibitors. The SuperPred online server predicts VHR inhibition for the studied compounds with a probability of 88.88–98.51%. To establish the efficiency of binding of 4H-1,3,5-oxadiazine derivatives to the active site of VHR (PDB ID: 3F81) in the AutoDock Vina program, we have carried out molecular docking studies. According to the results, the studied compounds effectively interact with the hydrophobic region of the VHR active site due to aromatic rings and the trichloromethyl group, but the polar catalytic cavity is not involved, and therefore inhibition cannot be effective. In this regard, we have built a number of model compounds containing a sulfate group and its derivatives (methyl ester and amide) in the para-position of the arylamine fragment. According to the results of molecular docking, these compounds effectively bind to the polar catalytic cavity of the enzyme due to hydrogen bonds, but due to the relative rigidity of their molecules, hydrophobic interactions are not fully realized. Therefore, in these model compounds between the arylamine fragment and the sulfo group, we introduced a spacer with a length of one to three methylene groups. Hit compounds have been selected—2-(4-((6-(4-chlorophenyl)-4-(trichloromethyl)-4H-1,3,5-oxadiazin-2-yl)amino)phenyl)ethane-1-sulfonic acid and its amide.

Mapping and monitoring the Trees outside Forest (ToF) is gaining significance in the scientific community as they provide critical ecosystem services such as protecting soil and water resources, wildlife habitat, and aesthetics including food, fuel, and fibre. Quantifying ToF can also provide useful information for emission estimation relating to the agriculture, forests, and other land use (AFOLU) category of the Intergovernmental Panel for Climate Change (IPCC). Despite the importance of quantifying ToF, very few studies have attempted to quantify them in India's natural resource inventory programs. In this study, we focused on Haryana State, India, to inventory ToF using very high‐resolution (VHR) Indian Remote Sensing (IRS) satellite data. Haryana's landscape is interspersed with croplands and ToF, thus providing a challenging environment to test VHR satellite data's ability to quantify the diversified landscape structure. We specifically used CARTOSAT‐1 panchromatic (2.5 m) and multispectral LISS‐IV (5.8 m) datasets to quantify the vegetation and build a much‐needed database for ToF. We used a novel classification scheme based on the geometry, that is, point, line, or polygon formations, to quantify ToF at a scale of 1:10,000. The obtained results suggest ToF with linear and block formations extended to 128.83 and 20.51 km2, respectively, accounting for ~3.38% of the Total Georgraphical Area of Haryana State while point formations established 2,774,531 in numbers. This study highlights the usefulness of VHR satellite data and fused imagery to quantify ToF in highly diverse landscape of Haryana. The results will help address vital ecosystem services from ToF, including greenhouse gas emissions quantification from the Agriculture, Forests and Other LandUse (AFOLU) category. [ABSTRACT FROM AUTHOR]

As an important application in remote sensing, landcover classification remains one of the most challenging tasks in very-high-resolution (VHR) image analysis. As the rapidly increasing number of Deep Learning (DL) based landcover methods and training strategies are claimed to be the state-of-the-art, the already fragmented technical landscape of landcover mapping methods has been further complicated. Although there exists a plethora of literature review work attempting to guide researchers in making an informed choice of landcover mapping methods, the articles either focus on the review of applications in a specific area or revolve around general deep learning models, which lack a systematic view of the ever advancing landcover mapping methods. In addition, issues related to training samples and model transferability have become more critical than ever in an era dominated by data-driven approaches, but these issues were addressed to a lesser extent in previous review articles regarding remote sensing classification. Therefore, in this paper, we present a systematic overview of existing methods by starting from learning methods and varying basic analysis units for landcover mapping tasks, to challenges and solutions on three aspects of scalability and transferability with a remote sensing classification focus including (1) sparsity and imbalance of data; (2) domain gaps across different geographical regions; and (3) multi-source and multi-view fusion. We discuss in detail each of these categorical methods and draw concluding remarks in these developments and recommend potential directions for the continued endeavor.

We developed a high reliable diluter 3HFFH3 for LC compositions and a reactive mesogen (RM)‐monomer carrying a polarized UV absorption unit, chalcone. As the diluter does not carry a vinyl group, a trade‐off between response property and reliability could be solved, and we succeeded to fabricate the PSVA cell that showed fast response speed and high voltage holding ratio. In addition, we also succeeded to fabricate the PI‐less IPS cell owing to use the RM‐monomer carrying the chalcone unit combined with the diluter 3HFFH3. [ABSTRACT FROM AUTHOR]

Satellites Over Seals (SOS), a project initiated in late 2016, is a crowdsourced method to determine factors behind the presence/absence patterns and to ultimately determine the global population of the Weddell seal (Leptonychotes weddellii). An iconic species, the Weddell seal is proposed to be part of the Antarctic Research and Monitoring Program required in the newly designated Ross Sea Region Marine Protected Area. This species is easy to detect via satellite imagery, due to its large size (3–4 m long, 1 m wide) and its dark color contrasting with the Antarctic coastal fast ice, where it aggregates on during breeding season. Using very high‐resolution satellite imagery (VHR; 0.31–0.60 m resolution) and the online platform Tomnod, we used VHR images from November 2010 and 2011 to cover the entirety of available fast ice around Antarctica. Before correcting for time of day or date, we searched for the presence/absence to identify a subset of where abundance estimates should be concentrated. More than 325 000 citizen scientists searched 790 VHR images, covering 268 611 km2 of fast ice, to determine the locations of seals. Algorithms ranked searchers to the degree their votes corresponded with others, a measure of searcher relative quality that we used to filter out unreliable searchers. Seal presence was detected on only 0.55% of available maps (total n = 1 116 058) within fast ice, revealing a sparse, irregular distribution. The rate of false‐negative detections was 1.7%, though false positives were high (67%), highlighting the importance of training for image interpretation to ensure differentiation between seals and landscape features (such as large rocks, ice chunks or depressions/holes in the ice). This approach not only allowed us to assess image resolution and quality, but also training, outreach and the effectiveness of this platform for introducing citizen scientists to the ecology of the Southern Ocean. [ABSTRACT FROM AUTHOR]

While remotely sensed images of various resolutions have been widely used in identifying changes in urban and peri-urban environments, only very high resolution (VHR) imagery is capable of providing the information needed for understanding the changes taking place in remote rural environments, due to the small footprints and low density of man-made structures in these settings. However, limited by data availability, mapping man-made structures and conducting subsequent change detections in remote areas are typically challenging and thus require a certain level of flexibility in algorithm design that takes into account the specific environmental and image conditions. In this study, we mapped all buildings and corrals for two remote villages in Mozambique based on two single-date VHR images that were taken in 2004 and 2012, respectively. Our algorithm takes advantage of the presence of shadows and, through a fusion of both spectra- and object-based analysis techniques, is able to differentiate buildings with metal and thatch roofs with high accuracy (overall accuracy of 86% and 94% for 2004 and 2012, respectively). The comparison of the mapping results between 2004 and 2012 reveals multiple lines of evidence suggesting that both villages, while differing in many aspects, have experienced substantial increases in the economic status. As a case study, our project demonstrates the capability of a coupling of VHR imagery with locally adjusted classification algorithms to infer the economic development of small, remote rural settlements.

Fully convolutional networks (FCN) such as UNet and DeepLabv3+ are highly competitive when being applied in the detection of earthquake-damaged buildings in very high-resolution (VHR) remote sensing images. However, existing methods show some drawbacks, including incomplete extraction of different sizes of buildings and inaccurate boundary prediction. It is attributed to a deficiency in the global context-aware and inaccurate correlation mining in the spatial context as well as failure to consider the relative positional relationship between pixels and boundaries. Hence, a detection method for earthquake-damaged buildings based on the object contextual representations (OCR) and boundary enhanced loss (BE loss) was proposed. At first, the OCR module was separately embedded into high-level feature extractions of the two networks DeepLabv3+ and UNet in order to enhance the feature representation; in addition, a novel loss function, that is, BE loss, was designed according to the distance between the pixels and boundaries to force the networks to pay more attention to the learning of the boundary pixels. Finally, two improved networks (including OB-DeepLabv3+ and OB-UNet) were established according to the two strategies. To verify the performance of the proposed method, two benchmark datasets (including YSH and HTI) for detecting earthquake-damaged buildings were constructed according to the post-earthquake images in China and Haiti in 2010, respectively. The experimental results show that both the embedment of the OCR module and application of BE loss contribute to significantly increasing the detection accuracy of earthquake-damaged buildings and the two proposed networks are feasible and effective.

In this paper, we present LC compositions having novel reactive mesogen (RM) carrying a chalcone unit and fluorinated diluter 3HFFH3 showed excellent voltage holding ratio (VHR) after photo irradiation for RM polymerization. On the other hand, LC composition having diluter substituted by alkenyl group showed lower VHR compared with our novel LC composition. [ABSTRACT FROM AUTHOR]

This research investigates the use of scale-space theory to detect individual trees in orchards from very-high resolution (VHR) satellite images. Trees are characterized by blobs, for example, bell-shaped surfaces. Their modeling requires the identification of local maxima in Gaussian scale space, whereas location of the maxima in the scale direction provides information about the tree size. A two-step procedure relates the detected blobs to tree objects in the field. First, a Gaussian blob model identifies tree crowns in Gaussian scale space. Second, an improved tree crown model modifies this model in the scale direction. The procedures are tested on the following three representative cases: an area with vitellaria trees in Mali, an orchard with walnut trees in Iran, and one case with oil palm trees in Indonesia. The results show that the refined Gaussian blob model improves upon the traditional Gaussian blob model by effectively discriminating between false and correct detections and accurately identifying size and position of trees. A comparison with existing methods shows an improvement of 10–20% in true positive detections. We conclude that the presented two-step modeling procedure of tree crowns using Gaussian scale space is useful to automatically detect individual trees from VHR satellite images for at least three representative cases.

The increasing availability of very-high resolution (VHR;

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Purpose: Sarcopenia, or loss of muscle mass, is associated with increased morbidity and mortality in oncologic resections and several other major surgeries. Complex ventral hernia repairs (VHRs) and abdominal wall reconstruction are often performed in patients at high risk for morbidity and recurrence, though limited data exist on outcomes related to sarcopenia. We aimed to determine if sarcopenia is associated with worse outcomes in patients undergoing VHR.Methods: We reviewed patients undergoing VHRs from 2014 to 2015. Preoperative CT images were analyzed for cross-sectional muscle mass. Patients with and without sarcopenia underwent statistical analysis to evaluate differences in perioperative morbidity and hernia recurrence. Muscle indices were analyzed independently for outcomes.Results: 135 patients underwent VHR with/without fistula takedown, staged repairs or other concomitant procedures. 27% had sarcopenia (age 34-84, BMI 27-33, 62% male). Postoperative complications occurred in 43% of sarcopenic patients and 47% of non-sarcopenic patients (p = 0.70). Surgical site infections (SSI) were seen in 16% of sarcopenic patients compared to 29% without sarcopenia (p = 0.14). There was no difference in hernia recurrence between groups (p = 0.90). However, after adjusting for diabetes and BMI, a 10 cm2/m2 decrease in muscle index had 1.44 OR of postoperative complications (p < 0.05).Conclusions: Though prevalent in our population, sarcopenia was not associated with an increase in postoperative complications, surgical site occurences/infections,  or hernia recurrence when previously published oncologic sarcopenia cutoffs were utilized. Previously established sarcopenia outcomes in malignancy may be attributable to an altered metabolic state that is not present in hernia repair patients. Larger-scale studies are recommended to establish new sarcopenia cutoffs for VHRs. [ABSTRACT FROM AUTHOR]

In cities, particulate matter (PM 10), carbon dioxide (CO 2), nitrogen dioxide (NO 2), and ground-level ozone (O 3) are some of the most significant air pollutants that have negative effects on human health, vegetation, and biodiversity. The city of Kaunas (Lithuania) represents the geographical region of north-eastern Europe and the Humid continental climate zone. By using a satellite-based approach with Worldview-3 (WV-3) satellite imagery at the very-high spatial and spectral resolution, we have detected and classified the most common tree species (a total of 284,305 trees) in Kaunas with an overall accuracy of about 88% e.g., Acer spp. (10%), Aesculus hippocastanum (11%), Pinus spp. (9%), Quercus spp. (24%), and Tilia spp. (5%) in both private (e.g., residential gardens) and public areas over a large study area (56 km2). The green cover of Kaunas city represents 64% of the total study area. In 2022, the 284,305 trees detected in Kaunas city center and surrounding areas have removed approximately 2084 tons of NO 2 , 217 tons of PM 10, and 22,938 tons of CO 2 while dominant trees formed more O 3 than they absorbed (1204 tons released) mainly due to the high ozone formation potential of Quercus spp. • In urban areas, tree species contribute to mitigating air pollution in cities. • The most significant air pollutants in Kaunas are PM 10 , CO 2 , NO 2 , and ground-level O 3. • All year round, Pinus spp. are more efficient at removing PM 10 than deciduous species. • Aesculus hippocastanum has the highest CO 2 removal capacity. • Acer spp., Aesculus hippocastanum , and Tilia spp. are suitable for Kaunas greening. [ABSTRACT FROM AUTHOR]

Ionizing radiation dosimetry with thermoluminescence (TL) materials based on silicon or glass can be interesting in its potential use in radiation monitoring as the solution to the constant looking of development of new radiation detectors. In this work, TL characteristics of sodium silicate exposed to beta radiation effects were studied. TL response beta irradiated exhibited a glow curve with two peaks centered at 398 K and 473 K. Samples showed linearity from 0.55 to 13.2 Gy. TL readings after 10 times showed a repeatability with an error of less than 1%. Remain information showed significant losses during the first 24 h, but its information was almost constant after 72 h of storage. The T max -T stop method exhibited three peaks which were mathematically analyzed with a general order deconvolution finding kinetic orders close to the second order for the first peak, meanwhile the kinetic order for the second peak and third peak are close to second order. Finally, the VHR method showed anomalous TL glow curve behavior with an increasing intensity TL as the heating rate increased. • Sodium Silicate Glass is good candidate for beta radiation monitoring applications. • Tl glow curve has two main peaks. They are composed of shallow and deep traps. • Trivalent ions Pr3+ modified the TL intensity of Sodium Silicate Glass. • TL glow curve deconvolution shows three peaks. • TL behavior observed is anomalous when varying the heating rate VHR. [ABSTRACT FROM AUTHOR]

The application potential of very high resolution (VHR) remote sensing imagery has been boosted by recent developments in the data acquisition and processing ability of aerial photogrammetry. However, shadows in images contribute to problems such as incomplete spectral information, lower intensity brightness, and fuzzy boundaries, which seriously affect the efficiency of the image interpretation. In this paper, to address these issues, a simple and automatic method of shadow detection is presented. The proposed method combines the advantages of the property-based and geometric-based methods to automatically detect the shadowed areas in VHR imagery. A geometric model of the scene and the solar position are used to delineate the shadowed and non-shadowed areas in the VHR image. A matting method is then applied to the image to refine the shadow mask. Different types of shadowed aerial orthoimages were used to verify the effectiveness of the proposed shadow detection method, and the results were compared with the results obtained by two state-of-the-art methods. The overall accuracy of the proposed method on the three tests was around 90%, confirming the effectiveness and robustness of the new method for detecting fine shadows, without any human input. The proposed method also performs better in detecting shadows in areas with water than the other two methods. [ABSTRACT FROM AUTHOR]

Background: Complex ventral hernia repair (VHR) is a common surgical operation but carries a risk of complications from surgical site infections (SSI) and occurrences (SSO). We aimed to create a predictive risk score to identify patients at increased risk for SSO or SSI within 30 days of surgery.Methods: Data were prospectively collected on all patients undergoing VHR between January 2008 and February 2015 by a single surgeon. Multivariable logistic regression was used to identify independent factors predictive of SSO and SSI. Significant predictors of SSO and SSI were assigned point values based on their odds ratios to create a novel risk score, the Hopkins ventral hernia repair SSO/SSI risk score; predicted and actual rates of outcomes were then compared using weighted regression.Results: During the study period, 362 patients underwent open VHR. Thirty-day SSO and SSI occurred in 18.5 and 10% of patients, respectively. After risk adjustment, ASA class ≥3 (1 point), operative time ≥4 h (2 points), and the absence of a postoperative wound vacuum dressing (1 point) were predictive of 30-day SSO. Predicted risk of SSO utilizing this scoring system was 9.7, 19.4, 29.1, and 38.8% for 1, 2, 3, and 4 points (AUC = 0.73). For SSI, operative time ≥4 h (1 point) and the lack of a wound vacuum dressing (1 point) were predictive. Predicted risk of SSI based on this scoring system was 12.5% for 1 point and 25% for 2 points (AUC = 0.71). Actual vs. predicted rates of SSO and SSI correlated strongly for risk model with a coefficient of determination (R 2) of 0.92 and 0.91, respectively.Conclusion: The novel Hopkins ventral hernia repair risk score accurately predicts risk of SSO and SSI after complex VHR. Further studies using a prospective randomized controlled trial will be needed to further validate our findings. [ABSTRACT FROM AUTHOR]

Recent advances in remote sensing technologies and the cost reduction of surveying, along with the importance of natural resources management, present new opportunities for mapping land cover at a very high resolution over large areas. This paper proposes and applies a framework to update hyperspatial resolution (<1 m) land thematic mapping over large areas by handling multi-source and heterogeneous data. This framework deals with heterogeneity both from observation and the targeted features. First, observation diversity comes from the different platform and sensor types (25-cm passive optical and 1-m LiDAR) as well as the different instruments (three cameras and two LiDARs) used in heterogeneous observation conditions (date, time, and sun angle). Second, the local heterogeneity of the targeted features results from their within-type diversity and neighborhood effects. This framework is applied to surface water bodies in the southern part of Belgium (17,000 km2). This makes it possible to handle both observation and landscape contextual heterogeneity by mapping observation conditions, stratifying spatially and applying ad hoc classification procedures. The proposed framework detects 83% of the water bodies—if swimming pools are not taken into account—and more than 98% of those water bodies greater than 100 m2, with an edge accuracy below 1 m over large areas. [ABSTRACT FROM AUTHOR]