Hasil untuk "Physical geography"

Shoukai Sun, Anke Huss, Derek Karssenberg et al.

Urban greenery, as a critical urban landscape component, plays an important role in improving the living environments’ and residents’ well-being. Previous studies have predominantly adopted satellite image-based vegetation measurements. This study aims to quantify pedestrian-perspective greenery visibility using Google Street View (GSV) images and to understand how greenery types and built environment characteristics influence the correlation between pedestrian and aerial greenery assessments. We collected GSV images located on 34,601 sampling points and applied the DeepLab v3+ deep learning model to quantify green view index (GVI) from the pedestrian perspective. We distinguished green vegetation view index (GVVI) and green terrain view index (GTVI) to differentiate vertical and horizontal greenery types. Normalized difference vegetation index (NDVI) was extracted from Sentinel-2 images using circular buffers of varying radii (10–200 m) centered on GSV sampling points. Sampling points were filtered based on the buffer distance to avoid overlapping NDVI pixels in neighboring sampling points. Spearman correlation analysis was conducted across different typologies (urban, intermediate, rural) to examine GVI-NDVI relationships. Street-level greenery exhibited substantial spatial heterogeneity across the whole of the study area (Basel, Switzerland). GVI vs. NDVI in buffers with different radii had strong positive correlations, with a maximum Spearman coefficient of 0.77 for the 15 m NDVI buffer. Correlation coefficients decreased progressively from urban (0.77) to intermediate (0.72) and rural (0.66) areas. Correlation coefficients strongly decreased with increasing buffer sizes. Analysis of GSV images with high NDVI but low GVI values indicates that greenery types and building distributions significantly affect the street-level visible greenery. This study links street-level greenery with features in the built environment by using different methods for assessing green exposure. The findings provide methodological insights for greenery exposure studies and inform evidence-based urban planning strategies for optimizing green visibility.

Florian Heinrichs

Under mild structural assumptions and regularity conditions on the marginal and conditional densities, an explicit bound on the $β$-mixing coefficients in terms of the physical dependence measure is provided. Consequently, weak physical dependence implies $β$-mixing and strong mixing for triangular arrays, complementing Hill (2025), who proved the converse implication under moment assumptions.

Szilárd Szabó, Abdelmajeed A. Elrasheed, Lilla Kovács et al.

Reference data are the most crucial points in model building. In geoscience, a scarcity of sufficient reference data is common. Pseudo-labelling (PL), i.e. incorporating high-probability data in the model-building process, offers a potential solution. We aimed to reveal the efficiency of PL in lithological mapping in a vegetation-free arid region of Sudan. Multiple Adaptive Regression Splines (MARS) and Random Forest (RF) were used to classify a Landsat 9 image. Reference data were collected during fieldwork and through visual interpretation. Image processing yielded classified maps with associated probability layers, from which 1000 additional traditional samples (PL data) were extracted at a 95 percent probability. A detailed accuracy assessment was conducted, and accuracy measures were evaluated using statistical analysis and visual inspection. MARS was found to be an ambiguous classifier because the probability was too optimistic related to the overall accuracy (OA) (81% of samples had above 99% probability, OA = 98.2%) compared to RF (21% above 99%, OA = 98.1%); that is, despite the high probability, the accuracy improvement was only 0.1 percent. At the class level, the correlation between probability and the F1-score was low (0.21%). The original and PL-based models resulted in different maps with improved accuracy, although the new model version showed lower probability values for both the classifiers. Visual inspection proved essential for better insights into the spatial patterns: expert knowledge is crucial for controlling the occurrence of rock types and identifying false classifications. The main finding is that probability should be handled carefully, as it does not guarantee high model performance in classification, although the PL approach can lead to more reliable maps.

Ning Zhang, Yongcheng Wang, Gang Li et al.

In current super-resolution (SR) research, blind SR models capable of handling multiple degradations have attracted significant attention. Inspired by variational autoencoders (VAEs) that model data distributions through latent representations, this paper proposes a VAE framework for unsupervised remote sensing image (RSI) SR. VAEs excel at learning rich latent representations, modeling probabilistic distributions of input data and unsupervised learning, making them inherently well-suited to real-world blind SR scenarios. The proposed framework consists of an encoder that maps low-resolution (LR) images into a latent space and a decoder that reconstructs super-resolved images from the latent representations. To enhance latent modeling, an alternating optimization strategy is implemented for training the encoder and decoder. Furthermore, a comprehensive loss function and a latent coding regularization strategy are designed to constrain latent representations while maintaining image domain consistency. Experimental results demonstrate that on synthetic data, our method achieves favorable performance in both visual quality and quantitative metrics. It also demonstrates competitively performance compared to supervised methods, particularly in 4× and 8× SR tasks. Additionally, evaluations on Jilin-1 satellite RSIs further validate the effectiveness of our approach.

John Alexander Cruz Morales

Starting from Greg Moore's description about Physical Mathematics, a framework is proposed in order to understand it, based on Gilles Châtelet's philosophy. It will be argued that Châtelet's ideas of inverting, splitting, augmenting and virtuality are crucial in the discussion about the nature of Physical Mathematics. Along this line, it will be proposed that mirror symmetry is a natural study case to test Châtelet's ideas in this context. This should be considered as a first step in a long term project aiming to study the relations among mathematics, physics and philosophy in the construction of a global understanding of the structure of the universe, as it was envisioned by Grothendieck in the late 80's of the last century and it was started to be developed independently by Châtelet in the beginning of the 90's. The main suggestion of the essay is that it is in the relations between mathematics, physics and philosophy that new knowledge arises.

Ke-ke Shang, Jiangli Zhu, Junfan Yi et al.

Complex networks are commonly used to explore human behavior. However, previous studies largely overlooked the geographical and economic factors embedded in collective attention. To address this, we construct attention networks from time-series data for the United States and China, each a key economic power in the West and the East, respectively. We reveal a strong macroscale correlation between urban attention and Gross Domestic Product (GDP). At the mesoscale, community detection of attention networks shows that high-GDP cities consistently act as core nodes within their communities and occupy strategic geographic positions. At the microscale, structural hole theory identifies these cities as key connectors between communities, with influence proportional to economic output. Overlapping community detection further reveals tightly connected urban clusters, prompting us to introduce geographic and topic-based metrics, which show that closely linked cities are spatially proximate and topically coherent. Of course, not all patterns were consistent across regions. A notable distinction emerged in the relationship between population size and urban attention, which was evident in the United States but absent in China. Building on these insights, we integrate key variables reflecting GDP, geography, and scenic resources into regression model to cross-verify the influence of economic and geographic factors on collective user attention, and unexpectedly discover that a composite index of population, access, and scenery fails to account for cross-city variations in attention. Our study bridges the gap between economic prosperity and geographic centrality in shaping urban attention landscapes.

Marco De Santis, Lisa Alazraki

This paper presents our submission to the MRL 2025 Shared Task on Multilingual Physical Reasoning Datasets. The objective of the shared task is to create manually-annotated evaluation data in the physical commonsense reasoning domain, for languages other than English, following a format similar to PIQA. Our contribution, FormaMentis, is a novel benchmark for physical commonsense reasoning that is grounded in Italian language and culture. The data samples in FormaMentis are created by expert annotators who are native Italian speakers and are familiar with local customs and norms. The samples are additionally translated into English, while preserving the cultural elements unique to the Italian context.

Shunan Feng, Adrien Wehrlé, Joseph Mitchell Cook et al.

The NASA MODIS MOD10A1 snow albedo product has enabled numerous glaciological applications. The temporal consistency of MODIS albedo is critical to obtaining reliable results from this 22-year time series. The orbit of Terra began to drift toward earlier acquisition times after the final inclination adjustment maneuver to maintain its nominal orbit by NASA on 27 February 2020, which may introduce biases that compromise the accuracy of quantitative time series analysis as the drift continues. Here, we evaluate the impact of Terra's orbital drift by comparing the differences between the Terra MODIS albedo and albedo products derived from Aqua MODIS, harmonized Landsat and Sentinel 2, Sentinel 3, and PROMICE (Programme for Monitoring of the Greenland Ice Sheet) ground measurements over the Greenland ice sheet. Our results suggest that the influence of orbital drift on albedo is small (+0.01 in 2020), but potentially biased for time series analysis. Our analysis also finds that the drift effect that causes earlier image acquisition time may lead to more apparently cloudy pixels and thus effectively reduce the Terra MODIS temporal resolution over Greenland.

Elisa Leonardelli, Sara Tonelli

The online diffusion of information related to Europe and migration has been little investigated from an external point of view. However, this is a very relevant topic, especially if users have had no direct contact with Europe and its perception depends solely on information retrieved online. In this work we analyse the information circulating online about Europe and migration after retrieving a large amount of data from social media (Twitter), to gain new insights into topics, magnitude, and dynamics of their diffusion. We combine retweets and hashtags network analysis with geolocation of users, linking thus data to geography and allowing analysis from an "outside Europe" perspective, with a special focus on Africa. We also introduce a novel approach based on cross-lingual quotes, i.e. when content in a language is commented and retweeted in another language, assuming these interactions are a proxy for connections between very distant communities. Results show how the majority of online discussions occurs at a national level, especially when discussing migration. Language (English) is pivotal for information to become transnational and reach far. Transnational information flow is strongly unbalanced, with content mainly produced in Europe and amplified outside. Conversely Europe-based accounts tend to be self-referential when they discuss migration-related topics. Football is the most exported topic from Europe worldwide. Moreover, important nodes in the communities discussing migration-related topics include accounts of official institutions and international agencies, together with journalists, news, commentators and activists.

Adelar Fochezatto, Carlos Henán Rodas Céspedes

Para promover o desenvolvimento regional, é importante conhecer como se comportam os indicadores demográficos empresariais nas diferentes regiões. Este estudo calcula indicadores anuais de nascimento, mortalidade, rotatividade e de sobrevivência de empresas nas mesorregiões do Rio Grande do Sul (RS) no período de 2006 a 2013. Para isso, é utilizada uma base de dados identificada que possibilita o acompanhamento longitudinal de todas as empresas formais. Os resultados mostram que as diferenças entre as mesorregiões são pequenas, indicando pouca influência da localização espacial sobre a demografia de empresas no curto prazo. Os resultados mostram, também, que as maiores taxas de sobrevivência de empresas estão na mesorregião Nordeste enquanto que as maiores taxas de nascimento e de rotatividade estão na mesorregião Sudeste do RS.

Chethan Parameshwara, Alessandro Achille, Matthew Trager et al.

We describe a first step towards learning general-purpose visual representations of physical scenes using only image prediction as a training criterion. To do so, we first define "physical scene" and show that, even though different agents may maintain different representations of the same scene, the underlying physical scene that can be inferred is unique. Then, we show that NeRFs cannot represent the physical scene, as they lack extrapolation mechanisms. Those, however, could be provided by Diffusion Models, at least in theory. To test this hypothesis empirically, NeRFs can be combined with Diffusion Models, a process we refer to as NeRF Diffusion, used as unsupervised representations of the physical scene. Our analysis is limited to visual data, without external grounding mechanisms that can be provided by independent sensory modalities.

Carlo Manzo, Gorka Muñoz-Gil, Giovanni Volpe et al.

Preface to the special issue "Characterisation of Physical Processes from Anomalous Diffusion Data" associated with the Anomalous Diffusion Challenge ( https://andi-challenge.org ) and published in Journal of Physics A: Mathematical and Theoretical. The list of articles included in the special issue can be accessed at https://iopscience.iop.org/journal/1751-8121/page/Characterisation-of-Physical-Processes-from-Anomalous-Diffusion-Data .

Zdenka Křenová, Tomáš Janík, Dušan Romportl

Abstract Appropriate management of European spruce forests in this time of climate change is a significant challenge. Uniform principles of quality forest practice must be applied, essentially in forests that are valuable from a conservation point of view. However, this may be problematic in protected areas with mixed ownership and disparate priorities. Managerial decision‐making is often based on personal knowledge, previous experience, priorities other than conservation, or political influence. Clear management guidelines at the European and national levels are lacking in many protected areas. In this article, we use data from the Šumava NP to elucidate varied management approaches applied by state and private municipal owners in forests of high conservation value. We used high‐resolution aerial photographs to evaluate the effects of forest management practices applied, and compared the status of spruce habitats before and after a major windstorm and large‐scale bark beetle outbreak. We found that follow‐up forest management measures have significantly affected forests in the Šumava NP negatively. However, changes are not equally spread across the area of study, with forest ownership important for the type and intensity of changes. Kašperské Hory Municipality, managed their forests more as a productive commercial forest as contrasted to the state owned forest. We compare our findings with experiences from other Central European national parks with similar forest management challenges. Options for improving and unifying the management of conservation‐valuable forests held in state and private ownership are discussed in the conclusion. Wide political consensus and stability together with long‐term mutual trust and a willingness to collaborate among conservationists, forest managers and forest owners were recognized to be essential for the successful achievement of conservation goals.

Mathew Thomas Arun

The detection of neutron-antineutron oscillation will be a discovery of fundamental importance in particle physics and cosmology. In models discussed widely in literature, the process is generated through heavy New Physics with weak, perturbative, coupling at the scale of the experiment. Acknowledging the fact that Nature has been quite evasive regarding the strength and scale of New Physics, we discuss a new mechanism, generated by confining New Physics, at $\sim 2$ GeV, resulting in low-energy baryon number violating effects. The mechanism predicts baryon number violating processes like neutron disappearance, neutron-neutron annihilation and neutron-anti neutron oscillation generated through the condensation of the linear moose.

Lingling Zhao, Fei Xu, Jun Xia et al.

Potential evapotranspiration (PET) is a comprehensive factor that characterises climate change, and considering the numerous methods to calculate PET, it is difficult to objectively select a method according to the requirements. In this study, the applicability of 12 commonly used PET estimation methods in China was studied. Based on temperature and humidity, China is divided into 11 temperature zones (TZ) and 5 arid and humid regions (AHRs). The study used the FAO Penman–Monteith (P-M) method as the standard, and the applicability of the 12 methods was analysed using four factors: correlation, annual mean values, seasonal distribution, and parameter characteristics. The results show that the radiation-based methods have the best monthly correlation with the P-M method, the temperature-based methods are second best, and mass-transfer-based methods perform the worst. Among these, the P-T method is the best, and the Hamon method is the worst. The Kharrufa and Abtew methods have the better applicability in higher TZs, whereas the Harg method has the least applicability. The seasonal distribution of radiation-based methods (excluding the Jensen method) in the different AHRs and different TZs is better than that of temperature-based and mass-transfer-based methods. According to the evaluation results of all factors, the Rohwer, P-T, and Mark methods are recommended when the data conditions are not conducive for the P-M method. HIGHLIGHTS There are many methods to calculate PET, while it is difficult to choose objectively according to the needs.; The applicability of 12 commonly used PET estimation methods in China was studied from four aspects.; Variation range of the national annual value estimated is 288.72–1355.1 mm.; The P-M method and energy-based methods have the highest monthly correlation.; The Rohwer, P-T, and Mark methods are recommended.;

Alexey Potapov, Martin McCoustra

Dust grains play a central role in the physics and chemistry of cosmic environments. They influence the optical and thermal properties of the medium due to their interaction with stellar radiation; provide surfaces for the chemical reactions that are responsible for the synthesis of a significant fraction of key astronomical molecules; and they are building blocks of pebbles, comets, asteroids, planetesimals, and planets. In this paper, we review experimental studies of physical and chemical processes, such as adsorption, desorption, diffusion, and reactions forming molecules, on the surface of reliable cosmic dust grain analogues as related to processes in diffuse, translucent, and dense interstellar clouds, protostellar envelopes, planet-forming disks, and planetary atmospheres. The information that such experiments reveal should be flexible enough to be used in many different environments. In addition, we provide a forward look discussing new ideas, experimental approaches, and research directions.

Steven Atkinson, Yiming Zhang, Liping Wang

There has been a surge in the interest of using machine learning techniques to assist in the scientific process of formulating knowledge to explain observational data. We demonstrate the use of Bayesian Hidden Physics Models to first uncover the physics governing the propagation of acoustic impulses in metallic specimens using data obtained from a pristine sample. We then use the learned physics to characterize the microstructure of a separate specimen with a surface-breaking crack flaw. Remarkably, we find that the physics learned from the first specimen allows us to understand the backscattering observed in the latter sample, a qualitative feature that is wholly absent from the specimen from which the physics were inferred. The backscattering is explained through inhomogeneities of a latent spatial field that can be recognized as the speed of sound in the media.

Julie Vallée, Maxime Lenormand

Hour-by-hour variations in spatial distribution of gender, age and social class within cities remain poorly explored and combined in the segregation literature mainly centered on home places from a single social dimension. Taking advantage of 49 mobility surveys compiled together (385,000 respondents and 1,711,000 trips) and covering 60% of France's population, we consider variations in hourly populations of 2,572 districts after disaggregating population across gender, age and education level. We first isolate five district hourly profiles (two 'daytime attractive', two 'nighttime attractive' and one more 'stable') with very unequal distributions according to urban gradient but also to social groups. We then explore the intersectional forms of these everyday geographies. Taking as reference the dominant groups (men, middle-age and high educated people) known as concentrating hegemonic power and capital, we analyze specifically whether district hourly profiles of dominant groups diverge from those of the others groups. It is especially in the areas exhibiting strong increase or strong decrease of ambient population during the day that district hourly profiles not only combine the largest dissimilarities all together across gender, age and education level but are also widely more synchronous between dominant groups than between non-dominant groups (women, elderly and low educated people). These intersectional patterns shed new light on areas where peers are synchronously located over the 24-hour period and thus potentially in better position to interact and to defend their common interests.

Bikas Chandra Bhattarai, John Faulkner Burkhart, Lena M. Tallaksen et al.

Discharge over the Narayani river catchment of Nepal was simulated using Statkraft's Hydrologic Forecasting Toolbox (Shyft) forced with observations and three global forcing datasets: (i) ERA-Interim (ERA-I), (ii) Water and Global Change (WATCH) Forcing Data ERA-I (WFDEI), and (iii) Coordinated Regional Climate Downscaling Experiment with the contributing institute Rossy Centre, Swedish Meteorological and Hydrological Institute (CORDEX-SMHI). Not only does this provide an opportunity to evaluate discharge variability and uncertainty resulting from different forcing data but also it demonstrates the capability and potential of using these global datasets in data-sparse regions. The fidelity of discharge simulation is the greatest when using observations combined with the WFDEI forcing dataset (hybrid datasets). These results demonstrate the successful application of global forcing datasets for regional catchment-scale modeling in remote regions. The results were also promising to provide insight of the interannual variability in discharge. This study showed that while large biases in precipitation can be reduced by applying a precipitation correction factor (p_corr_factor), the best result is obtained using bias-corrected forcing data as input, i.e. the WFDEI outperformed other forcing datasets. Accordingly, the WFDEI forcing dataset holds great potential for improving our understanding of the hydrology of data-sparse Himalayan regions and providing the potential for prediction. The use of CORDEX-SMHI- and ERA-I-derived data requires further validation and bias correction, particularly over the high mountain regions.

Hugo Frezat, Guillaume Balarac, Julien Le Sommer et al.

In this paper we present a new strategy to model the subgrid-scale scalar flux in a three-dimensional turbulent incompressible flow using physics-informed neural networks (NNs). When trained from direct numerical simulation (DNS) data, state-of-the-art neural networks, such as convolutional neural networks, may not preserve well known physical priors, which may in turn question their application to real case-studies. To address this issue, we investigate hard and soft constraints into the model based on classical transformation invariances and symmetries derived from physical laws. From simulation-based experiments, we show that the proposed transformation-invariant NN model outperforms both purely data-driven ones as well as parametric state-of-the-art subgrid-scale models. The considered invariances are regarded as regularizers on physical metrics during the a priori evaluation and constrain the distribution tails of the predicted subgrid-scale term to be closer to the DNS. They also increase the stability and performance of the model when used as a surrogate during a large-eddy simulation. Moreover, the transformation-invariant NN is shown to generalize to regimes that have not been seen during the training phase.