Search Results (748)

Recent approaches to geo-referencing X posts have focused on the use of language modelling techniques that learn geographic region-specific language and use this to infer geographic coordinates from text. These approaches rely on large amounts of labelled data to build accurate predictive models. However, obtaining significant volumes of geo-referenced data from Twitter, recently renamed X, can be difficult. Further, existing language modelling approaches can require the division of a given area into a grid or set of clusters, which can be dataset-specific and challenging for location prediction at a fine-grained level. Regression-based approaches in combination with deep learning address some of these challenges as they can assign coordinates directly without the need for clustering or grid-based methods. However, such approaches have received only limited attention for the geo-referencing task. In this paper, we adapt state-of-the-art neural network models for the regression task, focusing on geo-referencing wildlife Tweets where there is a limited amount of data. We experiment with different transfer learning techniques for improving the performance of the regression models, and we also compare our approach to recently developed Large Language Models and prompting techniques. We show that using a location names extraction method in combination with regression-based disambiguation, and purely regression when names are absent, leads to significant improvements in locational accuracy over using only regression. Full article

Numerous machine learning models are viewed as an important means for evaluating the built environment (BE) features and travel behavior. However, most of them ignore the interaction effects of the BE and geographic locations. To strengthen their spatial interpretability, the study combines the random forest and GeoShapley method to scrutinize the nonlinear and spatial interaction effects of the BE features on ride-hailing demand using multi-source data from Nanjing, China. The results indicate that the land use mixture, the interaction between the distance to city center and geographic locations, and geographic locations are the most essential factors influencing ride-hailing demand. All BE features exhibit nonlinear effects on ride-hailing demand. Moreover, Among the BE features, distance to city center, land use mixture, and distance to metro stop demonstrate significant interaction effects with geographic locations. The findings indicate the necessity of incorporating geospatial analysis into the relationships and offer implications for implementing location-specific strategies. Full article

The disappearance of foreign minors in Italy is a long-standing and critically underexamined social phenomenon. Despite alarming figures, public and institutional attention remains episodic and media-driven, often limited to high-profile or criminal cases. This study offers a socio-forensic analysis of official data from 2014 to 2023, revealing significant inconsistencies in how these cases are reported, categorized, and followed up by Italian institutions. It highlights how unaccompanied and migrant minors are especially vulnerable within a fragmented and reactive system that lacks transparency and effective preventive measures. Rather than presenting new empirical data, the article reinterprets existing sources to expose systemic gaps, drawing comparisons with the more structured approaches adopted in countries such as the United States, United Kingdom, and Spain. These international examples show how multilingual communication, early warning systems (e.g., AMBER Alert), and public geolocation tools can offer timely, coordinated responses to disappearances—tools that remain largely absent or underused in Italy. The article further argues for the integration of forensic geospatial methods, such as locus operandi analysis, remote sensing, and forensic geoarchaeology, not as experimental techniques, but as practical tools that could strengthen Italy’s institutional capacity to respond. Ultimately, this study seeks to elevate the discussion surrounding missing foreign minors from a marginal social concern to a matter of forensic and public interest, and to encourage interdisciplinary reflection on how such disappearances are framed—and too often dismissed—within the national landscape. Full article

The Santurbán paramo is a sensitive high-mountain ecosystem exposed to pressures from extractive and agricultural activities, as well as increasing tourism. In response, this study presents a context-aware recommendation system designed to support sustainable tourism through the integration of deep neural networks and ontology-based semantic modeling. The proposed system delivers personalized recommendations—such as activities, accommodations, and ecological routes—by processing user preferences, geolocation data, and contextual features, including cost and popularity. The architecture combines a trained TensorFlow Lite model with a domain ontology enriched with GeoSPARQL for geospatial reasoning. All inference operations are conducted locally on Android devices, supported by SQLite for offline data storage, which ensures functionality in connectivity-restricted environments and preserves user privacy. Additionally, the system employs geofencing to trigger real-time environmental notifications when users approach ecologically sensitive zones, promoting responsible behavior and biodiversity awareness. By incorporating structured semantic knowledge with adaptive machine learning, the system enables low-latency, personalized, and conservation-oriented recommendations. This approach contributes to the sustainable management of natural reserves by aligning individual tourism experiences with ecological protection objectives, particularly in remote areas like the Santurbán paramo. Full article

Building height information plays an important role in many urban-related applications, such as urban planning, disaster management, and environmental studies. With the rapid development of real scene maps, street view images are becoming a new data source for building height estimation, considering their easy collection and low cost. However, existing studies on building height estimation primarily utilize remote sensing images, with little exploration of height estimation from street-view images. In this study, we proposed a deep learning-based method for estimating the height of a single building in Baidu panoramic street view imagery. Firstly, the Segment Anything Model was used to extract the region of interest image and location features of individual buildings from the panorama. Subsequently, a cross-view matching algorithm was proposed by combining Baidu panorama and building footprint data with height information to generate building height samples. Finally, a Two-Branch feature fusion model (TBFF) was constructed to combine building location features and visual features, enabling accurate height estimation for individual buildings. The experimental results showed that the TBFF model had the best performance, with an RMSE of 5.69 m, MAE of 3.97 m, and MAPE of 0.11. Compared with two state-of-the-art methods, the TBFF model exhibited robustness and higher accuracy. The Random Forest model had an RMSE of 11.83 m, MAE of 4.76 m, and MAPE of 0.32, and the Pano2Geo model had an RMSE of 10.51 m, MAE of 6.52 m, and MAPE of 0.22. The ablation analysis demonstrated that fusing building location and visual features can improve the accuracy of height estimation by 14.98% to 69.99%. Moreover, the accuracy of the proposed method meets the LOD1 level 3D modeling requirements defined by the OGC (height error ≤ 5 m), which can provide data support for urban research. Full article

Background: Gastric cancer (GC) represents a significant global health burden with considerable heterogeneity in clinical and molecular behavior. The anatomical site of tumor origin—proximal versus distal—has emerged as a determinant of prognosis and response to therapy. The aim of this paper is to elucidate the transcriptional and regulatory differences between proximal gastric cancer (PGC) and distal gastric cancer (DGC) through master regulator (MR) analysis. Methods: We analyzed RNA-seq data from TCGA-STAD and microarray data from GEO (GSE62254, GSE15459). Differential gene expression and MR analyses were performed using DESeq2, limma, corto, and RegEnrich pipelines. A harmonized matrix of 4785 genes was used for MR inference following normalization and batch correction. Functional enrichment and survival analyses were conducted to explore prognostic associations. Results: Among 364 TCGA and 492 GEO patients, PGC was associated with more aggressive clinicopathological features and poorer outcomes. We identified 998 DEGs distinguishing PGC and DGC. PGC showed increased FOXM1 (a key regulator of cell proliferation), STAT3, and NF-κB1 activity, while DGC displayed enriched GATA6, CDX2 (a marker of intestinal differentiation), and HNF4A signaling. Functional enrichment highlighted proliferative and inflammatory programs in PGC, and differentiation and metabolic pathways in DGC. MR activity stratified survival outcomes, reinforcing prognostic relevance. Conclusions: PGC and DGC are governed by distinct transcriptional regulators and signaling networks. Our findings provide a biological rationale for location-based stratification and inform targeted therapy development. Full article

Topographic slope is a key parameter for characterizing landscape geomorphology. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) offers high-resolution along-track slopes based on the ground profiles generated by dense signal photons. However, the across-track slopes are typically derived using the ground photon geolocations from the weak-beam and strong-beam pair, limiting the retrieval accuracy and losing valid results over rugged terrains. The goal of this study is to propose a new method to derive the across-track slope merely using single-track photon data of a strong beam based on the theoretical formula of the received signal pulse width. Based on the ICESat-2 photon data over the Walker Lake area, the specific purposes are to (1) extract the along-track slope and surface roughness from the signal photon data on the ground; (2) generate an elevation frequency histogram (EFH) and calculate its root mean square (RMS) width; and (3) derive the across-track slope from the RMS width of the EFH and evaluate the retrieval accuracy against the across-track slope from the ICESat-2 product and plane fitting method. The results show that the mean absolute error (MAE) obtained by our method is 11.45°, which is comparable to the ICESat-2 method (11.61°) and the plane fitting method (12.51°). Our method produces the least invalid data proportion of ~2.5%, significantly outperforming both the plane fitting method (10.29%) and the ICESat-2 method (32.32%). Specifically, when the reference across-track slope exceeds 30°, our method can consistently yield the optimal across-track slopes, where the absolute median, inter quartile range, and whisker range of the across-track slope residuals have reductions greater than 4.44°, 1.31°, and 0.10°, respectively. Overall, our method is well-suited for the across-track slope estimation over rugged terrains and can provide higher-precision, higher-resolution, and more valid across-track slopes. Full article

This study evaluated the performance of the operational GK2A (GEO-KOMPSAT-2A) fog detection algorithm (GK2A_FDA) using ground-based visibility meter data from 176 stations across South Korea from 2021 to 2023. According to the verification method using the nearest pixel and 3 × 3 neighborhood pixel approaches to the visibility meter, the 3-year average probability of detection (POD) is 0.59 and 0.70, the false alarm ratio (FAR) is 0.86 and 0.81, and the bias is 4.25 and 3.73, respectively. POD is highest during daytime (0.72; bias: 7.34), decreases at night (0.57; bias: 3.89), and is lowest at twilight (0.52; bias: 2.36). The seasonal mean POD is 0.65 in winter, 0.61 in spring and autumn, and 0.47 in summer, with August reaching the minimum value, 0.33. While POD is higher in coastal areas than inland areas, inland regions show lower FAR, indicating more stable performance. Over-detections occurred regardless of geographic location and time, mainly due to the misclassification of low-level clouds and cloud edges as fog. Especially after sunrise, the fog dissipated and transformed into low-level clouds. These findings suggest that there are limitations to improving fog detection levels using satellite data alone, especially when the surface is obscured by clouds, indicating the need to utilize other data sources, such as objective ground-based analysis data. Full article

Activity space offers a valuable perspective for analyzing urban travel behavior and evaluating the performance of transportation systems in increasingly complex urban environments. However, the research on measuring activity spaces in multimodal transportation contexts remains limited. This study investigates multimodal transportation activity spaces in Hangzhou using 2023 smart card data. Multimodal travel chains are extracted, and residents’ activity spaces are quantified using 95% confidence ellipses. By applying the XGBoost and GeoShapley models, this study reveals the nonlinear effects and geospatial heterogeneity in how built environment and socioeconomic factors influence activity spaces. The key findings show that the distance to the nearest metro station, commercial POIs, and GDP significantly shape activity spaces through nonlinear relationships. Moreover, the interaction between the distance to the nearest metro station and geographical location generates pronounced geospatial effects. The results highlight the importance of multimodal integration in urban transport planning and provide empirical insights for enhancing system efficiency and sustainability. Full article

The accurate prediction of soil organic matter (SOM) content is essential for promoting sustainable soil management and addressing global climate change. Due to multiple factors such as topography and climate, especially in mountainous areas, SOM spatial prediction faces significant challenges. The main novelty of this study lies in proposing a geographic positional encoding mechanism that embeds geographic location information into the feature representation of a Transformer model. The encoder structure is further modified to enhance spatial awareness, resulting in the development of the Geo-Positional Transformer (GPTransformer). Furthermore, this model is integrated with a 1D-CNN to form a dual-branch neural network called the Geo-Positional Transformer-CNN (GPTransCNN). This study collected 1490 topsoil samples (0–20 cm) from cultivated land in Longshan County to develop a predictive model for mapping the spatial distribution of SOM across the entire cultivated area. Different models were comprehensively evaluated through ten-fold cross-validation, ablation experiments, and uncertainty analysis. The results show that GPTransCNN has the best performance, with an R² improvement of approximately 43% over the Transformer, 19% over the GPTransformer, and 15% over the 1D-CNN. This study demonstrates that by incorporating geographic positional information, GPTransCNN effectively combines the global modeling capabilities of the GPTransformer with the local feature extraction strengths of the 1D-CNN, which can improve the accuracy of SOM mapping in mountainous areas. This approach provides data support for sustainable soil management and decision-making in response to global climate change. Full article

Residential segregation remains a persistent challenge in European urban environments and is an increasing focal point in urban policy debates. This study investigates the changing geographies of ethnic diversity and residential segregation in Riga, the capital city of Latvia. The research addresses the complex dynamics of ethnic residential patterns within the distinctive context of post-socialist urban transformation, examining how historical legacies of ethnic diversity interact with contemporary migration flows to reshape neighborhood ethnic composition. Using geo-referenced data from 2000, 2011, and 2021 census rounds, we examined changes in the spatial distribution of five major ethnic groups. Our analysis employs the Dissimilarity Index to measure ethnic residential segregation and the Location Quotient to identify the residential concentration of ethnic groups across the city. The findings reveal that Riga’s ethnic landscape is undergoing a gradual yet impactful transformation. The spatial distribution of ethnic groups is shifting, with the increasing segregation of certain groups, particularly traditional ethnic minorities, coupled with a growing concentration of Europeans and non-Europeans in the inner city. The findings reveal distinctive patterns of ethnic diversification and demographic change, wherein long-term trends intersect with contemporary migration dynamics to produce unique trajectories of ethnic residential segregation, which differ from those observed in Western European contexts. However, the specific dynamics in Riga, particularly the persistence of traditional ethnic minority communities and the emergence of new ethnic groups, highlight the unique context of post-socialist urban landscapes. Full article

The full-waveform spaceborne laser altimeter improves footprint geolocation accuracy through waveform matching, providing critical data for on-orbit calibration. However, in areas with significant topographic variations or complex surface characteristics, traditional waveform matching methods based on the Pearson correlation coefficient (PCC-Match) are susceptible to errors from laser ranging inaccuracies and discrepancies in surface structures, resulting in reduced footprint geolocation stability. This study proposes a terrain-constrained cross-correlation matching (TC-Match) method. By integrating the terrain characteristics of the laser footprint area with spaceborne altimetry data, a sliding “time-shift” constraint range is constructed. Within this constraint range, an optimal matching search based on waveform structural characteristics is conducted to enhance the robustness and accuracy of footprint geolocation. Using GaoFen-7 (GF-7) satellite laser footprint data, experiments were conducted in regions of Utah and Arizona, USA, for validation. The results show that TC-Match outperforms PCC-Match regarding footprint geolocation accuracy, stability, elevation correction, and systematic bias correction. This study demonstrates that TC-Match significantly improves the geolocation quality of spaceborne laser altimeters under complex terrain conditions, offering good practical engineering adaptability. It provides an effective technical pathway for subsequent on-orbit calibration and precision model optimization of spaceborne laser data. Full article

Surface all-wave net radiation ($R_n$) is one of the essential parameters to describe surface radiative energy balance, and it is of great significance in scientific research and practical applications. Among various acquisition approaches, the estimation of $R_n$ from satellite data is gaining more and more attention. In order to obtain the daily $R_n$ ($R_{nd}$) from the instantaneous satellite observations, a parameter $C_d$, which is defined as the ratio between the $R_n$ at daily and at instantaneous under clear sky was proposed and has been widely applied. Inspired by the sinusoidal model, a new model for $C_d$ estimation, namely New Model, was proposed based on the comprehensive clear-sky $R_n$ measurements collected from 105 global sites in this study. Compared with existing models, New Model could estimate $C_d$ at any moment during 9:30~14:30 h, only depending on the length of daytime. Against the measurements, New Model was evaluated by validating and comparing it with two popular existing models. The results demonstrated that the $R_{nd}$ obtained by multiplying $C_d$ from New Model had the best accuracy, yielding an overall $R^2$ of 0.95, root mean square error (RMSE) of 14.07 Wm⁻², and Bias of −0.21 Wm⁻². Additionally, New Model performed relatively better over vegetated surfaces than over non- or less-vegetated surfaces with a relative RMSE (rRMSE) of 11.1% and 17.89%, respectively. Afterwards, the New Model $C_d$ estimate was applied with MODIS data to calculate $R_{nd}$. After validation, the $R_{nd}$ computed from $C_d$ was much better than that from the sinusoidal model, especially for the case MODIS transiting only once in a day, with $R_{nd}$-validated $R^2$ of 0.88 and 0.84, RMSEs of 19.60 and 27.70 Wm⁻², and Biases of −0.76 and 8.88 Wm⁻². Finally, more analysis on New Model further pointed out the robustness of this model under various conditions in terms of moments, land cover types, and geolocations, but the model is suggested to be applied at a time scale of 30 min. In summary, although the new $C_d$ model only works for clear-sky, it has the strong potential to be used in estimating $R_{nd}$ from satellite data, especially for those having fine spatial resolution but low temporal resolution. Full article

As a catalyst for sustainable urbanization, urban vibrancy drives human interactions, economic agglomeration, and resilient development through its spatial manifestation of diverse activities. While previous studies have emphasized the connection between built environment features—especially street network centrality—and urban vibrancy, the broader mechanisms through which the full spectrum of street configuration dimensions shape vibrancy patterns remain insufficiently examined. To address this gap, this study applies a GeoXAI approach that synergizes random forest modeling and GeoShapley interpretation to reveal the influence of street configuration on urban vibrancy. Leveraging multi-source geospatial data from Xiamen Island, China, we operationalize urban vibrancy through a composite index derived from three-dimensional proxies: life service review density, social media check-in intensity, and mobile device user concentration. Street configuration is quantified through a tripartite measurement system encompassing network centrality, detour ratio, and shape index. Our findings indicate that (1) street network centrality and shape index, as well as their interactions with location, emerge as the dominant influencing factors; (2) The relationships between street configuration and urban vibrancy are predominantly nonlinear, exhibiting clear threshold effects; (3) The impact of street configuration is spatially heterogeneous, as evidenced by geographically varying coefficients. The findings can enlighten urban planning and design by providing a basis for the development of nuanced criteria and context-sensitive interventions to foster vibrant urban environments. Full article

Visual geolocalization for aerial vehicles based on an analysis of Earth observation imagery is an effective method in GNSS-denied environments. However, existing methods for geographic location estimation have limitations: one relies on high-precision geodetic elevation data, which is costly, and the other assumes a flat ground surface, ignoring elevation differences. This paper presents a novel aerial vehicle geolocalization method. It integrates 2D information and relative depth information, which are both from Earth observation images. Firstly, the aerial and reference remote sensing satellite images are fed into a feature-matching network to extract pixel-level feature-matching pairs. Then, a depth estimation network is used to estimate the relative depth of the satellite remote sensing image, thereby obtaining the relative depth information of the ground area within the field of view of the aerial image. Finally, high-confidence matching pairs with similar depth and uniform distribution are selected to estimate the geographic location of the aerial vehicle. Experimental results demonstrate that the proposed method outperforms existing ones in terms of geolocalization accuracy and stability. It eliminates reliance on elevation data or planar assumptions, thus providing a more adaptable and robust solution for aerial vehicle geolocalization in GNSS-denied environments. Full article