Multi-source Data Integration Research Articles

Multi-Scale Flood Mapping under Climate Change Scenarios in Hexagonal Discrete Global Grids

Among the most prevalent natural hazards, flooding has been threatening human lives and properties. Robust flood simulation is required for effective response and prevention. Machine learning is widely used in flood modeling due to its high performance and scalability. Nonetheless, data pre-processing of heterogeneous sources can be cumbersome, and traditional data processing and modeling have been limited to a single resolution. This study employed an Icosahedral Snyder Equal Area Aperture 3 Hexagonal Discrete Global Grid System (ISEA3H DGGS) as a scalable, standard spatial framework for computation, integration, and analysis of multi-source geospatial data. We managed to incorporate external machine learning algorithms with a DGGS-based data framework, and project future flood risks under multiple climate change scenarios for southern New Brunswick, Canada. A total of 32 explanatory factors including topographical, hydrological, geomorphic, meteorological, and anthropogenic were investigated. Results showed that low elevation and proximity to permanent waterbodies were primary factors of flooding events, and rising spring temperatures can increase flood risk. Flooding extent was predicted to occupy 135–203% of the 2019 flood area, one of the most recent major flooding events, by the year 2100. Our results assisted in understanding the potential impact of climate change on flood risk, and indicated the feasibility of DGGS as the standard data fabric for heterogeneous data integration and incorporated in multi-scale data mining.

A GtoG Direct Coding Mapping Method for Multi-Type Global Discrete Grids Based on Space Filling Curves

DGGS (Discrete Global Grid System) has many subdivision models and coding methods. Due to the lack of underlying consistency of different DGGS codes, most of them are converted through longitude–latitude, which greatly reduces the interoperability efficiency of different DGGS data and has become one of the bottlenecks in efficient integration of multi-source DGGS data. Therefore, a direct mapping method from one grid code to another (Grid to Grid, GtoG) for multi-type DGGSs is proposed based on three classical DGGSs (triangular, diamond and hexagonal grids) and two commonly used filling curves (Hilbert curve and Z-curve). The mutual conversion rules of different grids expressing spatial point, line and surface data are constructed. Then, the above method is extended to the spherical icosahedral grid framework, and three different region coding mapping rule tables of the basic inside cells, boundary cells and vertex cells are designed. Finally, the experimental results show that, compared with the longitude–latitude conversion method, the average conversion efficiency of spatial point, line and surface data is increased by 2–4 orders of magnitude. This new method greatly improves the interoperability efficiency and provides a feasible solution for the efficient integration of multi-source DGGS data.

Street‐level traffic flow and context sensing analysis through semantic integration of multisource geospatial data

AbstractSensing urban spaces from multisource geospatial data is vital to understanding the transportation system in the urban context. However, the complexity of urban context and its indirect interaction with traffic flow deepen the difficulty of exploring their relationship. This study proposes a geo‐semantic framework first to generate semantic representations of multi‐hierarchical urban context and street‐level traffic flow, and then investigate their mutual correlation and predictability using a novel semantic matching method. The results demonstrate that each street is associated with its multi‐hierarchical spatial signatures of urban context and street‐level temporal signatures of traffic flow. The correlation between urban context and traffic flow displays higher values after semantic matching than those in multi‐hierarchies. Moreover, we found that utilizing traffic flow to predict urban context results in better accuracy than the reversed prediction. The results of signature analysis and relationship exploration can contribute to a deeper understanding of context‐aware transportation research.

Survival fluctuation is linked to precipitation variation during staging in a migratory shorebird

Understanding how weather conditions affect animal populations is essential to foresee population changes in times of global climate shifts. However, assessing year-round weather impacts on demographic parameters is hampered in migratory animals due to often unknown occurrence in space and time. We addressed this by coupling tracking and weather data to explain extensive variation in apparent survival across 19 years in a northern European population of little ringed plovers (Charadrius dubius). Over 90% (n = 21) of tracked individuals followed migration routes along the Indo-European flyway to south India. Building on capture–recapture histories of nearly 1400 individuals, we found that between-year variation in precipitation during post-breeding staging in northern South Asia explained 47% of variation in apparent adult survival. Overall, the intensity of the monsoon in South Asia explained 31–33% of variability in apparent survival. In contrast, weather conditions in breeding, final non-breeding and pre-breeding quarters appeared less important in this species. The integration of multi-source data seems essential for identifying key regions and periods limiting population growth, for forecasting future changes and targeting conservation efforts.

Digital Measurement of Construction Performance: Data-to-dashboard Strategy

Performance measurement in construction has been a topic of academic and industry inquiry in the UK since the 1990s. Despite the time elapsed, there is little evidence of a consistent industry-wide performance framework that drives decision-making and supports consistent measurement of performance on construction projects. A review of academic advancements and industry practices has been conducted to understand performance measurement in the construction industry, including the metrics assessed, processes for collecting and analysing data, and current limitations. The adoption of digital technologies on construction projects can support timely measurement of performance metrics, allowing for feedback and corrective action to improve performance. However, organisations struggle to connect the top-down measurement value with the bottom-up data capture technologies. The study of an exemplar commercial project was used to inductively develop a data-to-dashboard strategy that supports decision making in construction. The proposed strategy aligns performance metrics, digital tools and processes, and data analysis techniques in a consistent approach to interpret performance-related data and understand key issues. The development and review of the strategy on a live construction project highlights the challenges experienced with multi-source data integration and the translation of information into knowledge that drives decisions and deployment of timely corrective measures. The application of the strategy would ensure a consistent definition of metrics early in the project, and the continuous measurement of leading indicators. Future research will review the proposed strategy on further case study projects and develop an industry-wide multi-level performance measurement framework that uses the proposed strategy to improve performance.

Enabling building digital twin: Ontology-based information management framework for multi-source data integration

The emergence of the digital twin concept can potentially change the way people manage built assets thoroughly. This is because the semantics-based model and linked data approach behind the digital twin, as the successor of classical BIM, provide strong capability in integrating data from fragmented and heterogeneous sources and thus enable better-informed decision-making. Taking buildings as the case, this paper demonstrates the ontology-based Information Management Framework and elaborates on the process to integrate data through a common data model. Specifically, the Foundation Data Model (FDM) representing the operation of buildings and embedded systems is developed and two patterns of integration architecture are compared. To conceptualise all the essential entities and relationships, the building topology ontology and BRICK ontology are reused and merged to serve as a feasible FDM. According to the characteristic of asset management services that digital twins support, two integration architectures are compared, including the data warehouse approach and the mediator approach. A case study is presented to elaborate on the implementation of these two approaches and their applicability. This work sets out the standardised and modularised paradigms for discovering, fetching, and integrating data from disparate sources with different data curation manners.

Vitality Evaluation of Public Spaces in Historical and Cultural Blocks Based on Multi-Source Data, a Case Study of Suzhou Changmen

As an intermediate link in the three-level heritage protection system of City–Block–Relic, the historical and cultural block is the most important part. As a component of a city, the vitality of historical and cultural blocks is related to the quality of urban space and the inheritance of cultural memory, and it is of great significance to urban renewal and sustainable development. However, traditional urban vitality evaluation methods need to deal with a large number of complex data, and it is often challenging to take into account the dual characteristics of space and culture. Traditional data and independent analysis strategy cannot not function well anymore. Therefore, this study proposes a vitality evaluation model based on multi-source data, which can account for the dual characteristics of space and culture, carry out dimension-reduced analysis for complex elements, so as to display the vitality image and indicate the key influencing factors of the block vitality. This maybe of important significance for exploring the internal dynamism of historical and cultural blocks. Firstly, guided by the “space-culture” composition of historical and cultural blocks, a wide variety of multi-level complex influencing factors of the vitality can be summed up to establish a three-layer vitality evaluation model. Secondly, the integration of multi-source data is realized by using the digital platform and giving out vitality pictures of the block. In this process, the Changmen historical and cultural block of Suzhou is taken as the sample for empirical research. Finally, the evaluation model is verified by combining the field investigation and the external representation of vitality. The calculation results are compared with the external manifestation of vitality, demonstrating the critical influencing factors of the vitality of public space in historical and cultural blocks, pointing out the direction for the creation of a residential environment and the promotion of vitality in the blocks.

Bayesian multisource data integration for explainable brain-behavior analysis.

Different data sources can provide complementary information. Moving from a simple approach based on using one data source at a time to a systems approach that integrates multiple data sources provides an opportunity to understand complex brain disorders or cognitive processes. We propose a data fusion method, called Bayesian Multisource Data Integration, to model the interactions among data sources and behavioral variables. The proposed method generates representations from data sources and uses Bayesian network modeling to associate representations with behavioral variables. The generated Bayesian network is transparent and easy to understand. Bayesian inference is used to understand how the perturbation of representation is related to behavioral changes. The proposed method was assessed on the simulated data and data from the Adolescent Brain Cognitive Development study. For the Adolescent Brain Cognitive Development study, we found diffusion tensor imaging and resting-state functional magnetic resonance imaging were synergistic in understanding the fluid intelligence composite and the total score composite in healthy youth (9-11 years of age).

Sub-Block Urban Function Recognition with the Integration of Multi-Source Data.

The recognition of urban functional areas (UFAs) is of great significance for the understanding of urban structures and urban planning. Due to the limitation of data sources, early research was characterized by problems such as singular data, incomplete results, and inadequate consideration of the socioeconomic environment. The development of multi-source big data brings new opportunities for dynamic recognition of UFAs. In this study, a sub-block function recognition framework that integrates multi-feature information from building footprints, point-of-interest (POI) data, and Landsat images is proposed to classify UFAs at the sub-block level using a random forest model. The recognition accuracies of single- and mixed-function areas in the core urban area of Guangzhou, China, obtained by this framework are found to be significantly higher than those of other methods. The overall accuracy (OA) of single-function areas is 82%, which is 8–36% higher than that of other models. The research conclusions show that the introduction of the three-dimensional (3D) features of buildings and finer land cover features can improve the recognition accuracy of UFAs. The proposed method that uses open access data and achieves comprehensive results provides a more practical solution for the recognition of UFAs.

E-LION: Data integration semantic model to enhance predictive analytics in e-Learning

In the last years, Learning Management systems (LMSs) are acquiring great importance in online education, since they offer flexible integration platforms for organising a vast amount of learning resources, as well as for establishing effective communication channels between teachers and learners, at any direction. These online platforms are then attracting an increasing number of users that continuously access, download/upload resources and interact each other during their teaching/learning processes, which is even accelerating by the breakout of COVID-19. In this context, academic institutions are generating large volumes of learning-related data that can be analysed for supporting teachers in lesson, course or faculty degree planning, as well as administrations in university strategic level. However, managing such amount of data, usually coming from multiple heterogeneous sources and with attributes sometimes reflecting semantic inconsistencies, constitutes an emerging challenge, so they require common definition and integration schemes to easily fuse them, with the aim of efficiently feeding machine learning models. In this regard, semantic web technologies arise as a useful framework for the semantic integration of multi-source e-learning data, allowing the consolidation, linkage and advanced querying in a systematic way. With this motivation, the e-LION (e-Learning Integration ONtology) semantic model is proposed for the first time in this work to operate as data consolidation approach of different e-learning knowledge-bases, hence leading to enrich on-top analysis. For demonstration purposes, the proposed ontological model is populated with real-world private and public data sources from different LMSs referring university courses of the Software Engineering degree of the University of Malaga (Spain) and the Open University Learning. In this regard, a set of four case studies are worked for validation, which comprise advance semantic querying of data for feeding predictive modelling and time-series forecasting of students’ interactions according to their final grades, as well as the generation of SWRL reasoning rules for student’s behaviour classification. The results are promising and lead to the possible use of e-LION as ontological mediator scheme for the integration of new future semantic models in the domain of e-learning.

Multi-source and multi-scale data integration for the assessment of the marine environmental status of the Basque Coast (SE Bay of Biscay)

In the context of the European Marine Strategy Framework Directive (MSFD, 2008/56/EC), Member States are required to assess the environmental status of marine waters. With the aim to support practitioners in this effort, and guide them in the identification of management solutions, a team of researchers has brought together available information for the Basque coastal zone (SE Bay of Biscay), and assessed the marine environmental status of these waters. To undertake the study, the area was divided into Marine Reporting Units (MRUs), following the MSFD terminology, which had specific areas assigned. The information used for the assessment was obtained from a variety of open sources (i.e., in-situ long-term monitoring data, biological data from annual/triannual surveys, multibeam echosounder data, satellite data, and had been collected at different spatial scales, from the coastline to offshore, covering the whole Exclusive Economic Zone. A total of 67 indicators were used, covering some ecosystem components: physico-chemistry of sediments and waters, phytoplankton, benthic fauna and flora, fish, birds, mammals, and alien species. The associated data, for 2010 to 2019, were then collated and integrated to assess the environmental status in a holistic way, using NEAT (Nested Environmental status Assessment Tool). The analyses included spatial scenarios (coastline to offshore), as well as filtering by different MSFD descriptors (i.e., biodiversity, alien species, eutrophication, seafloor integrity and contamination). Overall, the Basque coast is in good status, with no clear spatial gradient. But when focusing on specific ecosystem components (e.g., seabirds, mammals, some commercial fish species), it is possible to identify some components in bad status, with scope for improvement through the implementation of management measures. The integrated assessment approach presented and the outputs that are obtained can be used to guide European practitioners in their efforts to prioritize management measures. Furthermore, results are comparable to those reported in official MSFD assessments, confirming the power of the NEAT software.

Multisource data integration for targeted bus exterior advertising

Bus exterior advertising plays a significant role in outdoor advertising, since it provides frequent exposure to a large number of residents. Traditional route selection methods are generally based on a rough estimation, for example, the number of total passengers of a bus route or the geographical features along the bus route. Targeted bus exterior advertising remains a challenge as little is known about the characteristics of the people along the bus route. In this study, we are aiming at determining a set of bus routes for a given ad category to maximize advertising effectiveness, by mining multiple data sources, including mobile phone data, bus GPS data, smart card data (SCD), and land use data. Specifically, we first estimated the distribution of potential target audiences using mobile phone data and land use data. Two optimization models are proposed considering different advertising requirements. For well-established brands that audiences are familiar with, a wide coverage-oriented bus route selection model is proposed to maximize the coverage of potential target audiences. For new brands that require a high level of exposure before they become recognizable, a deep coverage-oriented bus route selection model is proposed to maximize the total exposure times of the ads. Both models were demonstrated with a case study in Shenzhen, China to explicitly present the outcomes of the models and the differences between them. The calculation results show that the wide coverage-oriented model achieves an average of 84.8% improvement compared with baseline 1 which selects the bus routes with the most passengers, while an average of 9.2% improvement compared with baseline 2 which selects the bus route with the maximum coverage of the target area in reaching more potential target audiences. The exposure intensity of the deep coverage-oriented model is almost 3.7 times of the wide coverage-oriented model. The proposed models provide new options for advertisers to select a suitable advertising strategy according to their needs.

Unsupervised machine learning in urban studies: A systematic review of applications

Unsupervised learning (UL) has a long and successful history in untangling the complexity of cities. As the counterpart of supervised learning, it discovers patterns from intrinsic data structures without crafted labels, which is believed to be the key to real AI-generated decisions. This paper provides a systematic review of the use of UL in urban studies based on 140 publications. Firstly, the topic, technique, application, data type, and evaluation method of each paper are recorded, deriving statistical insights into the evolution and trends. Clustering is the most prominent method, followed by topic modeling. With the strong momentum of deep learning, a growing application field of UL methods is representing the complex real-world urban systems at multiple scales through multi-source data integration. Subsequently, a detailed review discusses how UL is applied in a broad range of urban topics, which are concluded by four dominant themes: urbanization and regional studies, built environment, urban sustainability, and urban dynamics. Finally, the review addresses common limitations regarding data quality, subjective interpretation, and validation difficulty of the results, which increasingly require interdisciplinary knowledge. Research opportunities are found in the rapidly evolving technological landscape of UL and in certain domains where supervised learning dominates.

Identification of Potential Parkinson's Disease Drugs Based on Multi-Source Data Fusion and Convolutional Neural Network.

Parkinson’s disease (PD) is a serious neurodegenerative disease. Most of the current treatment can only alleviate symptoms, but not stop the progress of the disease. Therefore, it is crucial to find medicines to completely cure PD. Finding new indications of existing drugs through drug repositioning can not only reduce risk and cost, but also improve research and development efficiently. A drug repurposing method was proposed to identify potential Parkinson’s disease-related drugs based on multi-source data integration and convolutional neural network. Multi-source data were used to construct similarity networks, and topology information were utilized to characterize drugs and PD-associated proteins. Then, diffusion component analysis method was employed to reduce the feature dimension. Finally, a convolutional neural network model was constructed to identify potential associations between existing drugs and LProts (PD-associated proteins). Based on 10-fold cross-validation, the developed method achieved an accuracy of 91.57%, specificity of 87.24%, sensitivity of 95.27%, Matthews correlation coefficient of 0.8304, area under the receiver operating characteristic curve of 0.9731 and area under the precision–recall curve of 0.9727, respectively. Compared with the state-of-the-art approaches, the current method demonstrates superiority in some aspects, such as sensitivity, accuracy, robustness, etc. In addition, some of the predicted potential PD therapeutics through molecular docking further proved that they can exert their efficacy by acting on the known targets of PD, and may be potential PD therapeutic drugs for further experimental research. It is anticipated that the current method may be considered as a powerful tool for drug repurposing and pathological mechanism studies.

Integrating Multiscale Geospatial Environmental Data into Large Population Health Studies: Challenges and Opportunities.

Quantifying the exposome is key to understanding how the environment impacts human health and disease. However, accurately, and cost-effectively quantifying exposure in large population health studies remains a major challenge. Geospatial technologies offer one mechanism to integrate high-dimensional environmental data into epidemiology studies, but can present several challenges. In June 2021, the National Institute of Environmental Health Sciences (NIEHS) held a workshop bringing together experts in exposure science, geospatial technologies, data science and population health to address the need for integrating multiscale geospatial environmental data into large population health studies. The primary objectives of the workshop were to highlight recent applications of geospatial technologies to examine the relationships between environmental exposures and health outcomes; identify research gaps and discuss future directions for exposure modeling, data integration and data analysis strategies; and facilitate communications and collaborations across geospatial and population health experts. This commentary provides a high-level overview of the scientific topics covered by the workshop and themes that emerged as areas for future work, including reducing measurement errors and uncertainty in exposure estimates, and improving data accessibility, data interoperability, and computational approaches for more effective multiscale and multi-source data integration, along with potential solutions.

History of Land Cover Change on Santa Cruz Island, Galapagos

Islands are particularly vulnerable to the effects of land cover change due to their limited size and remoteness. This study analyzes vegetation cover change in the agricultural area of Santa Cruz (Galapagos Archipelago) between 1961 and 2018. To reconstruct multitemporal land cover change from existing land cover products, a multisource data integration procedure was followed to reduce imprecision and inconsistencies that may result from the comparison of heterogeneous datasets. The conversion of native forests and grasslands into agricultural land was the principal land cover change in the non-protected area. In 1961, about 94% of the non-protected area was still covered by native vegetation, whereas this had decreased to only 7% in 2018. Most of the agricultural expansion took place in the 1960s and 1970s, and it created an anthropogenic landscape where 67% of the area is covered by agricultural land and 26% by invasive species. Early clearance of native vegetation took place in the more accessible—less rugged—areas with deeper-than-average and well-drained soils. The first wave of settlement consisted of large and isolated farmsteads, with 19% of the farms being larger than 100 ha and specializing in diary and meat production. Over the period of 1961–1987, the number of farms doubled from less than 100 to more than 200, while the average farm size decreased from 90 to 60 ha/farmstead. Due to labor constraints in the agricultural sector, these farms opted for less labor-intensive activities such as livestock farming. New farms (popping up in the 1990s and 2000s) are generally small in size, with <5 ha per farmstead, and settled in areas with less favorable biophysical conditions and lower accessibility to markets. From the 1990s onwards, the surge of alternative income opportunities in the tourism and travel-related sector reduced pressure on the natural resources in the non-protected area.

Multisource Data Integration and Comparative Analysis of Machine Learning Models for On-Street Parking Prediction

Searching for a free parking space can lead to traffic congestion, increasing fuel consumption, and greenhouse gas pollution in urban areas. With an efficient parking infrastructure, the cities can reduce carbon emissions caused by additional fuel combustion, waiting time, and traffic congestion while looking for a free parking slot. A potential solution to mitigating parking search is the provision of parking-related data and prediction. Previously many external data sources have been considered in prediction models; however, the underlying impact of contextual data points and prediction has not received due attention. In this work, we integrated parking occupancy, pedestrian, weather, and traffic data to analyze the impact of external factors on on-street parking prediction. A comparative analysis of well-known Machine (ML) Learning and Deep Learning (DL) techniques, including Multilayer Perceptron (MLP), Random Forest (RF), Decision Trees (DT), K-Nearest Neighbors (KNN), Gradient Boosting (GA), Adaptive Boosting (AB), and linear SVC for the prediction of OnStreet parking space availability has been conducted. The results show that RF outperformed other techniques evaluated with an average accuracy of 81% and an AUC of 0.18. The comparative analysis shows that less complex algorithms like RF, DT, and KNN outperform complex algorithms like MLP in terms of prediction accuracy. All four data sources have positively impacted the prediction, and the proposed solution can determine the best possible parking slot based on weather conditions, traffic flow, and pedestrian volume. The experiments on live prediction showed an ingest rate of 0.1 and throughput of 0.3 events per second, demonstrating a fast and reliable prediction approach for available slots within a 5–10 min time frame. The study is scalable for larger time frames and faster predictions that can be implemented for IoT-based big data-driven environments for on-street and off-street parking.

Key Component Capture and Safety Intelligent Analysis of Beam String Structure Based on Digital Twins

In the construction process of beam string structures, the environmental effect and corresponding mechanical properties of the structure are complex. The problem of the misjudgment of structural safety performance caused by the uncertainty of a structural mechanical parameter analysis under various factors needs to be solved. In this study, a method for capturing key components and an intelligent safety analysis of beam string structures based on digital twins (DTs) was proposed. Combined with the characteristics of DTs mapping feedback, a component capture and security analysis framework was formed. Driven by twin framework, multi-source data for structural safety analysis were obtained and the parameter association mechanism established. Considering the space-time evolution and the interaction between the virtual and real elements of the construction process, a multidimensional model was established. Driven by the Dempster–Shafer (D–S) evidence theory, the fusion of structural mechanics parameters was carried out. The safety of the structure was analyzed intelligently by capturing key structural components, thereby providing a basis for the safety maintenance of the structure. The integration of DTs modeling and multi-source data improves the accuracy and intelligence of structural construction safety analysis. In the analysis process, capturing the key components of the structure is the core step. Taking the construction process of a string supported beam roof (symmetrical structure) in a convention and exhibition center as an example, the outlined research method was applied. Based on DTs and D–S evidence theory, the variation degree of mechanical parameters of various components under temperature was determined. By comprehensively investigating the changes of various mechanical parameters, the key components of the structure were captured. Thus, the intelligent analysis of structural safety was realized. The comparison of data verified that the intelligent method can effectively analyze the safety performance of the structure.

Detection and characterization of slow-moving landslides in the 2017 Jiuzhaigou earthquake area by combining satellite SAR observations and airborne Lidar DSM

On 8th August 2017, a catastrophic Ms. 7.0 earthquake with a focal depth of 20 km struck the Jiuzhaigou County in Sichuan Province, China. It exerted a strong influence on the slope stability within the surrounding areas and triggered numerous secondary geohazards including rockfalls and other co-seismic landslides, which incurred drastic surface changes, and thus can be easily identified from cloud-free high-resolution optical imagery. Most of such landslides became stabilized shortly after the earthquake while others moving very slowly for years. In contrast, some slopes were destabilized without significant surface change into slow-moving landslides, which may pose long-term potential threats to people's life and property. Therefore, it is crucial to accurately identify these slow-moving landslides and regularly monitor their post-seismic activity. In this study, we employed the synthetic aperture radar interferometry (InSAR) techniques to detect and monitor slow-moving landslides after the earthquake in the Jiuzhaigou area, and analyzed the impacts of the earthquake on these landslides through integration of multi-source data (InSAR, Lidar, optical image, and field survey). As a result, 16 slow-moving landslides were detected by InSAR in the Jiuzhaigou area, including several historical landslides. The results of time-series InSAR analyses enabled identification of three kinds of landslide evolution modes affected by the earthquake, i.e. acceleration of deformation of pre-existing landslides, reactivation of dormant landslide, and remobilization of earthquake-triggered landslide. Each mode is supported by detailed analyses of multi-source data. The results demonstrated that satellite InSAR combined with high-resolution Lidar and optical data can provide a cost-effective approach of post-earthquake geohazards detection and monitoring.

Integration of geomatics acquisition and multi-source data for visualization and documentation of the finds in the Pauciuri archaeological site (Malvito, Calabria, Italy).

Abstract The surveys of the excavation area of the Pauciuri archaeological site, carried out with geomatic techniques, are described. 3D models of the site and of some particularly interesting remains have been obtained. For an early medieval burial both TLS and photogrammetric surveys were carried out, which allowed the development of two 3D models. The comparison between the two techniques has been made: the results are discussed.