Landscape Images Research Articles

Research on the tourist destination image of the Huaiyang Canal based on network data

The Huaiyang Canal, a significant section of the Grand Canal, boasts representative tourist attractions. This study analysis of online reviews from Ctrip and Mahive using R language, Gephi, ROST CM, and SPSS has provided insights into tourists’ perceptions of the Huaiyang Canal’s image. Key findings include: (1) Dominant landscape images encompass gardens, canals, and buildings, emphasizing the historical and cultural assets. Both cultural and natural landscapes equally captivate tourists. (2) The canal’s tourism image perception follows a “garden-history-canal” hierarchy with the canal as the central space and history expanding its tourism features. (3) The perceptions can be categorized into historical and cultural landscapes, man-made projects, and attraction perception. Despite varying tourist numbers in Huaian and Yangzhou, scenic spot experiences are similar. The overall perception of tourists is largely positive, but some express concerns about service attitudes and travel time planning.

Genetically encoded epigenetic sensors for visualization of H3K9me3, H3K9ac and H3K4me1 histone modifications in living cells

Post-translational modifications of histones play a crucial role in chromatin structure maintenance and epigenetic regulation. The LiveMIEL (Live-cell Microscopic Imaging of Epigenetic Landscape) method represents a promising approach for tracking histone modifications. It involves visualization of epigenetic modifications using genetically encoded fluorescent sensors and further analysis of the obtained intranuclear patterns by multiparametric image analysis. In this study, we designed three new red fluorescent sensors—MPP8-Red, AF9-Red and DPF3-Red—for live-cell visualization of patterns of H3K9me3, H3K8ac and H3K4me1, respectively. The observed fluorescent patterns were visually distinguishable, and LiveMIEL analysis clearly classified them into three corresponding groups. We propose that these sensors can be used for live-cell dynamic analysis of changes in organization of three epigenetic types of chromatin.

The Construction of the Image of Italy in the 19th Century on the Example of Travelogues Published in the Slovak Period Press

The paper analyses how Italy is depicted in selected 19th-century Slovak travelogues. We chose this period because the travelogue genre was established in Slovak literature in the 19th century. Second, the travel reports to Italy that appeared in the Slovak context in the 17th century had a different nature since the Counter-Reformation and the condemnation of Protestants to the Spanish galleys in Naples conditioned the journeys. These accounts focused more on personal suffering than the peculiarities of the countries. 19th-century travel writings had a different character. Magazinestried to meet readers’ interests by publishing documentary and factual literature. We chose the travelogues of two important cultural figures who visited Italy during the same period and had the opportunity to perceive the same cultural, social, and other circumstances. The emphasis is on the travellers ́ interests, the construction of their image and how nature, climate, and other characteristic country features are reflected in their landscape image. In addition to a re-examination of the text-creation mechanisms and the different rendering methods of the same aspect, the description of natural, cultural and social realia, the paper reveals their relationship to the depicted country and how their interest was influenced by their theological education and involvement in the Church. The paper outlines the genre form widespread in 19th-century Slovak journalism considering the then atmosphere and cultural events. After studying subject-related period documents and scientific works, we used the interpretive method, which pointed outways of depicting the visited country and its specifics. This is reflected in how the issue is treated: from a generalising view of the period, the significance of the period press, and the place of the authors to an interpretation of their works. In the conclusion, we seek to generalise the existing parallels and differences in the modes of imagery.Keywords:19th century, Slovak period press, travel writing, Italy, Venice, religious symbolism

Clinical application of whole-genome sequencing of solid tumors for precision oncology

Genomic alterations in tumors play a pivotal role in determining their clinical trajectory and responsiveness to treatment. Targeted panel sequencing (TPS) has served as a key clinical tool over the past decade, but advancements in sequencing costs and bioinformatics have now made whole-genome sequencing (WGS) a feasible single-assay approach for almost all cancer genomes in clinical settings. This paper reports on the findings of a prospective, single-center study exploring the real-world clinical utility of WGS (tumor and matched normal tissues) and has two primary objectives: (1) assessing actionability for therapeutic options and (2) providing clarity for clinical questions. Of the 120 patients with various solid cancers who were enrolled, 95 (79%) successfully received genomic reports within a median of 11 working days from sampling to reporting. Analysis of these 95 WGS reports revealed that 72% (68/95) yielded clinically relevant insights, with 69% (55/79) pertaining to therapeutic actionability and 81% (13/16) pertaining to clinical clarity. These benefits include the selection of informed therapeutics and/or active clinical trials based on the identification of driver mutations, tumor mutational burden (TMB) and mutational signatures, pathogenic germline variants that warrant genetic counseling, and information helpful for inferring cancer origin. Our findings highlight the potential of WGS as a comprehensive tool in precision oncology and suggests that it should be integrated into routine clinical practice to provide a complete image of the genomic landscape to enable tailored cancer management.

Analysis of Differences between Groups in Urban Landscape Images - Focusing on the Difference in Landscape Perception of Gwacheon Residents, Public Officials, and Visitors -

Analysis of Differences between Groups in Urban Landscape Images - Focusing on the Difference in Landscape Perception of Gwacheon Residents, Public Officials, and Visitors -

Garden Ecological Quality Evaluation Based on Green Landscape Image Semantic Segmentation Model

This work suggests predicting student performance using a Gaussian process model classification in order to address the issue that the prediction approach is too complex and the data set involved is too huge in the process of predicting students’ performance. In order to prevent overfitting, a sample set consisting of the three typical test outcomes from 465 undergraduate College English students is divided into training and test sets. The cross-validation technique is used in this study. According to the findings, Gaussian process model classification can accurately predict 92% of the test set with a prediction model, and it can also forecast students’ final exam marks based on their typical quiz scores. Furthermore, it is discovered that the prediction accuracy increases with the sample set’s distance from the normal distribution; this prediction accuracy rises to 96% when test scores with less than 60 points are taken out of the analysis.

Emotional Design and Validation Study of Human–Landscape Visual Interaction

The formal beauty of “objects” is the main focus of modern rural landscapes, ignoring human interaction with the environment and the emotional reflection in this behavioral process. It is unable to satisfy the emotional needs of younger people who aspire to a high-quality life in the rural environment. The research idea of this paper is ‘first assessment—then design—then validation’. First, A 5-point Likert scale was used to investigate differences in contemporary young people’s emotional perceptions of the four rural natural landscapes in terms of instinct, behavior, and reflection. Then, using architectural design methods, a visual attraction element (viewing platform) was added by selecting samples that varied in all three dimensions (visual richness, behavioral attraction, and depth of thought). After that, a desktop eye tracker was used to record the eyeball characteristics of participants viewing the current images of natural landscapes and images of modified natural landscapes (pupil diameter, fixation duration, gaze point, etc.), and these data were combined with the subjective psychological perception scale score to determine whether or not the subjects’ positive emotions are evoked by the modified natural environment. The findings indicate that placing visually attractive elements between people and the natural world can cause subjects to feel good, think deeply, and feel more a part of the surroundings. Furthermore, we confirmed that subjects’ emotions can be evoked by 2D natural environment pictures and that the length of time subjects gaze at a picture is unaffected by the size of any individual element.

Тестування методу топографічної спорідненості зображень на зображеннях поверхні землі

Introduction. In connection with the development of the method of topographic affinity of images, it became necessary to conduct its testing according to the criteria of workability and efficiency. The purpose of the paper is testing of the method of determining the topographic affinity of images based on taking into account the detected special zones on the images of the natural landscape for the autonomous navigation of UAVs. Results. According to the results of testing for three tasks, the method showed its effectiveness at the level of 100%. Conclusions. The method of topographic affinity of images can work with a large number of complex and diverse images of the Earth's surface, which cannot be analyzed by other known methods, and with high efficiency. It can be used to build a system of autonomous navigation of UAVs separately or together with other methods. Keywords: unmanned aerial vehicle, unmanned aircraft complex, autonomous navigation, special points, special areas, method of analysis of special areas of images.

Landscape Image Defogging System Based on DCPA Algorithm Optimization

As the global climate environment deteriorates gradually, the collected images are covered by fog, which reduces the clarity of the images. Therefore, the processing of fog images is very important. In landscape image defogging research, the defogging process may be affected by factors such as data quality, noise interference, and computational efficiency. To improve the defogging effect of landscape fog images, a landscape image defogging system was put forward with the optimization of dark channel prior algorithm. The image defogging algorithm was combined with the improved atmospheric scattering model estimation algorithm and the dark channel convolutional network image defogging algorithm to achieve image defogging. The atmospheric light estimation method based on atmospheric scattering model combined transmittance map and grayscale map information to achieve optimization of defogging effect. In the improvement of the dark channel prior algorithm, a convolutional network was introduced for feature extraction to enhance the smoothing of image brightness changes and transmittance estimation. The research findings demonstrated that the signal-to-noise ratio of the image defogging algorithm estimated by the atmospheric scattering model could reach up to 19dB, which was about 15.4% higher than that of existing 5 image defogging algorithms on average, indicating that the image resolution of the algorithm was higher after defogging. In the Reside dataset, the image defogging algorithm based on dark channel prior increased the signal-to-noise ratio by about 9.5%, the average gradient by about 10.4%, the structural similarity by about 12%, and the information entropy by about 5.8%, indicating that the effect of the algorithm was stable and the image defogging effect was good. The dark channel convolutional network image defogging algorithm had less running time and reduced the complexity of the defogging structure, by contrast, it reduced the running time by about 67%. The average scores for the operability, stability, and defogging effect of the system were 9.87 points, 9.85 points, and 9.54 points, respectively, indicating good performance of the system. The user feedback on natural landscape fog maps, architectural landscape fog maps, and historical landscape fog maps is good, and the user experience is high.

Methodological and legislative approaches regarding virtual reality / Artificial Intelligence as tools for interconnection between biodiversity and neuropsychiatric disorders

Neuropsychiatric disorders have an increasing percentage among the current human population, which is why the use of biodiversity elements in their treatment is a key aspect in the process of psychological rehabilitation, based on biophilia. It is currently used as an intermediate VR/AI tool to generate positive emotions and well-being, based on nature images of landscapes and biodiversity. Although there are a number of problems that arise in the use of VR / AI in the biomedical field, mainly due to legislative and ethical aspects, the digitization of the biomedical field represents the opening of new perspectives on the diagnosis and treatment of neuropsychiatric disorders, resulting in new directions of scientific research aimed at significantly improving the study and understanding of the healing mechanisms of psychopathologies.

Comparing hybrid models for recognising objects in thermal images at nighttime

This research aims to revolutionize urban object recognition by developing cloud-based Python programs using intelligent algorithms. Unlike current models that focus on colour enhancement in nighttime thermal images, this work addresses the critical challenge of accurate object detection in urban landscapes. The proposed method incorporates a binary generative adversarial network (GAN) generator that can switch bidirectionally between daytime colour (DC) and nighttime infrared (NTIR) images. memory-based visual image memory (MVAM), system extracts important descriptive information from urban landscape images, reducing problems related to small sample sizes. This discussion presents a comprehensive improvement and evaluation of a deep learning image classification pipeline using Google Colab, demonstrating advanced image processing. Using TensorFlow, Keres and scikit image libraries combined with advanced algorithms such as DenseNet121 and MobileNetV2 presents a clear approach. We created a Bidirectional GAN + MVAM for object recognition in this work. Our method performed well, with an accuracy of 81.43%, precision of 51.16, recall of 50.11, and F-score of 46.37. The systematic presentation of the code presents a careful strategy to ensure optimal performance, stability, and efficiency of deep learning and image processing tasks.

UNDERSTANDING ASSOCIATIVE PORTRAITS IN LINGUISTICS: CONCEPT AND CORE PRINCIPLES

In recent years, one of the most pressing issues in linguistics has been the determination of the linguistic landscape image of the external world as perceived by language speakers. This endeavour is primarily pursued through associative experiments conducted collectively among speakers of a language. Such experiments aim to uncover how individuals mentally map and interpret the world around them through language. A crucial aspect of this research involves creating associative portraits of notable individuals within society. These portraits help in understanding how famous people are collectively perceived and represented linguistically by different groups of speakers. In this context, the current article delves into the problem of studying associative portraits within the field of linguistics. It explores the methodologies used in these associative experiments, discusses the importance of these portraits in capturing societal perceptions, and elucidates the underlying linguistic principles that define the concept of the associative portrait. By doing so, the article aims to reveal the linguistic essence of associative portraits, offering insights into how language shapes and reflects our understanding of prominent figures in society.

From fragments to system: a research on the mesoscale structure of vernacular landscapes in the Hang-Jia-Hu area, China

ABSTRACT Vernacular landscape incorporates spatial structures intimately associated with regional environments, agricultural production, and rural life. Analyzing and understanding these structures can help reveal spatial patterns and intrinsic mechanisms of vernacular landscapes and provide reference for contemporary rural construction. This paper aims to analyze the mesoscale structure of vernacular landscapes in the Hang-Jia-Hu area by discussing spatial relationships between four main elemental patches: water systems, farmlands, settlements and hills. Special attention is given to the correlations between landscape structure and local agriculture. Initially adopting a grid sampling approach, the paper extracts 31 fragment samples of vernacular landscape images (each 2 × 2 km) from the satellite imagery of the Hang-Jia-Hu area. Subsequently, via semantic segmentation, the four elements are identified and differentiated. Integrating related knowledge from environmental history, agricultural history, human geography, and other disciplines, the paper carries out a typological analysis on the vernacular fragment samples, deriving the latent structural types, which consist of concepts and abstract schemas. Based on the association between these structural types, establishes a structural system, as the primary integrated conclusion of this research, and looking forward to offering framework reference for contemporary rural construction.

Image of Celestial Firmament in Poetic Cosmology of “Evenings on a Farm near Dikanka”

This study explores the features of poetic cosmology in N. V. Gogol’s tales from the cycle “Evenings on a Farm near Dikanka”. It examines the poetics of the nocturnal landscape and the symbolic richness of celestial space. The functions of light details in poetic depictions of the universe are identified. Special attention is paid to portraying the Universe as a visible expression of the problem of good and evil, which serves as the core theme of the entire cycle “Evenings...”. It is argued that celestial space is not only a central landscape image but also a distinct ontological category. The study reveals that for the cycle “Evenings...”, two contrasting models of the universe — harmonious and chaotic — play a significant role, based on the relationships between earth and sky. Earth and sky, as two coordinates of the universe, can form a cohesive picture for Gogol, where earthly space is revealed through celestial beauty. Celestial space can be understood as a kind of screen onto which human potential for spiritual transformation or sorrow due to its absence is projected. Beneath Gogol’s primary aestheticization of the daytime and nighttime universe lies a philosophical depth in his paintings, prompting reflections on human earthly journey, the sinfulness of earthly existence, and the aspiration towards the heavens, which generates the image of the disconnection between earthly and celestial realms.

A deep learning approach based on graphs to detect plantation lines

Identifying plantation lines in aerial images of agricultural landscapes is re-quired for many automatic farming processes. Deep learning-based networks are among the most prominent methods to learn such patterns and extract this type of information from diverse imagery conditions. However, even state-of-the-art methods may stumble in complex plantation patterns. Here, we propose a deep learning approach based on graphs to detect plantation lines in UAV-based RGB imagery, presenting a challenging scenario containing spaced plants. The first module of our method extracts a feature map throughout the backbone, which consists of the initial layers of the VGG16. This feature map is used as an input to the Knowledge Estimation Module (KEM), organized in three concatenated branches for detecting 1) the plant positions, 2) the plantation lines, and 3) the displacement vectors between the plants. A graph modeling is applied considering each plant position on the image as vertices, and edges are formed between two vertices (i.e. plants). Finally, the edge is classified as pertaining to a certain plantation line based on three probabilities (higher than 0.5): i) in visual features obtained from the backbone; ii) a chance that the edge pixels belong to a line, from the KEM step; and iii) an alignment of the displacement vectors with the edge, also from the KEM step. Experiments were conducted initially in corn plantations with different growth stages and patterns with aerial RGB imagery to present the advantages of adopting each module. We assessed the generalization capability in the other two cultures (orange and eucalyptus) datasets. The proposed method was compared against state-of-the-art deep learning methods and achieved superior performance with a significant margin considering all three datasets. This approach is useful in extracting lines with spaced plantation patterns and could be implemented in scenarios where plantation gaps occur, generating lines with few-to-no interruptions.

Application of image rendering based on improved neural networks and sensors in landscape design

Landscape planning and design is an indispensable part of modern urban construction. In landscape design, rendering technology can transform designers' imagination into actual images, allowing people to better understand the design intent. In order to solve the problem of insufficient precision in image rendering in landscape design, this paper proposes a landscape design image rendering technology based on improved neural network technology. This technology combines stereo matching algorithms, which evaluate the corrected stereo images to calculate the differences between the real scene and the underlying model. The stereo matching algorithm is based on the basic model and obtains accurate depth information from a wide range image pair, making the rendered landscape image more realistic and precise. The experiment demonstrates the effectiveness of the landscape design image rendering system through two important aspects: processing speed and parallax accuracy. The development of image rendering technology in landscape design cannot be separated from the mutual promotion of theory and practice. The technology proposed in this article has certain practical value and hopes to make more positive contributions to urban construction and cultural inheritance.

AG‐SDM: Aquascape generation based on stable diffusion model with low‐rank adaptation

AbstractAs an amalgamation of landscape design and ichthyology, aquascape endeavors to create visually captivating aquatic environments imbued with artistic allure. Traditional methodologies in aquascape, governed by rigid principles such as composition and color coordination, may inadvertently curtail the aesthetic potential of the landscapes. In this paper, we propose Aquascape Generation based on Stable Diffusion Models (AG‐SDM), prioritizing aesthetic principles and color coordination to offer guiding principles for real artists in Aquascape creation. We meticulously curated and annotated three aquascape datasets with varying aspect ratios to accommodate diverse landscape design requirements regarding dimensions and proportions. Leveraging the Fréchet Inception Distance (FID) metric, we trained AGFID for quality assessment. Extensive experiments validate that our AG‐SDM excels in generating hyper‐realistic underwater landscape images, closely resembling real flora, and achieves state‐of‐the‐art performance in aquascape image generation.

Interpreting regional characteristics of Tibetan-Qiang houses in Northwestern Sichuan by Deep Learning and Image Landscape

This paper presents a framework for interpreting regional features of houses in the Tibetan-Qiang region by Deep Learning (DL) and Image Landscape (IL), which learns the typical features from online building photos in different subordinate areas of the whole region through a set of datasets and DL models. The contribution of this framework is taking online building images as a proxy of rural building characteristics, which significantly improves the scope and efficiency of related built heritage studies and accurately reveals the representative features of houses in remote rural areas. The results are validated by established studies, and the framework can be transferred to other regions through the provided path and openly published datasets.

THE ROLE OF INVESTMENT LAG IN ECONOMIC SYSTEM DEVELOPMENT TRAJECTORY

THE ROLE OF INVESTMENT LAG IN ECONOMIC SYSTEM DEVELOPMENT TRAJECTORY

Cenas de um Brasil sem saída: imagens do neossujeito na telenovela Pantanal

The study addresses the Brazilian soap opera Pantanal (2022) from the perspective in which the narrative presents the character Madeleine. The reiteration of aesthetic arrangements made up of elements such as water and checkered domestic environments place the character in images of anesthetic landscapes, making it possible to infer the figurativeness of the neo-subject. Considering the analysis frames of the chapters shown between March 31, 2022 and May 21, 2022 – from the first appearance of the character until the moment of the plane crash that ends the girl’s trajectory in the plot –, it is possible to assume that the investigated scenes, in terms of the produced meaning effects, function as a metaphor and metonymy of a neoliberal apparatus, allowing the audience to perceive itself housed, eventually, in places similar to those that are framed by the telenovela and occupied by the character.