2014
Authors
Sillero, N; Oliveira, MA; Sousa, P; Sousa, F; Goncalves Seco, L;
Publication
AMPHIBIA-REPTILIA
Abstract
The Societas Europaea Herpetologica (SEH) decided in 2006 through its Mapping Committee to implement the New Atlas of Amphibians and Reptiles of Europe (NA2RE: http://na2re.ismai.pt) as a chorological database system. Initially designed to be a system of distributed databases, NA2RE quickly evolved to a Spatial Data Infrastructure, a system of geographically distributed systems. Each individual system has a national focus and is implemented in an online network, accessible through standard interfaces, thus allowing for interoperable communication and sharing of spatial-temporal data amongst one another. A Web interface facilitates the access of the user to all participating data systems as if it were one single virtual integrated data-source. Upon user request, the Web interface searches all distributed data-sources for the requested data, integrating the answers in an always updated and interactive map. This infrastructure implements methods for fast actualisation of national observation records, as well as for the use of a common taxonomy and systematics. Using this approach, data duplication is avoided, national systems are maintained in their own countries, and national organisations are responsible for their own data curation and management. The database could be built with different representation levels and resolution levels of data, and filtered according to species conservation matters. We present the first prototype of NA2RE, composed of the last data compilation performed by the SEH (Sillero et al., 2014). This system is implemented using only open source software: PostgreSQL database with PostGIS extension, Geoserver, and OpenLayers.
2023
Authors
Monge Soares, R; Nabais, M; Pereiro, TD; Dias, R; Hipólito, J; Fonte, J; Gonçalves Seco, L; Menéndez-Marsh, F; Neves, A;
Publication
Estudos do Quaternário / Quaternary Studies
Abstract
2024
Authors
Sá, R; Gonçalves, LJ; Medina, J; Neves, A; Marsh, F; Al Rawi, M; Canedo, D; Dias, R; Pereiro, T; Hipólito, J; da Silva, AL; Fonte, J; Seco, LG; Vázquez, M; Moreira, J;
Publication
Journal of Computer Applications in Archaeology
Abstract
Geospatial data acquisition methods like airborne LiDAR allow for obtaining large volumes of data, such as aerial and satellite imagery, which are increasingly being used in archaeology. As in other subjects, the ability to produce raw datasets far exceeds the capacity of domain experts to process and analyze them, but recent developments in image processing, Geographic Information Systems (GIS), Machine Learning (ML) and related technologies enable the transformation of large volumes of data into useful information. However, these technologies are challenging to use and not designed to interact with each other. Hence, tools are needed to efficiently manage, share, document, and reuse archaeological data. This article presents the Odyssey SDI platform, a spatial data infrastructure for annotating, validating, and visualizing data about archaeological sites. This platform is built upon GeoNode, and special-purpose modules were developed for dealing with archaeological information. The main contribution is the integration of remote sensing, GIS features and ML algorithms in a single framework. © 2024 The Author(s).
2023
Authors
Soares, RM; Nabais, M; Pereiro, TD; Dias, R; Hipólito, J; Fonte, J; Seco, LG; Menéndez Marsh, F; Neves, A;
Publication
Estudos do Quaternario
Abstract
This study presents a new tridimensional perspective on Castelo Velho de Safara (Moura), one of the great walled settlements of the Iron Age/Roman Republic by the Guadiana River, obtained through a high-resolution survey using a drone integrated with a LiDAR sensor. The outline of the walls was defined in more detail, which meant revising the occupation area, now estimated at circa 1.36 hectares. Several unknown elements were detected, such as the entrance area and other possible defensive structures. The data obtained for the Castelo Velho de Safara demonstrate the potential of LiDAR for understanding the topographical characteristics of this type of fortified enclosure, whose structural remains are not always clear to the naked eye. © 2023, APEQ - Associacao Portuguesa para o Estudo do Quaternario. All rights reserved.
2023
Authors
Menéndez Marsh, F; Al Rawi, M; Fonte, J; Dias, R; Gonçalves, LJ; Seco, LG; Hipólito, J; Machado, JP; Medina, J; Moreira, J; Do Pereiro, T; Vázquez, M; Neves, A;
Publication
Journal of Computer Applications in Archaeology
Abstract
2023
Authors
Canedo, D; Fonte, J; Seco, LG; Vazquez, M; Dias, R; Do Pereiro, T; Hipolito, J; Menendez-Marsh, F; Georgieva, P; Neves, AJR;
Publication
IEEE ACCESS
Abstract
Mapping potential archaeological sites using remote sensing and artificial intelligence can be an efficient tool to assist archaeologists during project planning and fieldwork. This paper explores the use of airborne LiDAR data and data-centric artificial intelligence for identifying potential burial mounds. The challenge of exploring the landscape and mapping new archaeological sites, coupled with the difficulty of identifying them through visual analysis of remote sensing data, results in the recurring issue of insufficient annotations. Additionally, the top-down nature of LiDAR data hinders artificial intelligence in its search, as the morphology of archaeological sites blends with the morphology of natural and artificial shapes, leading to a frequent occurrence of false positives. To address this problem, a novel data-centric artificial intelligence approach is proposed, exploring the available data and tools. The LiDAR data is pre-processed into a dataset of 2D digital elevation images, and the known burial mounds are annotated. This dataset is augmented with a copy-paste object embedding based on Location-Based Ranking. This technique uses the Land-Use and Occupation Charter to segment the regions of interest, where burial mounds can be pasted. YOLOv5 is trained on the resulting dataset to propose new burial mounds. These proposals go through a post-processing step, directly using the 3D data acquired by the LiDAR to verify if its 3D shape is similar to the annotated sites. This approach drastically reduced false positives, attaining a 72.53% positive rate, relevant for the ground-truthing phase where archaeologists visit the coordinates of proposed burial mounds to confirm their existence.
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