Search Results (300)

Agriculture faces a dual challenge in the context of climate change, serving as both a significant contributor to greenhouse gas (GHG) emissions and a sector highly vulnerable to its impacts. Addressing this requires a transition toward climate-resilient agriculture (CRA). Emerging technologies, including geospatial tools (e.g., Geographic Information Systems (GISs) and remote sensing (RS)), as well as artificial intelligence (AI), offer promising methods to support this transition. However, their individual capabilities, limitations, and appropriate applications are not always well understood or clearly delineated in the literature. A common issue is the frequent overlap between GISs and RS, with many studies assessing GIS contributions while concurrently employing RS techniques, without explicitly distinguishing between the two (or vice versa). In this sense, the objective of this review is to conduct a critical analysis of the existing state of the art in terms of the distinct roles, limitations, and complementarities of GISs, RS, and AI in advancing CRA, guided by an original definition we propose for CRA (structured around three key dimensions and their corresponding targets). Furthermore, this review introduces a synthesis matrix that integrates both the individual contributions and the synergistic potential of these technologies. This synergy-focused matrix offers not just a summary, but a practical decision support matrix that could be used by researchers, practitioners, and policymakers in selecting the most appropriate technological configuration for their objectives in CRA-related work. Such support is increasingly needed, especially considering that RS and AI have experienced exponential growth in the past five years, while GISs, despite being the more established “big brother” among these technologies, remain underutilized and is often insufficiently understood in agricultural applications. Full article

Plant–endophyte symbioses are widespread in grasslands. While symbiotic interactions often provide hosts with major fitness enhancements, the role of the endophyte Alternaria oxytropis, which produces swainsonine in locoweeds (Oxytropis and Astragalus spp.), remains enigmatic. We compared endophyte-infected (E+) and endophyte-free (E−) plants of three main Chinese locoweed species (O. kansuensis, O. glabra, and O. ochrocephala) under controlled conditions, and analyzed environmental factors at locoweed poisoning hotspots for herbivores. The results demonstrated significant species-specific effects: E+ plants of O. glabra and O. ochrocephala exhibited 26–39% reductions in biomass, net photosynthetic rate, and stomatal conductance, with elevated CO₂ levels, while O. kansuensis showed no measurable impacts. Swainsonine concentrations were 16–20 times higher in E+ plants (122.6–151.7 mg/kg) than in E− plants. Geospatial analysis revealed that poisoning hotspots for herbivores consistently occurred in regions with extreme winter conditions (minimum temperatures ≤ −17 °C and precipitation ≤ 1 mm during the driest month), suggesting context-dependent benefits under abiotic stress. These findings suggest that the ecological role of A. oxytropis may vary depending on both host species and environmental context, highlighting a trade-off between growth costs and potential stress tolerance conferred by A. oxytropis. The study underscores the need for field validation to elucidate the adaptive mechanisms maintaining this symbiosis in harsh environments. Full article

With the widespread adoption of mobile devices and the increasing availability of user trajectory data, accurately predicting the next location a user will visit has become a pivotal task in location-based services. Despite recent progress, existing methods often fail to effectively disentangle the diverse and entangled behavioral signals, such as collaborative user preferences, global transition mobility patterns, and geographical influences, embedded in user trajectories. To address these challenges, we propose a novel framework named Multi-Perspective Hypergraphs with Contrastive Learning (MPHCL), which explicitly captures and disentangles user preferences from three complementary perspectives. Specifically, MPHCL constructs a global transition flow graph and two specialized hypergraphs: a collective preference hypergraph to model collaborative check-in behavior and a geospatial-context hypergraph to reflect geographical proximity relationships. A unified hypergraph representation learning network is developed to preserve semantic independence across views through a dual propagation mechanism. Furthermore, we introduce a cross-view contrastive learning strategy that aligns multi-perspective embeddings by maximizing agreement between corresponding user and location representations across views while enhancing discriminability through negative sampling. Extensive experiments conducted on two real-world datasets demonstrate that MPHCL consistently outperforms state-of-the-art baselines. These results validate the effectiveness of our multi-perspective learning paradigm for next-location prediction. 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

Municipal Solid Waste (MSW) management programs can help to mitigate the triple planetary crises of climate change, biodiversity loss and pollution. However, their success largely depends on the public willingness to engage in the pro-environmental separation and delivery of MSW, particularly for difficult-to-manage items such as electronics, batteries and appliances, which often contain toxic materials. Most existing research tends to focus on infrastructure improvements or behavioral interventions, with little integration of psychosocial and contextual analyses to develop evidence-based strategies for increasing community participation in the sustainable management of MSW. To address this gap, we conducted a study combining quantitative data from surveys with qualitative and geospatial data obtained through social mapping sessions and information obtained from local waste collectors in five municipalities in Norte de Santander, Colombia—a region marked by high socioeconomic vulnerability. Our study presents a novel integration of psychosocial and geospatial data to inform MSW interventions in low-resource settings. We identified that the awareness of the consequences of poor MSW management, the awareness of environmental benefits of delivery and the subjective norm predicts the willingness to separate and deliver MSW. Nonetheless, various psychosocial and contextual barriers hinder these actions. Based on these insights, we propose a low-cost, community-tailored intervention to enhance the separation and delivery of difficult-to-manage MSW and foster civic engagement in similar socio-environmental contexts. Full article

Potentially toxic elements such as cadmium (Cd) in agricultural soils represent a global concern due to their toxicity and potential accumulation in the food chain. However, our understanding of cadmium’s complex sources and the mechanisms controlling its spatial distribution across diverse edaphic and geological contexts remains limited, particularly in underexplored agricultural regions. Our study aimed to assess the total accumulated Cd content in soils under avocado cultivation and its association with edaphic, geochemical, and geomorphological variables. To this end, we considered the total concentrations of other metals and explored their associations to gain a better understanding of Cd’s spatial distribution. We analyzed 26 physicochemical properties, the total concentrations of 22 elements (including heavy and trace metals such as As, Ba, Cr, Cu, Hg, Ni, Pb, Sb, Se, Sr, Tl, V, and Zn and major elements such as Al, Ca, Fe, K, Mg, and Na), and six geospatial variables in 410 soil samples collected from various avocado-growing regions in Peru in order to identity potential associations that could help explain the spatial patterns of Cd. For data analysis, we applied (1) univariate statistics (skewness, kurtosis); (2) multivariate methods such as Spearman correlations and principal component analysis (PCA); (3) spatial modeling using the Geodetector tool; and (4) non-parametric testing (Kruskal–Wallis test with Dunn’s post hoc test). Our results indicated (1) the presence of hotspots with Cd concentrations exceeding 3 mg·kg⁻¹, displaying a leptokurtic distribution (skewness = 7.3); (2) dominant accumulation mechanisms involving co-adsorption and cation competition (Na⁺, Ca²⁺), as well as geogenic co-accumulation with Zn and Pb; and (3) significantly higher Cd concentrations in Leptosols derived from Cretaceous intermediate igneous rocks (diorites/tonalites), averaging 1.33 mg kg⁻¹ compared to 0.20 mg·kg⁻¹ in alluvial soils (p < 0.0001). The factors with the greatest explanatory power (q > 15%, Geodetector) were the Zn content, parent material, geological age, and soil taxonomic classification. These findings provide edaphogenetic insights that can inform soil cadmium (Cd) management strategies, including recommendations to avoid establishing new plantations in areas with a high risk of Cd accumulation. Such approaches can enhance the efficiency of mitigation programs and reduce the risks to export markets. Full article

Healthcare accessibility is among the most critical challenges affecting millions, reflecting profound geospatial disparities in Latin America. This study aims to evaluate healthcare service geospatial accessibility patterns, comparing the geospatial coverage between public and private healthcare facilities in Santiago district, Panama. We first apply the Two-Step Floating Catchment Area (2SFCA) method and its extended variant (E2SFCA) to calculate geospatial accessibility indexes at public and private healthcare facilities. We then use Getis–Ord Gi* and Local Moran geospatial statistical analysis to identify significant clusters of high and low accessibility. The results reveal that public healthcare facilities still offer higher geospatial coverage than private healthcare facilities, with higher geospatial accessibility in the central zone and lower geospatial accessibility in the south zone of Santiago. These findings highlighted the location of new healthcare facilities in zones with lower geospatial accessibility coverage. This study provides reproducible methodological tools for other geographical contexts. It also contributes to improving decision-making and formulating public policies to reduce spatial disparities in healthcare services in Panama and other Caribbean and Latin American countries. Full article

Semantic segmentation of remote sensing images is a fundamental task in geospatial analysis and Earth observation research, and has a wide range of applications in urban planning, land cover classification, and ecological monitoring. In complex geographic scenes, low target-background discriminability in overhead views (e.g., indistinct boundaries, ambiguous textures, and low contrast) significantly complicates local–global information modeling and results in blurred boundaries and classification errors in model predictions. To address this issue, in this paper, we proposed a novel Multi-Scale Local–Global Mamba Feature Pyramid Network (MLMFPN) through designing a local–global information synergy modeling strategy, and guided and enhanced the cross-scale contextual information interaction in the feature fusion process to obtain quality semantic features to be used as cues for precise semantic reasoning. The proposed MLMFPN comprises two core components: Local–Global Align Mamba Fusion (LGAMF) and Context-Aware Cross-attention Interaction Module (CCIM). Specifically, LGAMF designs a local-enhanced global information modeling through asymmetric convolution for synergistic modeling of the receptive fields in vertical and horizontal directions, and further introduces the Vision Mamba structure to facilitate local–global information fusion. CCIM introduces positional encoding and cross-attention mechanisms to enrich the global-spatial semantics representation during multi-scale context information interaction, thereby achieving refined segmentation. The proposed methods are evaluated on the ISPRS Potsdam and Vaihingen datasets and the outperformance in the results verifies the effectiveness of the proposed method. Full article

The United Nations (UN) framework defines indicator 13.1.1 as the number of deaths, missing persons, and directly affected individuals due to disasters per 100,000 population. This indicator is associated with target 13.1, which calls for urgent actions against climate-related hazards and natural disasters in all countries. However, there is a lack of official data providers and well-established methodologies for assessing the resilience of populated areas to natural disasters. Earth observation (EO), geospatial technologies, and local data may support the estimation of this indicator and, as such, enhance the resilience of specific communities against hazards. Thus, the present study aims to enhance the capacity to monitor Sustainable Development Goals (SDGs) using the abovementioned technologies. In this context, a methodology that integrates ecoregion-specific model training and flood potential related geospatial datasets has been developed to estimate the number of houses affected by floods. This methodology relies on disaster-related databases, such as the UN’s DesInventar, and flood- and exposure-related data, including precipitation and soil moisture products combined with hydro-modelling based on digital elevation models, infrastructure datasets, and population products. By integrating these data sources, different machine learning regression models were trained and stratified by ecoregions to predict the number of affected houses and, as such, provide a more comprehensive understanding of community resilience to floods in the Sahel region. This effort is particularly crucial as the frequency and intensity of floods significantly increase in many areas due to climate change. Full article

The world population affected by seismic risk is increasing due to urban sprawl, especially in vulnerable areas of countries with high population growth. Despite this trend, seismic exposure assessments have predominantly focused on cities in high-income countries, leaving a knowledge gap in data-scarce, seismically active urban areas. This research presents a novel, scalable geospatial methodology for seismic exposure assessment in contexts with limited data availability and its application to San José, Costa Rica, evaluating its time and cost efficiency. The methodology prioritizes the use of free and open-access geospatial data to construct city-scale Geospatial Exposure Databases (city-GEDs) at the individual building level. These databases integrate key attributes from the Global Earthquake Model (GEM) taxonomy, including the building footprint, the plan regularity, the construction date, the roof material, the relative position within the urban block, and urban block compactness. Random Forest classification models were developed to assign buildings to expert-defined building typologies (BTs). In the case of San José, 7226 buildings were classified into eight typologies using the derived attributes, achieving a classification error of 46%. When the building height—visually sampled—was included, the error decreased significantly to 13%, confirming its importance in typology prediction and emphasizing the need for efficient acquisition strategies. This approach is essential for quick pre- or post-disaster seismic risk assessment, allowing time and cost-effective data collection and analysis. This contribution is particularly relevant for Central America and other seismically active regions with limited data, supporting improved risk analysis and urban resilience planning. Full article

The expansion of the aquaculture sector offers important economic opportunities but also presents significant challenges, particularly in disease management and prevention. Geographic Information Systems (GISs) have become essential tools for supporting aquatic animal health activities. However, despite their benefits, GISs are still underutilized, particularly in developing countries. To promote the adoption of GISs among aquaculture professionals, a specialized GIS course was developed to improve the prowess of users, equipping them with geospatial analysis skills aimed at epidemiological surveillance and disease response in aquaculture. This study describes a GIS capacity-building initiative developed under the Aquae Strength project. The training approach focuses on the context-specific use of geospatial data and practical applications, and provides a learning environment that fosters autonomy through hands-on, problem-based learning. The program utilizes the open-source QGIS software version 3.28 and incorporates customized materials and exercises based on real-world aquaculture scenarios. The authors hypothesized that the course, due to its cost-effectiveness and use of open-source software, would be particularly beneficial in low- and middle-income settings. The methodological framework described is explicitly designed for easy replication, allowing aquaculture professionals worldwide to download all the course materials and implement similar GIS capacity-building initiatives. The project was funded by the Italian Ministry of Health and supported by the World Organisation for Animal Health (WOAH). It runs from February 2022 to February 2025, with a one-year extension. Full article

This study explores how urban morphological and environmental factors influence Urban Heat Islands (UHIs) using a geospatial modeling approach. The aim of the research is to develop a methodology to assess UHI effects, emphasizing the role of urban morphology, land use, and vegetation in nighttime heat accumulation. A micro-scale analysis with a 50 m resolution is conducted by integrating a custom QGIS plugin with open-access data, ensuring broad applicability. The 50 m resolution was chosen because it allows for the capture of local variations in UHI intensity while maintaining the scalability of the urban analysis across different city contexts. Non-parametric statistical analyses (ANOVA, Kruskal–Wallis H test, and correlation assessments) were used to evaluate the relationships between the urban parameters—wind corridors, altitude, vegetation (NDVI), surface water (NDWI), and the Sky View Factor (SVF)—and Nighttime Land Surface Temperature (LST). Given that UHI variations during summer, particularly in cities of the Iberian Peninsula, are closely linked to summer heat severity, this factor was considered to classify the cities for the study. Correlation analyses confirm that all tested factors influence LST, with wind corridors being the least significant. The model performance evaluation shows the highest errors in cities with lower summer severity (RMSE = 1.586 °C, MAE = 1.2686 °C, MAPE = 6.99%) and the best performance in warmer cities (RMSE = 1.4 °C, MAE = 1.14 °C, MAPE = 4.5%). Validation in four cities of the Iberian Peninsula confirmed the model’s reliability, with the worst RMSE value of 2.04 °C. These findings contribute to a better understanding of the factors driving UHIs and provide a scalable assessment framework. Full article

Digital tools, especially games, are increasingly important for enabling citizen participation in urban planning. Among these, Minecraft has been widely utilized to engage children, leveraging its virtual environment to represent geospatial data. However, systematic methods for collecting and analyzing player-generated data within Minecraft remain underexplored. Playful Participatory Planning System (P-PPS) framework that transforms player actions (e.g., building, removing, planting) within Minecraft, using OpenStreetMap (OSM) data to create game environments, back into geospatial data for analysis. The framework’s applicability was demonstrated through two case studies, one with 58 schoolchildren and 18 adults in Ireland. The results reveal that schoolchildren, while highly engaged, demonstrated a high density of actions within limited areas, suggesting a need for guidance on spatial distribution and ecological considerations. In contrast, adults prioritized the urban context and exhibited greater spatial consistency in their actions. Challenges emerged in managing online interactions, emphasizing the need for clear guidelines and moderation strategies. This research demonstrates the potential of Minecraft as a platform for participatory urban planning, exploring its use as a collaborative immersive mapping tool. Full article

Monitoring built-up dynamics is essential for sustainable urban and territorial planning. This study presents an innovative geospatial methodology integrating multi-temporal satellite data fusion, transfer learning, machine learning classification, and open-access cloud computing to systematically identify, quantify, and map the spatiotemporal evolution of built-up areas. The methodology was applied at a territorial scale in southern Italy using Landsat multispectral imagery acquired and elaborated through Google Earth Engine. Compared to more conventional classification methods, the proposed integrated approach ensures scalability, reproducibility, and computational efficiency. Landsat multispectral imagery from 2006 to 2024 was classified using a Random Forest (RF) algorithm, trained and validated with CORINE Land Cover maps for 2006, 2012, and 2018. For 2024, a transfer learning strategy was adopted, enabling classification through a model fine-tuned with historical data and validated independently. Accuracy assessment returned an Overall Accuracy (OA) of 0.890 and F1-scores between 0.803 and 0.811 for 2006–2018. For 2024, the OA reached 0.926 with an F1-score of 0.926, confirming the effectiveness of the proposed framework. This integrated methodology not only allows for determining the extent of urban expansion over the considered timelines, but, by introducing two spatial metrics, Urban Density and the Urban Dispersion Index (UDI), also enables the characterization of the morphological evolution of urban growth. The methodology ensures spatial and temporal consistency, offering a scalable and automated framework for long-term monitoring that provides a decision support tool for urban growth management and environmental planning, especially in data-limited contexts. Full article

This review synthesizes historical and contemporary research on wildlife–vehicle collisions and roadkill, outlining its evolution from early documentation to modern road ecology. It discusses how early efforts in North America and Europe that quantified animal casualties and developed standardized methodologies formed current studies that use advanced geospatial tools, citizen science, and artificial intelligence to analyze spatiotemporal patterns. We examine key ecological, methodological, and economic impacts of roadkill on wildlife populations and human safety, highlighting the role of road density, vehicle speed, and seasonal factors. The framework presented also underscores a commitment to sustainability by integrating environmental conservation with infrastructural development and socio-economic resilience. The review details various mitigation strategies, from fencing and wildlife crossings to dynamic signage, and evaluates their effectiveness in reducing mortality rates, thereby supporting sustainable development in transportation infrastructure and wildlife management. It also identifies research gaps and outlines future directions, advocating for integrated, multidisciplinary approaches to improve wildlife conservation, infrastructure planning, and public awareness in the context of rapidly expanding road networks. Full article