Hasil untuk "Maps"

V. Rusch, H. Asamura, Hirokazu Watanabe et al.

P. Stothard, D. Wishart

G. Foody

The accuracy of thematic maps derived by image classification analyses is often compared in remote sensing studies. This comparison is typically achieved by a basic subjective assessment of the observed difference in accuracy but should be undertaken in a statistically rigorous fashion. One approach for the evaluation of the statistical significance of a difference in map accuracy that has been widely used in remote sensing research is based on the comparison of the kappa coefficient of agreement derived for each map. The conventional approach to the comparison of kappa coefficients assumes that the samples used in their calculation are independent, an assumption that is commonly unsatisfied because the same sample of ground data sites is often used for each map. Alternative methods to evaluate the statistical significance of differences in accuracy are available for both related and independent samples. Approaches for map comparison based on the kappa coefficient and proportion of correctly allocated cases, the two most widely used metrics of thematic map accuracy in remote sensing, are discussed. An example illustrates how classifications based on the same sample of ground data sites may be compared rigorously and highlights the importance of distinguishing between one- and two-sided statistical tests in the comparison of classification accuracy statements.

Günter P. Wagner, L. Altenberg

Qi Li, Yue Wang, Yilun Wang et al.

Constructing HD semantic maps is a central component of autonomous driving. However, traditional pipelines require a vast amount of human efforts and resources in annotating and maintaining the semantics in the map, which limits its scalability. In this paper, we introduce the problem of HD semantic map learning, which dynamically constructs the local semantics based on onboard sensor observations. Meanwhile, we introduce a semantic map learning method, dubbed HDMapNet. HDMapNet encodes image features from surrounding cameras and/or point clouds from LiDAR, and predicts vectorized map elements in the bird's-eye view. We benchmark HDMapNet on nuScenes dataset and show that in all settings, it performs better than baseline methods. Of note, our camera-LiDAR fusion-based HDMapNet outperforms existing methods by more than 50 % in all metrics. In addition, we develop semantic-level and instance-level metrics to evaluate the map learning performance. Finally, we showcase our method is capable of predicting a locally consistent map. By introducing the method and metrics, we invite the community to study this novel map learning problem.

Adam Porter

This lesson demonstrates how to visualize data through choropleth maps using Python and the Folium library. It discusses common problems encountered with choropleth maps and explains how to add interactive elements and save the maps for sharing.

Yang Gao, Mingdu Luo, Hongmiao Wang et al.

Abstract Metabolic reactions play important roles in organisms such as providing energy, transmitting signals, and synthesizing biomacromolecules. Charting unknown metabolic reactions in cells is hindered by limited technologies, restricting the holistic understanding of cellular metabolism. Using mass spectrometry-resolved stable-isotope tracing metabolomics, we develop an isotopologue similarity networking strategy, namely IsoNet, to effectively deduce previously unknown metabolic reactions. The strategy uncovers ~300 previously unknown metabolic reactions in living cells and mice. Specifically, we elaborately chart the metabolic reaction network related to glutathione, unveiling three previously unreported reactions nestled within glutathione metabolism. Among these, a transsulfuration reaction, synthesizing γ-glutamyl-seryl-glycine directly from glutathione, underscores the role of glutathione as a sulfur donor. Functional metabolomics studies systematically characterize biochemical effects of previously unknown reactions in glutathione metabolism, showcasing their diverse functions in regulating cellular metabolism. Overall, these newly uncovered metabolic reactions fill gaps in the metabolic network maps, facilitating exploration of uncharted territories in cellular biochemistry.

V. Longhi, A. Martino, A. M. Lingua et al.

Globalisation has contributed to rapid economic growth but has also exposed vulnerabilities such as the spread of pests in agriculture. An example is the <em>Popillia Japonica Newman</em> beetle, introduced to Italy in 2014, which has caused significant economic losses, mainly affecting vine cultures. Reliable identification of pests is essential for its management, but it is time-consuming and laborious. This has prompted growing interest in image-based methods, supported by computer vision (CV), which can significantly improve efficiency in insect detection. This study aims to evaluate a CV algorithm's effectiveness in identifying adult specimens of <em>Popillia</em> using Near-Infrared sensors on Uncrewed Aerial Systems (UAS). The project, conducted in two vineyards in northern Italy, intends to establish a replicable and standardised data acquisition protocol for future monitoring activities. Insects detected by the CV-based method are validated by manual counting performed by entomologists. In a GIS environment, prescription maps are generated in near real-time to identify where the vineyard is most affected and to guide the drone spraying treatment only on the areas in which the threshold is exceeded. The study demonstrates effective semi-automated monitoring, with a clear correlation between CV-based and manual insect measurements, as indicated by the Pearson correlation coefficients ranging from 0.89 to 0.96. Although the CV-based method may overestimate insect numbers, it provides valuable insights for targeted pest management interventions and damage assessment. The project outcomes offer a promising approach to safeguarding agriculture against invasive species, enhancing regional economic resilience while minimising the spread of insecticide, the required time, and human interaction with harmful substances.

Piotr Narowski, Dariusz Heim, Maciej Mijakowski

This article proposes new values and geospatial models of winter and summer external design temperatures for designing buildings’ heating, ventilation, and air-conditioning (HVAC) systems. The climatic design parameters applicable in Poland for the sizing of these installations are approximately 50 years old and do not correspond to Poland’s current climate. New values of climatic design parameters were determined following the methods described in European standards and the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Handbook of Fundamentals. The determined climatic design parameters, particularly the winter and summer external design temperatures, were compared with those currently in force by law in Poland. The external air design dry-bulb temperatures presented in the article were developed based on meteorological and climatic data from the years 1991–2020 from two data sources: synoptic data from the Institute of Meteorology and Water Management (IMWM) in Poland and reanalysis models of the ERA5 database of the European Centre for Medium-Range Weather Forecasts (ECMWF). According to ASHRAE, with 99.6% and 0.4% frequency of occurrence, external air design dry-bulb temperatures for winter and summer were used to develop mathematical geospatial models of external design temperatures for the Central Europe area with Poland’s territory in the centre part. Scattered data from 667 meteorological stations were interpolated to 40,000 uniform mesh points using a biharmonic spline interpolation method to develop these models. Linear regression and ANOVA analysis for the ERA5-generated data from 900 checkpoint data items were used to estimate the correctness of these models. Verified models were used to calculate winter and summer external design temperature isolines presented together with colour space representation on Mercator projected maps of Central Europe.

Feng Zhang, Xinran Liu, Changxin Gao et al.

Existing retinex-based low-light image enhancement strategies focus heavily on crafting complex networks for Retinex decomposition but often result in imprecise estimations. To overcome the limitations of previous methods, we introduce a straightforward yet effective strategy for Retinex decomposition, dividing images into colormaps and graymaps as new estimations for reflectance and illumination maps. The enhancement of these maps is separately conducted using a diffusion model for improved restoration. Furthermore, we address the dual challenge of perturbation removal and brightness adjustment in illumination maps by incorporating brightness guidance. This guidance aids in precisely adjusting the brightness while eliminating disturbances, ensuring a more effective enhancement process. Extensive quantitative and qualitative experimental analyses demonstrate that our proposed method improves the performance by approximately <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the LOL dataset compared to other state-of-the-art diffusion-based methods, while also validating the model’s generalizability across multiple real-world datasets.

Lesego Senjoba, Hajime Ikeda, Hisatoshi Toriya et al.

This study introduces a novel method for analyzing vibration data related to drill bit failure. Our approach combines explainable artificial intelligence (XAI) with convolutional neural networks (CNNs). Conventional signal analysis methods, such as fast Fourier transform (FFT) and wavelet transform (WT), require extensive knowledge of drilling equipment specifications, which limits their adaptability to different conditions. In contrast, our method leverages XAI algorithms applied to CNNs to directly identify fault signatures from vibration signals. The signals are transformed into their frequency components and then employed as inputs to a CNN model, which is trained to detect patterns indicative of drill bit failure. XAI algorithms are then employed to generate attention maps, highlighting regions of interest in the CNN. By scrutinizing these maps, engineers can identify critical frequencies associated with drill bit failure, providing valuable insights for maintenance and optimization. This method offers a transparent and interpretable framework for analyzing vibration data, enabling informed decision-making and proactive maintenance strategies to enhance drilling efficiency and minimize downtime. The integration of XAI with CNNs facilitates a deeper understanding of the root causes of drill bit failure and improves overall drilling performance.

G.R. Rama Devi, M. Swamy Das, M.V. Ramana Murthy

MANET (Mobile ad-hoc networks) is typically a no-infrastructure multi-hop network where every node interacts with other network nodes either indirectly or directly via intermediate nodes. A lot of research is being undertaken to save the energy of mobile nodes at different levels. Power-relevant issues can have an effect on every layer of the stack, making the traditional layered approach ineffective. In this work, cross layered routing protocol based on PSO (Particle swarm optimization) with adapted contention window technique is proposed. To form consistent and energy efficient routing paths, PSO algorithm uses Traffic index, Average energy load, data success rate &amp; trust value parameters that are computed from network layer. After establishing routing paths, network's contentions are measured MAC layers for communications and contention with measured contentions and average energy loads. The trust of nodes is computed using the following constraints: group trust-provided by neighbour nodes &amp; quality trust-computed by node QoS. Dual authentications with EnDA (enhanced dual authentication) using key management strategies for enhanced data security and integrity. ECC (Elliptical curve cryptography) and Diffie-Hellman key exchanges with bilinear maps improve security of communications. The suggested protocol when measured in terms of energy usage and secure key agreements during network's transmissions, showed satisfactory performances.

R. González, F.X. Aymerich, M. Alberich et al.

Background and purpose: An impaired neurovascular coupling has been described as a possible player in neurodegeneration and cognitive decline. Migraine is a recurrent and incapacitating disorder that starts early in life and has shown neurovascular coupling abnormalities. Despite its high prevalence, the physiology and underlying mechanisms are poorly understood. In this context, new biomarkers from magnetic resonance imaging (MRI) are needed to bring new knowledge into the field. The aim of this study was to determine the vein density from Susceptibility-Weighted Imaging (SWI) MRI, in subjects with migraine and healthy controls; and to assess whether it relates to Resting-State functional MRI (RS-fMRI). Materials and methods: The cohort included 30 healthy controls and 70 subjects with migraine (26 episodic, 44 chronic) who underwent a brain 3.0 T MRI. Clinical characteristics were also collected. Maps of density of veins were generated based on a Mamdani Fuzzy-Type Rule-Based System from the SWI MRI. Mean values of vein density were obtained in grey (GM) and white matter (WM) Freesurfer lobar parcellations. The Amplitude of Low-Frequency Fluctuations (ALFF) image was calculated for the RS-fMRI, and the mean values over the parcellated GM lobes were estimated. Differences between groups were assessed through and analysis of variance (age, sex, education and anxiety as covariates; p < 0.05), followed by post-hoc comparisons. Associations were run between clinical and MRI-derived variables. Results: When comparing the density of veins in GM, no differences between groups were found, neither associations with clinical variables. The density of veins was significantly higher in the WM of the occipital lobe for subjects with chronic migraine compared to controls (30%, p < 0.05). WM vein density in either frontal, temporal or cingulate regions was associated with clinical variables such as headache days, disability scores, and cognitive impairment (r between 0.25 and 0.41; p < 0.05). Mean values of ALFF did not differ significantly between controls and subjects with migraine. Strong significant associations between vein density and ALFF measures were obtained in most GM lobes for healthy subjects (r between 0.50 and 0.67; p < 0.05), instead, vein density in WM was significantly associated with ALFF for subjects with migraine (r between 0.32 and 0.58; p < 0.05). Conclusions: Results point towards an increase in vein density in subjects with migraine, when compared to healthy controls. In addition, the association between GM vein density and ALFF found in healthy subjects was lost in migraine. Taken together, these results support the idea of abnormalities in the neurovascular coupling in migraine. Quantitative SWI MRI indicators in migraine might be an interesting target that may contribute to its comprehension.

Rayanne Alves de Oliveira, Paloma Maria Pereira de Sousa, Janiel Conceição da Silva et al.

Abstract Objective: to analyse the spatial distribution and trend of leprosy in municipalities of a health region in a Northeast Brazilian state. Methods: this was an ecological time-series study based on compulsory notification of leprosy cases by the municipalities covered by the Imperatriz-MA Regional Health Management Unit, between 2008 and 2017; prevalence and mean prevalence for the period were calculated; spatial analysis of the area was carried out and maps were generated using ArcGis 10.5. Prais-Winsten regression was used for trend analysis. Result: 4,029 cases of the disease were identified, and average prevalence ranged from 2.0 to 11.5 cases/10,000 inhabitants-year. The overall trend was downward. Governador Edson Lobão had the highest prevalence, 11.5 cases/10,000 inhabitants, and Lajeado Novo had the lowest prevalence, 2.0 cases/10,000 inhabitants. Conclusion: spatial distribution of leprosy cases was heterogeneous in the municipalities studied and prevalence had a falling trend.

Maryam Imani

Abstract There are two types of important information in a polarimetric synthetic aperture radar (PolSAR) image: spatial features in two dimensions and polarimetric characteristics in the scattering dimension. Considering both polarimetric and spatial information is important for PolSAR image classification. Convolutional kernels show superior performance for extraction of spatial information from two dimensions of an image in convolutional neural networks (CNNs). But learning CNNs needs large enough training sets to achieve the optimum weights of kernels while there are not usually sufficient training samples for PolSAR images. To deal with this difficulty, a convolutional kernel‐based covariance descriptor (CKCD) is introduced for PolSAR image classification in this study. To extract contextual characteristics, compatible with the original image, the fixed‐valued convolutional kernels randomly selected from the image are used, which do not require any learning, and so do not need any training samples. To include more local spatial information and find the relation among the polarimetric features, the covariance descriptor is constructed on the extracted feature maps. Then, the polarimetric‐contextual features are given to a support vector machine with a matrix logarithm‐based kernel. Finally, the guided filter is applied to the initial classification map to result a smoothed classification map with preserved edges. The experiments on three real PolSAR images show superiority of the proposed CKCD method compared to several PolSAR classification methods such as 2DCNN and 3DCNN in small sample size situations.

André M. Amorim, Lucas C. Marinho, Augusto Francener

We describe three new species of Malpighiaceae that are endemic to central Brazil and related to the Heteropterys pannosa complex, a group of xylopodiferous, unbranched subshrubs with fruit in mericarps that have a strongly reduced or no dorsal wing. Heteropterys tocantinensis is more common in eastern Tocantins State and on the border with Bahia State, and there are a few records from Mato Grosso State. Heteropterys veadeirensis is restricted to northern Goiás State and H. walteri has a wider distribution, occurring in some municipalities in northern Goiás and southern Tocantins. Additionally, we also provide detailed redescriptions of H. pannosa and H. rosmarinifolia, the two previously known species in this complex. All species are considered Endangered (EN) based on IUCN criteria, especially due to the low area of occupancy. Illustrations, distribution maps, and information about phenology and habitat are also provided for all taxa.

Ju Zeng, Mou Leong Tan, Yi Lin Tew et al.

Continuous oil palm distribution maps are essential for effective agricultural planning and management. Due to the significant cloud cover issue in tropical regions, the identification of oil palm from other crops using only optical satellites is difficult. Based on the Google Earth Engine (GEE), this study aims to evaluate the best combination of open-source optical and microwave satellite data in oil palm mapping by utilizing the C-band Sentinel-1, L-band PALSAR-2, Landsat 8, Sentinel-2, and topographic images, with the Muda River Basin (MRB) as the test site. The results show that the land use land cover maps generated from the combined images have accuracies from 95 to 97%; the best combination goes to Sentinel-1 and Sentinel-2 for the overall classification. Meanwhile, the best combination for oil palm classification is C5 (PALSAR-2 + Landsat 8), with the highest producer accuracy (96%) and consumer accuracy (100%) values. The combination of C-band radar images can improve the classification accuracy of oil palm, but compared with the combination of L-band images, the oil palm area was underestimated. The oil palm area had increased from 2015 to 2020, ranging from 10% to 60% across all combinations. This shows that the selection of optimal images is important for oil palm mapping.

Lars Mewes, Tobias Ebert

Promoting entrepreneurial activities is crucial for regions to facilitate innovation and economic development. Yet, becoming an entrepreneur is not aspired by all people, and regions may differ considerably in their entrepreneurship potential. Assessing and providing accurate estimates of the entrepreneurship potential across fine-grained spatial scales is thus crucial to inform regional policymakers, but it still remains a major challenge due to data availability. Here we used the lab data set from the British Broadcasting Corporation (BBC) covering 368,364 individuals and providing high-resolution data about their residences to map the entrepreneurship potential across 9271 postcode sectors in Great Britain. We used a novel mapping approach that relies on a spatial smoothing function based on distance weights to utilize the most fine-grained spatial level available in the data. Our detailed maps show substantial difference in entrepreneurship potential across postcode sectors in Great Britain and within the largest cities: London, Birmingham and Manchester.

Zhixia Wang, Wei Wang, Fengshou Gu et al.

Global orbits connect the saddle points in an infinite period through the homoclinic and heteroclinic types of manifolds. Different from the periodic movement analysis, it requires special strategies to obtain expression of the orbit and detect the associated profound dynamic behaviors, such as chaos. In this paper, a global dynamic frequency method is applied to detect the homoclinic and heteroclinic bifurcation of the complicated nonlinear systems. The so-called dynamic frequency refers to the newly introduced frequency that varies with time t , unlike the usual static variable. This new method obtains the critical bifurcation value as well as the analytic expression of the orbit by using a standard five-step hyperbolic function-balancing procedure, which represents the influence of the higher harmonic terms on the global orbit and leads to a significant reduction of calculation workload. Moreover, a new homoclinic manifold analysis maps the periodic excitation onto the target global manifold that transfers the chaos discussion of non-autonomous systems into the orbit computation of the general autonomous system. That strategy unifies the global bifurcation analysis into a standard orbit approximation procedure. The numerical simulation results are shown to compare with the predictions.

Saeedeh Eskandari, Mohammad Reza Jaafari, Patricia Oliva et al.

The Zagros forests in Western Iran are valuable ecosystems that have been seriously damaged by human interference (harvesting the wood and forest sub-products, converting the forests to the agricultural lands, and grazing) and natural events (drought events and fire). In this study, we generated accurate land cover (LC), and tree canopy cover percentage (TCC%) maps for the forests of Shirvan County, a part of Zagros forests in Western Iran using Sentinel-2, Google Earth, and field data for protective management. First, we assessed the accuracy of Google Earth data using 300 random field plots in 10 different land cover types. For land cover mapping, we evaluated the performance of four supervised classification algorithms (minimum distance (MD), Mahalanobis distance (MaD), neural network (NN), and support vector machine (SVM)). The accuracy of the land cover maps was assessed using a set of 150 stratified random plots in Google Earth. We mapped the forest canopy cover by using the normalized difference vegetation index (NDVI) map, and field plots. We calculated the Pearson correlation between the NDVI values and the TCC% (obtained from field plots). The linear regression between the NDVI values and the TCC% was used to obtain the predictive model of TCC% based on the NDVI. The results showed that Google Earth data yielded an overall accuracy of 94.4%. The SVM algorithm had the highest accuracy for the classification of Sentinel-2 data with an overall accuracy of 81.33% and a kappa index of 0.76. The results of the forest canopy cover analysis showed a Pearson correlation coefficient of 0.93 between the NDVI and TCC%, which is highly significant. The results also showed that the linear regression model is a good predictive model for TCC% estimation based on the NDVI (r² = 0.864). The results can be used as a baseline for decision-makers to monitor land cover change in the region, whether produced by human activities or natural events and to establish measures for protective management of forests.