From Archive to GIS: Recovering Spatial Information for Tholos IV at the Palace of Nestor from the Notebooks of Lord William Taylour

Internet Archaeology ◽

10.11141/ia.56.5 ◽

2021 ◽

Author(s):

Michael Loy ◽

Sharon R. Stocker ◽

Jack L. Davis

Keyword(s):

Spatial Information ◽

3D Gis ◽

American School ◽

Gis Model ◽

Classical Studies ◽

Palace Of Nestor ◽

Small Finds ◽

Digital Survey

This article is a case study in doing new things with old data. In 1953 Lord William Taylour directed the excavation of a monumental vaulted tholos tomb known as 'Tholos IV' at the site of ancient Pylos, Messenia, Greece. The excavation was conducted over two months, during which detailed notes were recorded in three notebooks now kept in the Archives of the American School of Classical Studies at Athens. The formal publication of Tholos IV, however, contains only a basic narrative of the excavation, offering neither precise detail on stratigraphy, object find spots, nor even a complete inventory of small finds. The present study goes back to the original notebooks kept by Taylour and combines the data contained in them with a new digital survey of Tholos IV to produce a comprehensive and accurate 3D GIS model for the excavation. Furthermore, the GIS has been produced in such a way that its dataset is compatible with new excavation data currently generated in the ongoing Palace of Nestor Excavations (PONEX) project, bringing together two excavation campaigns conducted under very different circumstances, methodologies, and recording protocols. Discussion follows on how the production of this GIS deepens our understanding not just of the legacy excavation, but also of the site and its wider landscape.

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DEVELOPING A 3-D GIS MODEL FOR URBAN PLANNING. CASE STUDY: AMPANG JAYA, MALAYSIA

PLANNING MALAYSIA JOURNAL ◽

10.21837/pmjournal.v16.i7.501 ◽

2018 ◽

Vol 16 (7) ◽

Author(s):

Mimi Zaleha Abdul Ghani ◽

Yazid Sarkom ◽

Zalina Samadi

Keyword(s):

Decision Making ◽

Urban Planning ◽

Spatial Information ◽

Gis Model ◽

Written Information ◽

Urban Planning And Design ◽

Spatio Temporal ◽

The Rich ◽

Interactive Visualisation

This paper aims to explore the rich potential of interactive visualisation environment integrating GIS for modelling urban growth and spatio-temporal transformation of Malaysian cities. As a case study example, authors consider a 3-D GIS model of Ampang Jaya, Selangor to investigate the techniques of data acquisition, data reconstruction from physical to digital, urban analysis and visualisation in constructing a digital model ranging from low to high geometric content including 2-D digital maps, digital orthographic and full volumetricparametric modelling. The key aspect of this virtual model is how it would assist in understanding the urban planning and design of Ampang Jaya by translating complex spatial information that are currently used by the authorities for planning activities such as maps, plans and written information into responsive, easily understandable spatial information. It could serve as a new platform to disseminate information about Ampang Jaya, bridge gaps among professionals involved in planning processes, improve communications among decision makers, stakeholders and the public as well as support decision making about thespatial growth of Ampang Jaya. Demonstrations of Ampang Jaya will also provide a clearer picture of the importance of ownership and control of 3-D models by local councils in empowering them in decision making, for example, in improving transparency, and avoiding misuse by project developers (Shiffer 1993; Sunesson et al., 2008). Such environment will improve the subsequent digital models and research in the area of urban design and planning in Malaysia where visual communication is pivotal.

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Getting information on suspended sediments in a large river from acoustic backscatter

E3S Web of Conferences ◽

10.1051/e3sconf/20184004017 ◽

2018 ◽

Vol 40 ◽

pp. 04017

Author(s):

Adrien Vergne ◽

Céline Berni ◽

Jérôme Le Coz

Keyword(s):

Suspended Sediment Concentration ◽

Spatial Information ◽

Suspended Sediments ◽

Sediment Concentration ◽

Large River ◽

Acoustic Backscatter ◽

Qualitative Information ◽

Sediment Size ◽

The Common

There has been a growing interest in the last decade in extracting information on Suspended Sediment Concentration (SSC) from acoustic backscatter in rivers. Quantitative techniques are not yet effective, but acoustic backscatter already provides qualitative information on suspended sediments. In particular, in the common case of a bi-modal sediment size distribution, corrected acoustic backscatter can be used to look for sand particles in suspension and provide spatial information on their distribution throughout a river crosssection. This paper presents a case-study where these techniques have been applied.

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Boundary Loss-Based 2.5D Fully Convolutional Neural Networks Approach for Segmentation: A Case Study of the Liver and Tumor on Computed Tomography

Algorithms ◽

10.3390/a14050144 ◽

2021 ◽

Vol 14 (5) ◽

pp. 144

Author(s):

Yuexing Han ◽

Xiaolong Li ◽

Bing Wang ◽

Lu Wang

Keyword(s):

Image Segmentation ◽

Spatial Information ◽

Medical Images ◽

Tumor Segmentation ◽

Convolutional Networks ◽

Learning Framework ◽

Fully Convolutional Networks ◽

Segmentation Accuracy ◽

Boundary Information

Image segmentation plays an important role in the field of image processing, helping to understand images and recognize objects. However, most existing methods are often unable to effectively explore the spatial information in 3D image segmentation, and they neglect the information from the contours and boundaries of the observed objects. In addition, shape boundaries can help to locate the positions of the observed objects, but most of the existing loss functions neglect the information from the boundaries. To overcome these shortcomings, this paper presents a new cascaded 2.5D fully convolutional networks (FCNs) learning framework to segment 3D medical images. A new boundary loss that incorporates distance, area, and boundary information is also proposed for the cascaded FCNs to learning more boundary and contour features from the 3D medical images. Moreover, an effective post-processing method is developed to further improve the segmentation accuracy. We verified the proposed method on LITS and 3DIRCADb datasets that include the liver and tumors. The experimental results show that the performance of the proposed method is better than existing methods with a Dice Per Case score of 74.5% for tumor segmentation, indicating the effectiveness of the proposed method.

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