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This study assesses soil productivity in a 15-hectare cotton field using an integrated approach combining field data, laboratory analysis, and remote sensing techniques. Soil samples were collected and analyzed for key parameters including nitrogen (N), humus, phosphorus (P2O5), potassium (K2O), carbonates, pH, and electrical conductivity (EC). In addition to low salinity, these analyses showed low results for humus and nutrient parameters. A Pearson correlation analysis showed that low organic matter and high salinity had a strong negative correlation with crop productivity, explaining 37% of the variation in NDVI values. Remote sensing indices (NDVI, SAVI, NDMI, and NDSI) confirmed these findings by highlighting the relationship between soil properties and spectral reflectance. This research demonstrates the effectiveness of remote sensing in soil assessment, emphasizing its critical role in sustainable agricultural planning. By integrating traditional methods with advanced remote sensing technologies, this study provides actionable insights for policymakers and practitioners to improve soil productivity and ensure food security. [ABSTRACT FROM AUTHOR]

Land elements have been used as basic landform descriptors in many science disciplines, including soil mapping, vegetation mapping, and landscape ecology. This paper presents a semi-automatic method based on k-means unsupervised classification to analyze geomorphometric features as landform elements in Armenia. First, several data layers were derived from DEM: elevation, slope, profile curvature, plan curvature and flow path length. Then, k-means algorithm has been used for classifying landform elements based on these morphomertic parameters. The classification has seven landform classes. Overall, landform classification is performed in the form of a three-level hierarchical scheme. The resulting map reflects the general topography and landform character of Armenia.

Landslide susceptibility mapping (LSM) is an important decision basis for regional landslide hazard risk management, territorial spatial planning and landslide decision making. The current convolutional neural network (CNN)-based landslide susceptibility mapping models do not adequately take into account the spatial nature of texture features, and vision transformer (ViT)-based LSM models have high requirements for the amount of training data. In this study, we overcome the shortcomings of CNN and ViT by fusing these two deep learning models (bottleneck transformer network (BoTNet) and convolutional vision transformer network (ConViT)), and the fused model was used to predict the probability of landslide occurrence. First, we integrated historical landslide data and landslide evaluation factors and analysed whether there was covariance in the landslide evaluation factors. Then, the testing accuracy and generalisation ability of the CNN, ViT, BoTNet and ConViT models were compared and analysed. Finally, four landslide susceptibility mapping models were used to predict the probability of landslide occurrence in Pingwu County, Sichuan Province, China. Among them, BoTNet and ConViT had the highest accuracy, both at 87.78%, an improvement of 1.11% compared to a single model, while ConViT had the highest F1-socre at 87.64%, an improvement of 1.28% compared to a single model. The results indicate that the fusion model of CNN and ViT has better LSM performance than the single model. Meanwhile, the evaluation results of this study can be used as one of the basic tools for landslide hazard risk quantification and disaster prevention in Pingwu County. [ABSTRACT FROM AUTHOR]

This paper analyzes and discusses the potential and limits of using Facebook data to analyse spatial-temporal urban rhythms and city centrality through the availability of services and retail. During September 2020, we harvested Facebook Places data using Graph API for our pilot area consisting in the city of Brno. Specifically, we focused on the spatial distribution of 'Food & Beverage', 'Shopping & Retail' and 'Medical & Health' categories, and their associated opening hours. The paper provides a detailed description of data collection and post-processing among possible analysis methods and visualizations presented in the context of our case study. The study focuses on inter-urban structures and urban rhythms through the analysis of opening hours of retail and services. We also discuss the benefits and potential problems associated with the use of these data. [ABSTRACT FROM AUTHOR]

Population distribution modelling can benefit many different domains, for example, transportation, urban planning, ecology or emergency management. Information about the location and number of people in an affected area is crucial for decision-makers during emergencies and crises. Mobile phone data represents relatively reliable and time accurate information on real-time population distribution, movement and behaviour. In this study, we evaluate the spatio-temporal distribution of population derived from phone data of the selected pilot area (City of Brno, Czech Republic). Analysis is based on the dataset describing the estimated human presence (EHP) with two values – visitors and transiting persons. The temporal change of data is first analysed and further processed using two methodological approaches. First, the dasymetric method is used where the building geometry and technical attributes served as a target layer. Second, the results of building level analysis are transformed into a regular grid zone of both visitors and the general EHP. Resulting spatio-temporal patterns are compared to the census data. Results demonstrate how the proposed building level dasymetric approach can improve the spatial granularity of EHP. Potential use of proposed methodology within selected emergency situations is further discussed. [ABSTRACT FROM AUTHOR]

Landmarks play an important role in navigation especially when people in the familiar and unfamiliar environment. These landmarks are usually used for expressing their spatial knowledge. This prior knowledge is related to the spatial familiarity that the spatial knowledge is acquired by individuals as a function of their experience in the environment (Gale et al., 1990). Individuals prefers to take into highly account their familiar route within the environment. Furthermore, the route descriptions include the differed types of landmarks in familiar/unfamiliar routes (Lovelace et al., 1999). The user's level of spatial familiarity for people is a key factor of how they navigate in the environment (Savage et al., 2012). In human navigation or wayfinding, the dependency on familiar landmarks is the highest priority (Golledge, 2003). Landmarks are more distinguishably salient and prominent than the another spatial features (Sorrows and Hirtle, 1999). Therefore, the landmark salience refers to how easily this prominent spatial feature can be regarded as a landmark (Raubal and Winter, 2002). The landmark salience mainly divided into visual, structural (important location), and cognitive (semantic). Visual salience refers to the visual characteristics of spatial features such as color contrast with surroundings. Some visual characteristics include facade areas, shapes, colors and other properties. Semantic salience refers to the spatial knowledge related features such as a cultural importance of building. Structural salience refers to the spatial features play an important role in the structure of the spatial environment. The level of spatial knowledge of the individual also an important factor in landmark selection (Nuhn and Timpf, 2018). Hamburger and Röser (2014) investigated landmark semantics and showed that famous buildings were better recognized than unfamiliar ones. Quesnot and Roche (2015) found that the familiar individuals prefer the semantic landmarks in environment while the unfamiliar individuals prefer the landmark with the prominent visual and structural. In contrast, people unfamiliar with an environment prefer landmarks with outstanding visual and structural characteristics. The existing literature studies the method that landmark indicators to be considered are not comprehensive while can't reflect on the landmark characteristics. The reason is that the method of calculation and extraction for landmarks can't conform the individual preference needs of navigation. Compared with the traditional subjective evaluation method, the eye-tracking method provide objectivity and reliability. For example, a mobile eye tracking system was used by Kiefer et al. (2014) to investigate the process of self-localization. They discovered that test persons directed their attention longer on landmarks on the map and aligned them with objects in the surroundings during successful localization of their own position. Schwarzkopf et al. (2013) used fixed and mobile eye tracking devices to study that participates selected the navigation landmarks in the virtual environment and the real-world of the airport. In general, there are an only limited number of works related to the influence of spatial familiarity on landmark by using eye tracking method. At present, many navigation systems are providing users with route directions under different travel modes. These navigation systems have the "distance-to-turn" method in route directions, however the landmark knowledge has not been conformed to user's habits and spatial cognitive in wayfinding. In order to solve these problems, this paper aims to study the characteristics of the user's spatial familiarity and landmark salience evaluation method in navigation. The following questions are addressed in this study: 1) What's the relationship between the spatial familiarity with the landmark selection in navigation for individuals? 2) What is the quantitative evaluation model to connect between the spatial familiarity with the landmark salience by using the eye-tracking data? In our experiments, we will use eye tracking to explore the influence of spatial familiarity on landmark salience. The experimental path is part of the campus area of Nanjing Normal University. The subjects are divided into novice students and senior students from Nanjing Normal University as unfamiliar and familiar groups respectively. The procedures are divided into two parts, namely, the subjects should walk the experimental path within the fixation time and then they are asked to wear VR eye tracking module a Glass DK ? special for HTC view (China; https://www.7invensun.com/) to do three types of tasks about landmark picture with the characteristics of visual, semantic and structural. With this research, we expect to establish a landmark salience evaluation method through eye movement data and analyse the landmark selection rules of groups with different spatial familiarity. It is helpful to design the pedestrian navigation system based on landmarks, improve the pertinence and reliability of navigation services and reduce the cognitive load for users. [ABSTRACT FROM AUTHOR]