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Abstract Documenting tangible cultural heritage using 3D modeling techniques is gradually becoming an indispensable component of archaeological practice. The 3D modeling techniques based on photogrammetry and LiDAR scanning enable high-accuracy and high-realistic reconstruction of archaeological sites, and have been proven a powerful tool for documenting archaeological excavations. However, dynamically documenting an ongoing excavation using these techniques is still considered tedious, time-consuming, expensive, and dependent on expertise. Moreover, the application of 3D modeling techniques in archaeological excavations still faces some technical challenges, such as modeling with multi-source and multi-scale data, fusing local models at different times into a whole, achieving fast modeling while GPU workstations are not available in the field, and evaluating the quality of 3D models. As a result, there are still very few archaeological teams deeply engaged in dynamic documentation with 3D modeling techniques, and traditional drawing sketches and taking photographs still dominate. In these senses, documenting the archaeological excavation at the Yunxian Man site (located in Hubei, China) is an invaluable opportunity for exploration and practice. Archaeologists determined to conduct dynamically documenting at the beginning of the 6th excavation project for the site, and established a rotation system to reconcile physical excavation with digital preservation. Through repeated practice and communication, we proposed a workflow and pursued several new methods to enhance the feasibility of dynamically documenting, and obtained 4D models of the ongoing archaeological excavations. In 2022, the Yunxian Man site unearthed the most intact fossil of hominin cranium from about one million years ago in the Eurasian continent, preserving important and scarce anatomical features of early humans in Asia. As the original taphonomic context of the fossil corroded away during physical excavations, the digital documentation consisting of 4D models serves as permanent original data source in subsequent archaeological research. Moreover, we obtained cross-scale 3D models from geographical environment to archaeological site, excavation area, and cultural remains, and all of these 3D models are in an actual, unified coordinate framework. Thus, we can contribute to multidisciplinary cross-collaborative research through data sharing. Considering that digital documentations serve a great value in archaeological research, this paper focuses on sharing the workflow and methods to facilitate digital preservation for more archaeological projects.

Documenting tangible cultural heritage using 3D modeling techniques is gradually becoming an indispensable component of archaeological practice. The 3D modeling techniques based on photogrammetry and LiDAR scanning enable high-accuracy and high-realistic reconstruction of archaeological sites, and have been proven a powerful tool for documenting archaeological excavations. However, dynamically documenting an ongoing excavation using these techniques is still considered tedious, time-consuming, expensive, and dependent on expertise. Moreover, the application of 3D modeling techniques in archaeological excavations still faces some technical challenges, such as modeling with multi-source and multi-scale data, fusing local models at different times into a whole, achieving fast modeling while GPU workstations are not available in the field, and evaluating the quality of 3D models. As a result, there are still very few archaeological teams deeply engaged in dynamic documentation with 3D modeling techniques, and traditional drawing sketches and taking photographs still dominate. In these senses, documenting the archaeological excavation at the Yunxian Man site (located in Hubei, China) is an invaluable opportunity for exploration and practice. Archaeologists determined to conduct dynamically documenting at the beginning of the 6th excavation project for the site, and established a rotation system to reconcile physical excavation with digital preservation. Through repeated practice and communication, we proposed a workflow and pursued several new methods to enhance the feasibility of dynamically documenting, and obtained 4D models of the ongoing archaeological excavations. In 2022, the Yunxian Man site unearthed the most intact fossil of hominin cranium from about one million years ago in the Eurasian continent, preserving important and scarce anatomical features of early humans in Asia. As the original taphonomic context of the fossil corroded away during physical excavations, the digital documentation consisting of 4D models serves as permanent original data source in subsequent archaeological research. Moreover, we obtained cross-scale 3D models from geographical environment to archaeological site, excavation area, and cultural remains, and all of these 3D models are in an actual, unified coordinate framework. Thus, we can contribute to multidisciplinary cross-collaborative research through data sharing. Considering that digital documentations serve a great value in archaeological research, this paper focuses on sharing the workflow and methods to facilitate digital preservation for more archaeological projects. [ABSTRACT FROM AUTHOR]

Abstract Scientists, stakeholders and decision makers face trade-offs between adopting simple or complex approaches when modeling ecosystem services (ES). Complex approaches may be time- and data-intensive, making them more challenging to implement and difficult to scale, but can produce more accurate and locally specific results. In contrast, simple approaches allow for faster assessments but may sacrifice accuracy and credibility. The ARtificial Intelligence for Ecosystem Services (ARIES) modeling platform has endeavored to provide a spectrum of simple to complex ES models that are readily accessible to a broad range of users. In this paper, we describe a series of five "Tier 1" ES models that users can run anywhere in the world with no user input, while offering the option to easily customize models with context-specific data and parameters. This approach enables rapid ES quantification, as models are automatically adapted to the application context. We provide examples of customized ES assessments at three locations on different continents and demonstrate the use of ARIES' spatial multi-criteria analysis module, which enables spatial prioritization of ES for different beneficiary groups. The models described here use publicly available global- and continental-scale data as defaults. Advanced users can modify data input requirements, model parameters or entire model structures to capitalize on high-resolution data and context-specific model formulations. Data and methods contributed by the research community become part of a growing knowledge base, enabling faster and better ES assessment for users worldwide. By engaging with the ES modeling community to further develop and customize these models based on user needs, spatiotemporal contexts, and scale(s) of analysis, we aim to cover the full arc from simple to complex assessments, minimizing the additional cost to the user when increased complexity and accuracy are needed. Graphical abstract Unlabelled Image Highlights • Ecosystem service (ES) modeling is typically time consuming. • Limited data and model reuse hinder new applications and progress in the field. • We demonstrate 5 cloud-based ARIES models that can run on global or customized data. • Models produce consistent outputs, including ES supply, demand and surplus/deficit. • Community-level data and model sharing can advance progress in ES modeling. [ABSTRACT FROM AUTHOR]

Computer modeling and simulation can be very demanding in terms of computational resources. Cloud computing has opened up new avenues for the scientific researchers with limited resources to do complicated simulation. In order to investigate whether cloud computing is suitable for modeling and simulation, we first describe how to build a cloud-based modeling and simulation platform using GNU Octave and MatlabMPI on the Amazon Elastic Compute Cloud (EC2). Then, we evaluation its performance taking the geophysical inversion modeling as an example. The results show that the cloudbased modeling and simulation platform is suitable for basic modeling for free. It can provide much more higher performance with acceptable price. Furthermore, we can cut down the cost by employing the Spot instance without losing the computation performance. [ABSTRACT FROM AUTHOR]