Search Results (45)

Territorial tourism has brought new development opportunities for historic and cultural neighborhoods. However, an insufficient understanding of the spatial distribution and influencing mechanisms of neighborhood vitality continues to constrain effective revitalization strategies. This study takes the Shuitingmen Historical and Cultural Neighborhood in Quzhou, China, as a case study and develops a multi-dimensional vitality evaluation framework incorporating point-of-interest (POI) data, location-based service (LBS) heatmaps, street network data, historical resources, and environmental perception indicators. The Analytic Hierarchy Process (AHP) is applied to assign indicator weights and calculate composite vitality scores across 19 streets. The results reveal that (1) comprehensive evaluation corrects the bias of single indicators and highlights the value of integrated assessment; (2) vitality is higher on rest days than on weekdays, with clear temporal patterns and two types of daily fluctuation trends—similar and differential; and (3) vitality levels are spatially uneven, with higher vitality in central and western areas and lower performance in the southeast, often related to low accessibility and functional monotony. This study confirms a strong positive correlation between street vitality and objective spatial factors, offering strategic insights for the micro-scale renewal and sustainable revitalization of historic neighborhoods. Full article

Analyses of thermokarst hazard risk are becoming increasingly crucial in the context of global warming. A significant aspect of thermokarst research is the mapping of landscapes based on their vulnerability to thermokarst processes. The exponential growth of remote sensing data and the advent of novel techniques have paved the way for the creation of sophisticated techniques for the study of natural disasters, including thermokarst phenomena. This study applies machine learning techniques to assess the vulnerability of tundra landscapes to thermokarst by integrating supervised classification using random forest with morphometric analysis based on the Topography Position Index. We recognized that the thermokarst landscape with the greatest potential for future permafrost thawing occupies 20% of the study region. The thermokarst-affected terrains and water bodies located in the undegraded uplands account for 13% of the total area, while those in depressions and valleys account for 44%. A small part (6%) of the study region represents areas with stable terrains within depressions and valleys that underwent topographic alterations and are likely to maintain stability in the future. This approach enables big geodata-driven predictive modeling of permafrost hazards, improving thermokarst risk assessment. It highlights machine learning and Google Earth Engine’s potential for forecasting landscape transformations in vulnerable Arctic regions. Full article

Assessing vines’ vigour is essential for vineyard management and automatization of viticulture machines, including shaking adjustments of berry harvesters during grape harvest or leaf pruning applications. To address these problems, based on a standardized growth class assessment, labeled ground truth data of precisely located grapevines were predicted with specifically selected Machine Learning (ML) classifiers (Random Forest Classifier (RFC), Support Vector Machines (SVM)), utilizing multispectral UAV (Unmanned Aerial Vehicle) sensor data. The input features for ML model training comprise spectral, structural, and texture feature types generated from multispectral orthomosaics (spectral features), Digital Terrain and Surface Models (DTM/DSM- structural features), and Gray-Level Co-occurrence Matrix (GLCM) calculations (texture features). The specific features were selected based on extensive literature research, including especially the fields of precision agri- and viticulture. To integrate only vine canopy-exclusive features into ML classifications, different feature types were extracted and spatially aggregated (zonal statistics), based on a combined pixel- and object-based image-segmentation-technique-created vine row mask around each single grapevine position. The extracted canopy features were progressively grouped into seven input feature groups for model training. Model overall performance metrics were optimized with grid search-based hyperparameter tuning and repeated-k-fold-cross-validation. Finally, ML-based growth class prediction results were extensively discussed and evaluated for overall (accuracy, f1-weighted) and growth class specific- classification metrics (accuracy, user- and producer accuracy). Full article

This study aims to identify the added value of smart city technologies in real estate development, one of the most significant factors that would transform traditional real estate into smart ones. In total, 16 technologies utilized at both levels have been investigated. The research followed an integrative review methodology; the review is based on 168 publications. The compiled results based on metadata analysis displayed the state of each technology’s added values and usage in both scales. A total of 131 added values were identified. These added values were categorized based on the real estate life cycle sub-phases and processes. Moreover, the value of the integration between these technologies was revealed. The review and results proved that these technologies are mature enough for practical use; therefore, real estate developers, city management, planners, and experts should focus on implementing them. City management should invest in Big Data and geodata and adopt several technologies based on the aspects required for development. This study can influence stakeholders, enhance their decision-making on which technology would suit their needs, and provide recommendations on who to utilize them. Also, it provides a starting point for stakeholders who aim to establish a road map for incorporating smart technologies in future smart real estate. Full article

Geodata, geographical information science (GISc), and GeoAI (geo-intelligence workflows) play an increasingly important role in predictive disaster risk reduction and management (DRRM), aiding decision-makers in determining where and when to allocate resources. There have been discussions on the ethical pitfalls of these predictive systems in the context of DRRM because of the documented cases of biases in AI systems in other socio-technical systems. However, none of the discussions expound on how to audit geo-intelligence workflows for biases from data collection, processing, and model development. This paper considers a case study that uses AI to characterize housing stock vulnerability to flooding in Karonga district, Malawi. We use Friedman and Nissenbaum’s definition and categorization of biases that emphasize biases as a negative and undesirable outcome. We limit the scope of the audit to biases that affect the visibility of different housing typologies in the workflow. The results show how AI introduces and amplifies these biases against houses of certain materials. Hence, a group within the population in the area living in these houses would potentially miss out on DRRM interventions. Based on this example, we urge the community of researchers and practitioners to normalize the auditing of geo-intelligence workflows to prevent information disasters from biases. Full article

The present article is devoted to the issue of studying the patterns of displacement of superincumbent rock over panels of a mine obtained using advanced seismic technologies, allowing for the study of the boundaries of caving zones in the depths of rock mass. A seismic exploration has been performed in local areas of Zhomart mine responsible for the development of Zhaman-Aybat cuprous sandstone deposits in Central Kazakhstan at the stage of repeated mining with pulling of previously non-mined ore pillars and superincumbent rock caving. A 2D field seismic exploration has been accomplished, totaling to 8000-line m of seismic lines using seismic shot point. The survey depth varied from 455 m to 625 m. The state-of-the-art technologies of kinematic and dynamic analysis of wavefield have been widely used during data processing and interpretation targeted at identifying anomalies associated with the structural heterogeneity of the pays and rock mass, engaging modern algorithms and mathematical apparatuses of specialized geodata processing systems. The above effort resulted in new data regarding the location and morphology of the reflectors, characterizing geological heterogeneity of the section, zones of smooth rock displacement, and displacement of strata with significant disturbance of the rocks overlying mined-out productive pay. The potential of the application of modern 2D seismic exploration to studying an underworked zone with altered physical and mechanical properties located over an ore deposit has been assessed. The novelty and practical significance of the research lies in the determination of the boundaries of zones of displacement and superincumbent rock caving over the panels obtained using state-of-the-art technologies of seismic exploration. The deliverables may be used to improve the process of recognizing specific types of technogenic heterogeneities in the rock mass, impacting the efficiency and safety of subsurface ore mining, both for localization and mining monitoring. Full article

This study investigates the use of participatory geographic information systems (PGIS) for hazard assessment in small island developing states (SIDS), with a focus on spatial literacy and community-based disaster management. By partnering with the Lavongai community on Papua New Guinea, this research aimed to empower community members through skill development in geodata processing. The program leveraged local knowledge and the global positioning system to create participatory maps, enhancing both community capacity and researcher data quality. Workshops and focus group discussions (FGDs) were conducted to assess the community’s understanding of spatial concepts related to disaster risks. The core objective was a preliminary assessment of the community’s social and economic vulnerability to coastal disasters, using household data and GIS analysis. The results showed varied vulnerability levels within the community, highlighting the need for targeted disaster mitigation training and nature-based solutions. High-resolution satellite imagery and a simple bathtub model simulated sea level rise, identifying land-uses at risk. The program concluded with a community presentation of thematic maps, fostering collaboration and transparency. Future projects will address environmental challenges identified by local leaders and prioritize skill development, social data collection, and water resource mapping. Full article

Tombolos are ephemeral coastal landforms, which may form and disappear over short geological time periods. Thus, they are susceptible to marine processes. During the last decades, however, climate change and the subsequent sea level rise seems to have affected a large part of the world’s coastlines. Tombolos are particularly prone to the imminent sea level rise. Many tombolos globally may disappear in the coming decades. Our work aims to quantify the susceptibility of the tombolos along the Greek coastline in relationship to the sea level rise. We mapped all Greek tombolos and created an online (and public) geodata base. For each tombolo, we measured its primary physiographical characteristics (e.g., length and width), and also its height above sea level. Based on that, we applied two scenarios proposed by the IPCC concerning the future sea level rise (RCP 2.6 and RCP 8.5), in order to check to what extent the Greek tombolos may disappear or face extreme erosion in the next few decades. Our results indicate that more than half of the Greek tombolos will be fully flooded and disappear in 100 years even under the optimistic scenario. Even those that remain will still face severe erosion problems. Full article

In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements. Full article

Integrated geo- and environmental monitoring in mining represents a high-dimensional challenge (location, altitude/depth, time and sensors). This is challenging for experts but poses great problems for a multitude of participants and stakeholders in building up a complete process understanding. The Epe research cooperation aims to elucidate the ground movement at the Epe cavern storage facility with a public participation process. The research cooperation was founded by the city of Gronau, the citizens’ initiative cavern field Epe, the company EFTAS, Münster, and the Research Center of Post-Mining at the Technische Hochschule Georg Agricola, Bochum. This research cooperation is the first in Germany to involve direct collaboration between science and the public. In the cavern field, which has been in operation since the 1970s, brine is extracted, and at the same time natural gas, crude oil and helium, as well as hydrogen in the future, are stored in the subsurface. The technical focus of this work was the development of a high-resolution spatiotemporal analysis of ground movements. The area is monitored annually by the mining company’s mine surveyor. The complexity of the monitoring issue lies in the fact that the western part is a bog area and a former bog area. Furthermore, the soils in the eastern part are very humus-rich and show strong fluctuations in the groundwater and therefore complex hydraulic conditions. At the same time, there are few fixed scatterers or prominent points in the area that allow high-resolution spatiotemporal monitoring using simple radar interferometry methods. Therefore, the SBAS method (Small Baseline Subset), which is based on an aerial method, was used to analyze the radar interferometric datasets. Using an SBAS analysis, it was possible to evaluate a time series of 760 scenes over the period from 2015 to 2023. The results were integrated with the mine survey maps on the ground movement and other open geodata on the surface, the soil layers and the overburden. The results show complex forms of ground movement. The main influence is that of mining. Nevertheless, the influence of organic soils with drying out due to drought years and uplift in wet years is great. Thus, in dry years, ground subsidence accelerates, and in wet years, ground subsidence not only slows down but in some cases also causes uplift. This complexity of ground movements and the necessary understanding of the processes involved has been communicated to the interested public at several public information events as part of the research cooperation. In this way, an understanding of the mining process was built up, and transparency was created in the subsurface use, also as a part of the energy transition. In technical terms, the research cooperation also provides a workflow for developing the annual mine survey maps into an integrated geo- and environmental monitoring system with the development of a transparent participatory geomonitoring process to provide resilience management to a mining location. Full article

A vessel automatic identification system (AIS) provides a large amount of dynamic vessel information over a large coverage area and data volume. The AIS data are a typical type of big geo-data with high dimensionality, large noise, heterogeneous densities, and complex distributions. This poses a challenge for the clustering and analysis of vessel sailing routes. This study proposes an efficient vessel sailing route clustering and analysis method based on AIS data that uses traffic density images to transform the clustering problem of complex AIS trajectories into an image processing problem. First, a traffic density image is constructed based on the statistics of the preprocessed AIS data. Next, the main sea route regions of traffic density images are extracted based on local image features, geometric structures, and spatial features. Finally, the sailing trajectories are clustered using the extracted sailing patterns. Based on actual vessel AIS data, multimethod comparisons and performance analysis experiments are conducted to verify the feasibility and effectiveness of the proposed method. These experimental results reveal that the proposed method displays potential for the clustering task of challenging vessel sailing routes. Full article

The role of geodata technologies in humanitarian action is arguably indispensable in determining when, where, and who needs aid before, during, and after a disaster. However, despite the advantages of using geodata technologies in humanitarianism (i.e., fast and efficient aid distribution), several ethical challenges arise, including privacy. The focus has been on individual privacy; however, in this article, we focus on group privacy, a debate that has recently gained attention. We approach privacy through the lens of informational harms that undermine the autonomy of groups and control of knowledge over them. Using demographically identifiable information (DII) as a definition for groups, we first assess how these are derived from geodata types used in humanitarian DRRM. Second, we discuss four informational-harm threat models: (i) biases from missing/underrepresented categories, (ii) the mosaic effect—unintentional sensitive knowledge discovery from combining disparate datasets, (iii) misuse of data (whether it is shared or not); and (iv) cost–benefit analysis (cost of protection vs. risk of misuse). Lastly, borrowing from triage in emergency medicine, we propose a geodata triage process as a possible method for practitioners to identify, prioritize, and mitigate these four group-privacy harms. Full article

Recent studies have demonstrated the potential of machine learning methods for fast and accurate mineral classification based on microscope thin sections. Such methods can be extremely useful to support geoscientists during the phases of operational geology, especially when mineralogical and petrological data are fully integrated with other geological and geophysical information. In order to be effective, these methods require robust machine learning models trained on pre-labeled data. Furthermore, it is mandatory to optimize the hyper-parameters of the machine learning techniques in order to guarantee optimal classification accuracy and reliability. Nowadays, deep learning algorithms are widely applied for image analysis and automatic classification in a large range of Earth disciplines, including mineralogy, petrography, paleontology, well-log analysis, geophysical imaging, and so forth. The main reason for the recognized effectiveness of deep learning algorithms for image analysis is that they are able to quickly learn complex representations of images and patterns within them. Differently from traditional image-processing techniques based on handcrafted features, deep learning models automatically learn and extract features from the data, capturing, in almost real-time, complex relationships and patterns that are difficult to manually define. Many different types of deep learning models can be used for image analysis and classification, including fully connected deep neural networks (FCNNs), convolutional neural networks (CNNs or ConvNet), and residual networks (ResNets). In this paper, we compare some of these techniques and verify their effectiveness on the same dataset of mineralogical thin sections. We show that the different deep learning methods are all effective techniques in recognizing and classifying mineral images directly in the field, with ResNets outperforming the other techniques in terms of accuracy and precision. In addition, we compare the performance of deep learning techniques with different machine learning algorithms, including random forest, naive Bayes, adaptive boosting, support vector machine, and decision tree. Using quantitative performance indexes as well as confusion matrixes, we demonstrate that deep neural networks show generally better classification performances than the other approaches. Furthermore, we briefly discuss how to expand the same workflow to other types of images and geo-data, showing how this deep learning approach can be generalized to a multiscale/multipurpose methodology addressed to the analysis and automatic classification of multidisciplinary information. This article has tutorial purposes, too. For that reason, we will explain, with a didactical level of detail, all the key steps of the workflow. Full article

The availability of geodata with high spatial and temporal resolution is increasing steadily. Often, these data are continuously generated by distributed sensor networks and provided as geodata streams. Geostatistical analysis methods, such as spatiotemporal autocorrelation, have thus far been applied primarily to historized data. As such, the advantages of continuous and up-to-date acquisition of geodata have not yet been transferred to the analysis phase. At the same time, open-source frameworks for distributed stream processing have been developed into powerful real-time data processing tools. In this paper a methodology is developed to apply analyses of spatiotemporal autocorrelation directly to geodata streams through a distributed streaming process using open-source software frameworks. For this purpose, we adapt the extended Moran’s I index for continuous and up-to-date computation, then apply it to simulated geospatial data streams of recorded taxi trip data. Various application scenarios for the developed methodology are tested and compared on a distributed computing cluster. The results show that the developed methodology can provide geostatistical analysis results in real time. This research demonstrates how modern datastream processing technologies have the potential to significantly advance the way geostatistical analysis can be performed and used in the future. Full article

Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration). Full article