Search Results (27)

In Spain, inbound European tourism exhibits marked territorial imbalances whose evolution is difficult to characterize using aggregate indicators. This study analyzes its spatiotemporal patterns at the municipal level between July 2019 and December 2025 based on experimental statistics from the National Institute of Statistics compiled using mobile phone data. The objective is to identify processes of growth, persistence, and spatial intensification using a geospatial methodology based on the Space–Time Cube (STC) and Emerging Hot Spot Analysis (EHSA). The analysis covers the 1000 municipalities with the highest cumulative volume of European tourists, which account for most of the flows recorded during the period. The results show positive and statistically significant temporal trends in 911 municipalities, although the formation of persistent spatial clusters is considerably less widespread. EHSA identified 48 municipalities classified as hot spots when applying a one-month temporal neighborhood and 77 when using a three-month configuration. The two classifications showed an observed agreement of 96.0% and, for the four shared categories, a Cohen’s kappa coefficient of 0.660. The post-pandemic recovery in tourism did not, therefore, result in a homogeneous territorial consolidation of stable spatial patterns. We identify persistent hubs, areas undergoing intensification, and destinations with episodic behavior, located primarily in metropolitan, coastal, and island areas. The main contribution of the study lies in the development of a reproducible workflow based on the STC–EHSA integration, capable of distinguishing between temporal growth, persistence, intensification, and spatial intermittency, and of evaluating the stability of the results under different temporal neighborhood configurations. Full article

A central challenge in tight fault-depression reservoirs is understanding how three-dimensional fracture structures control gas storage and flow. This study introduces a data-driven, geologically informed framework that integrates structural-mechanical coupling to decipher fracture networks within the Shahezi Formation. Our model, based on rock failure criteria, achieves quantitative fracture prediction across one-dimensional to three-dimensional scales. This capability overcomes the limitations inherent in single-method approaches for tight, fracture-dominated reservoirs. By synthesizing sedimentary facies-controlled reservoir modeling, sweet-spot inversion, and geo-engineering integration, we establish a predictive system for accurate reservoir assessment. The continental clastic Shahezi Formation is typified by secondary fractures. This study utilizes leverage small-scale data (core, thin section, log) to quantify key parameters (fracture density, aperture), enabling a systematic analysis of fracture typology, heterogeneity, and controls. Building on this foundation, and spatially constrained by large-scale datasets (seismic interpretation, stress-field simulations), we developed a robust fracture development model for deep tight reservoirs. Stress-field modeling delineated fracture-prone zones, where a discrete fracture network (DFN) model was built to characterize 3D fracture geometry and connectivity. Integrating simulated fracture size and aperture-derived permeability allowed us to quantify fracture contribution to total permeability, ultimately mapping favorable targets. The results identify favorable zones primarily in the western sector of the study area, forming an NS-trending, belt-like distribution. They are mainly concentrated around the wells Changshen-4, Changshen-40, and Changshen-41. This distribution is clearly controlled by the Qianshenzijing Fault. Full article

Ground-based optical equipment for detecting geostationary orbit space targets typically involves long-exposure imaging, facing challenges such as small and blurred target images, complex backgrounds, and star streaks obstructing the view. To address these issues, this study proposes a GSTD-DETR model based on Real-Time Detection Transformer (RT-DETR), which aims to balance model efficiency and detection accuracy. First, we introduce a Dynamic Cross-Stage Partial (DynCSP) backbone network for feature extraction and fusion, which enhances the network’s representational capability by reducing convolutional parameters and improving information exchange between channels. This effectively reduces the model’s parameter count and computational complexity. Second, we propose a ResFine model with a feature pyramid designed for small target detection, enhancing its ability to perceive small targets. Additionally, we improve the detection head and incorporate a Dynamic Multi-Channel Attention mechanism, which strengthens the focus on critical regions. Finally, we designed an Area-Weighted NWD loss function to improve detection accuracy. The experimental results show that compared to RT-DETR-r18, the GSTD-DETR model reduces the parameter count by 29.74% on the SpotGEO dataset. Its AP₅₀ and AP_50:95 improve by 1.3% and 4.9%, reaching 88.6% and 49.9%, respectively. The GSTD-DETR model demonstrates superior performance in the detection accuracy of faint and small space targets. Full article

The widespread use of mobile phones and social media platforms provides valuable information about users’ behavior and activities. Mobile phone data are rich on positional information, but lack semantic context. Conversely, geo-social media data reveal users’ opinions and activities, but are rather sparse in space and time. In the context of emergency management, both data types have been considered separately. To exploit their complementary nature and potential for emergency management, this paper introduces a novel methodology for improving situational awareness with the focus on urban events. For crowd detection, a spatial hot spot analysis of mobile phone data is used. The analysis of geo-social media data involves building spatio-temporal topic-sentiment clusters of posts. The results of the spatio-temporal contextual enrichment include unusual crowds associated with topics and sentiments derived from the analyzed geo-social media data. This methodology is demonstrated using the case study of the Vienna Pride. The results show how crowds change over time in terms of their location, size, topics discussed, and sentiments. Full article

Habitat mapping plays a crucial role in assessing marine protected areas (MPA) and implementing marine spatial management approaches. This study aims to present the spatial habitat distribution of the Jabal Ali Marine Sanctuary, considering the development projects implemented in its proximity. It serves as a reference for guiding conservation management efforts. The study focuses on in situ hyperspectral measurements of the optical properties of both the water column and the substrate. Additionally, a high density of geo-referenced spot checks were conducted, serving as sample points for ecological evaluation and ground-truthing. An “object-oriented” approach was adopted to generate the seabed map in two evaluated studies conducted in 2006 and 2017. While the 2017 survey identified 16 habitats, the 2006 study characterized only 10 habitats. These habitat maps serve as powerful tools for implementing mitigation measures and providing scientific support to mitigate the negative impact on the most crucial marine habitats within the context of a protected area management framework. Furthermore, monitoring the cover of the most important habitats provides an integrative indicator to maintain the good environmental status of the marine sanctuary. Based on this study, the information will be a reference for evaluating and synergizing the management approaches implemented by both the competent authority and the different stakeholders in the sanctuary. Full article

With the increasing interest in space science exploration, the number of spacecraft in Earth’s orbit has been steadily increasing. To ensure the safety and operational integrity of active satellites, advanced surveillance and early warning of unknown space objects such as space debris are crucial. The traditional threshold-based filter for space object detection heavily relies on manual settings, leading to limitations such as poor flexibility, high false alarm rates, and weak target detection capability in low signal-to-noise ratios. Therefore, detecting faint and small objects against a complex starry background remains a formidable challenge. To address this challenge, we propose a novel, intelligent, and accurate detection method called You Only Look Once for Space Object Detection (SOD-YOLO). Our method includes the following novel modules: Multi-Channel Histogram Truncation (MHT) enhances feature representation, CD-ELAN based on Central Differential Convolution (CDC) facilitates learning contrast information, the Space-to-Depth (SPD) module replaces pooling layer to prevent small object feature loss, a simple and parameter-free attention module (SimAM) expands receptive field for Global Contextual Information, and Alpha-EIoU optimizes the loss function for efficient training. Experiments on our SSOD dataset show SOD-YOLO has the ability to detect objects with a minimum signal-to-noise ratio of 2.08, improves AP by 11.2% compared to YOLOv7, and enhances detection speed by 42.7%. Evaluation on the Spot the Geosynchronous Orbit Satellites (SpotGEO) dataset demonstrates SOD-YOLO’s comparable performance to state-of-the-art methods, affirming its generalization and precision. Full article

Rock glaciers are an integral part of the periglacial environment. At the regional scale in the Greater Caucasus, there have been no comprehensive systematic efforts to assess the distribution of rock glaciers, although some individual parts of ranges have been mapped before. In this study we produce the first inventory of rock glaciers from the entire Greater Caucasus region—Russia, Georgia, and Azerbaijan. A remote sensing survey was conducted using Geo-Information System (GIS) and Google Earth Pro software based on high-resolution satellite imagery—SPOT, Worldview, QuickBird, and IKONOS, based on data obtained during the period 2004–2021. Sentinel-2 imagery from the year 2020 was also used as a supplementary source. The ASTER GDEM (2011) was used to determine location, elevation, and slope for all rock glaciers. Using a manual approach to digitize rock glaciers, we discovered that the mountain range contains 1461 rock glaciers with a total area of 297.8 ± 23.0 km². Visual inspection of the morphology suggests that 1018 rock glaciers with a total area of 199.6 ± 15.9 km² (67% of the total rock glacier area) are active, while the remaining rock glaciers appear to be relict. The average maximum altitude of all rock glaciers is found at 3152 ± 96 m above sea level (a.s.l.) while the mean and minimum altitude are 3009 ± 91 m and 2882 ± 87 m a.s.l., respectively. We find that the average minimum altitude of active rock glaciers is higher (2955 ± 98 m a.s.l.) than in relict rock glaciers (2716 ± 83 m a.s.l.). No clear difference is discernible between the surface slope of active (41.4 ± 3°) and relict (38.8 ± 4°) rock glaciers in the entire mountain region. This inventory provides a database for understanding the extent of permafrost in the Greater Caucasus and is an important basis for further research of geomorphology and palaeoglaciology in this region. The inventory will be submitted to the Global Land Ice Measurements from Space (GLIMS) database and can be used for future studies. Full article

Background: Obstructive sleep apnea (OSA) is highly prevalent yet underdiagnosed. This study aimed to develop a predictive signature, as well as investigate competing endogenous RNAs (ceRNAs) and their potential functions in OSA. Methods: The GSE135917, GSE38792, and GSE75097 datasets were collected from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database. Weighted gene correlation network analysis (WGCNA) and differential expression analysis were used to identify OSA-specific mRNAs. Machine learning methods were applied to establish a prediction signature for OSA. Furthermore, several online tools were used to establish the lncRNA-mediated ceRNAs in OSA. The hub ceRNAs were screened using the cytoHubba and validated by real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR). Correlations between ceRNAs and the immune microenvironment of OSA were also investigated. Results: Two gene co-expression modules closely related to OSA and 30 OSA-specific mRNAs were obtained. They were significantly enriched in the antigen presentation and lipoprotein metabolic process categories. A signature that consisted of five mRNAs was established, which showed a good diagnostic performance in both independent datasets. A total of twelve lncRNA-mediated ceRNA regulatory pathways in OSA were proposed and validated, including three mRNAs, five miRNAs, and three lncRNAs. Of note, we found that upregulation of lncRNAs in ceRNAs could lead to activation of the nuclear factor kappa B (NF-κB) pathway. In addition, mRNAs in the ceRNAs were closely correlated to the increased infiltration level of effector memory of CD4 T cells and CD56^bright natural killer cells in OSA. Conclusions: In conclusion, our research opens new possibilities for diagnosis of OSA. The newly discovered lncRNA-mediated ceRNA networks and their links to inflammation and immunity may provide potential research spots for future studies. Full article

During the end of the Bronze Age, the territory of present-day eastern Romania was occupied by Noua communities, belonging to the Noua-Sabatinovka-Coslogeni (NSC) cultural complex. Although these communities have left us a large number of archaeological sites, this period is rather poorly known and understood, mostly because the investigation of Late Bronze Age (LBA) sites is very rare, usually consisting of small test trenches or fieldwalks. The main characteristic of these communities and the subject of our study is represented by the so-called ashmounds (grey, quasi-circular spots, visible on the soil surface, with small elevations and diameters of 25–30 m), present inside most settlements. Our paper aims at highlighting the spatial characteristics of these sites, using GIS (Geographic Information System) tools, as well as aerial photographs, LiDAR (Light Detection and Ranging) measurements, magnetometry and geo-electrical methods, in order to identify the relationship existing between Noua communities and the inhabited environment, in the area known as the Jijia River catchment. Thus, our approach was able to outline the way in which the geographical peculiarities determined the establishment of new settlements, revealing that the human groups from the end of the Bronze Age preferred low terrains with smooth slopes, located in the immediate vicinity of the most important watercourse of the inhabited micro-area. Additionally, our geophysical studies allowed us to confirm the lack of ash located within the ashmound, as well as to signal the possibility that these features have become visible on the soil surface only due to the irreversible damage caused by intensive agricultural processes. Despite the small number of excavations, to this day an important number of studies have been dedicated to the communities and features in question; however, no analysis has yet been performed that unites the tools specific to GIS software with the usage of non-invasive methods (such as aerial photographs, LiDAR measurements and geophysical techniques). Full article

Recently, one of the major environmental issues is the pollution of soil with Antimony (Sb), which is ecologically detrimental and potentially carcinogenic to humans. In developing countries such as Iran, Sb concentrations in soils have not yet been accurately determined. Therefore, the purpose of this study was to examine the levels of Sb and the other HMs (Pb, Cd, As, Ni, Hg and Cr) in the surface soils of Bojnourd, Iran, as well as their distribution and potential risks to the environment and human health. A total of 37 soil samples (0–20 cm) were taken from different sampling stations: 900 × 900 m regular grid for traffic areas (TA), residential areas (RA) and suburb areas (SA). The contamination factor (CF) and geo-accumulation index (I_geo) are two indices that were used to reflect the potential ecological risk from HMs. Geographic information system (GIS), Spearman correlation matrix and health risk indexes were utilized to investigate the source and potential hazard of HMs. The results showed that the average concentration of HMs in TA was highly enriched compared to other areas. Most soil samples were identified to have low levels of Sb and Ni pollution, while having moderate to high levels of Pb, Cr, As, Hg and Cd pollution, as determined by the pollution indices (I_geo and CF). Geostatistical analysis and GIS mapping of the spatial distribution of HM concentrations showed that there have been similar patterns of spatial distribution for Cd, Cr, Ni and Sb and their hot spots were in the southeast, west and center of the city. Neither the hazard quotient (HQ) nor the hazard index (HI) of the examined HMs indicated any non-carcinogenic risk to adults or children. However, carcinogenic risk assessment revealed that cancer risk was raised from Cr and Cd contents for children, while these elements showed an acceptable risk for adults. Furthermore, children’s carcinogenic and non-carcinogenic values were greater than adults’, indicating more potential health hazards associated with these HMs. Therefore, assessing the risk posed by HM pollution in urban surface soil is vital and urgent for children. A more detailed investigation is also required to identify the spatial distribution of soil pollution in areas recognized as enriched in Sb. A proper assessment of the environmental risk and the corresponding risk to humans from HM in a study area can be critical to developing an appropriate remediation method. Full article

This work summarizes the main characteristics of atmospheric and hydro-physical parameters of the water surface derived from microwave radiometric data. First, current knowledge based on experimental measurements and model calculations of emissivity of the atmosphere and water surface in the microwave spectrum is presented. Emphasis is placed on remote radio-physical methods that have the peculiarity of being related to atmospheric radio-transparency which is one of the main advantages of the microwave radiometric method compared to optical and infrared methods. A detailed presentation is attempted with specific examples of classification of water surface phenomena using software modules included in the system used for the processing of data of radio-physical experiments by the Cosmos-1500 satellite. In addition, the statistical characteristics of the “spotting” of radio-brightness temperatures obtained for the most informative thresholds are analyzed and it is argued that these characteristics for the Pacific areas can also be used to detect abnormal phenomena on the water surface of the Mediterranean Sea. Finally, it is emphasized that the results obtained from this work make it possible to rapidly evaluate various parameters such as temperature, water surface waves, foam formation areas, etc., providing predictions and allocating irregular areas. Full article

Geo-Kompsat-2A (GK-2A) is the third new-generation geostationary meteorological satellite that orbits Asia and monitors China and its surrounding areas, following the Himawari-8 and Fengyun-4A satellites. The nadir point positioning and satellite channel parameters of the GK-2A are better than those of the Himawari-8 and FY-4A, which are more conducive to fire monitoring in China. In this study, a new fire detection algorithm is proposed based on GK-2A satellite data. That is, considering the large solar zenith angle correction for reflectance and the proportion information of background pixels in the existing spatial threshold method, fires under the different underlying surface types and solar radiation states can be automatically identified. Moreover, the accuracy of the Himawari-8 fire monitoring algorithm and the present algorithm of GK-2A is compared and analyzed through the ground truth fire spot data. The results show that compared with the original fire monitoring algorithm with fixed parameter thresholds, the brightness temperature difference of this algorithm is reduced by 0.55 K, and the correction coefficient is reduced by 0.6 times, the fire can be found earlier, and the monitoring sensitivity is improved. According to the practical fire case, the present fire monitoring algorithm of GK-2A has better monitoring accuracy than the fire monitoring algorithm of Himawari-8. The present fire monitoring algorithm of GK-2A can meet the fire monitoring requirements under different sun angles, different cloud cover ratios and vegetation ratios with good versatility. Full article

The limited resource of Geostationary Earth Orbit (GEO) is precious and most telecommunication, weather and navigational satellites are placed in this orbit. In order to guarantee the safety and health of active satellites, advanced surveillance and warning of unknown space targets such as space debris are crucial. However, space object detection still remains a very challenging problem because of the weak target characteristics and complex star background. To solve this problem, we conduct a deep-learning-based framework called PP-YOLOv2 for single-frame object detection and design a post-processing algorithm named CFS for further candidate filtration and supplement. First, we transform the label information and generate the according bounding boxes to train the PP-YOLOv2 detector to extract candidate coordinates for each frame. Then, the CFS technique is designed as an effective post-processing procedure to obtain the eventual prediction results. Experiments were conducted over a dataset from the Kelvins SpotGEO challenge, which demonstrate the effectiveness and the comparable detection performance of our proposed pipeline. Finally, the deployment results on NVIDIA Jetson Nano show that the proposed method has a competitive application prospect for a space target monitoring system. Full article

The current study intended to geospatially analyze the potentiality and site suitability of geo-ecotourism in West Bengal, India. The state of West Bengal is a great platform for diverse tourism and has enormous potential to cultivate geo-ecotourism, as has come up in recent years. The current effort throws some valuable light on the possibility of turning the many geologically, geomorphologically and ecologically significant tourist spots of West Bengal into geo-ecotourism sites, aided with geospatial techniques. The study deals with the qualitative and quantitative investigation of the potentiality of the whole state by dividing it into several geo-ecotourism zones, based on its physiographic setting and Land Use Land Cover (LULC) features, using satellite image data. The application of geospatial technology combined with Remote Sensing (RS) and Geographic Information System (GIS) was employed for this geospatial analysis to portray the potential zones using cartographic and statistical techniques. Furthermore, nine criteria were selected to run the Analytic Hierarchy Process (AHP) method to determine the site suitability for geo-ecotourism. The present submission attempts to record the mapping and analysis of geo-ecotourism of West Bengal employing a secondary database, an expert’s opinions and primary observations, with the application of the AHP method and GIS. The outcomes of the study were found to be very significant, as they indicate a proviso for geo-ecotourism development in the state and will contribute to the formation of location-specific planning and the sustainable management of geo-ecotourism. Full article

Traffic accidents in urban areas lead to reduced quality of life and added pressure in the cities’ infra-structures. In the context of smart city data is becoming available that allows a deeper analysis of the phenomenon. We propose a data fusion process from different information sources like road accidents, weather conditions, local authority reports tools, traffic, fire brigade. These big data analytics allow the creation of knowledge for local municipalities using local data. Data visualizations allow big picture overview. This paper presents an approach to the geo-referenced accident-hotspots identification. Using ArcGIS Pro, we apply Kernel Density and Hot Spot Analysis (Getis-Ord Gi*) tools, identifying the existence of black spots in terms of location and context conditions, and evaluate the possible human, environmental and circumstantial factors that may influence the severity of accidents. The results were validated by an expert committee. This approach can be applied to other cites wherever this data is available. Full article