Resultados de investigación

Datos de desprendimientos de rocas en España desde 1800 a 2021 extraidos de la prensa nacional y local. Incluye daños materiales y humanos, así como la fuente de datos., Data on rockfalls in Spain from 1800 to 2021 extracted from the national and local press. It includes material and human damages, as well as the data source.

Data from the archaeological intervention as a result of the rehabilitation and adaptation works to the Roman quarry Medol (Tarragona) carried out from April and December 2013. The data are from Roman times (2nd century BC - 2nd century AD) and include archaeological inventory, photographs, plans and drawings.,
Dades de la intervenció arqueològica arran de les obres de rehabilitació i adequació a la Pedrera romana del Mèdol (Tarragona) duta a terme entre abril i desembre de 2013. Les dades són d’època romana (S. II aC-II dC) i inclouen l’inventari arqueològic, fotografies, planimetries i làmines.

JavaScript code to be implemented in Google Earth Engine(c) for large-scale, multi-temporal remote sensing of palaeo-river networks.

This research presents a seasonal multi-temporal approach to the detection of palaeo-rivers over large areas based on long-term vegetation dynamics and spectral decomposition techniques. Twenty-eight years of Landsat 5 data, a total of 1711 multi-spectral images, have been bulk processed using Google Earth Engine© Code Editor and cloud computing infrastructure.

JavaScript code to be implemented in Google Earth Engine(c) for automated machine learning-based potsherd detection using high-resolution drone imagery.

This research presents the first proof of concept for the automated recording of material culture dispersion across large areas using high resolution drone imagery, photogrammetry and a combination of machine learning and geospatial analysis that can be run using the Google Earth Engine geospatial cloud computing platform.

JavaScript code to be implemented in Google Earth Engine(c) for Hybrid MSRM-Based Deep Learning and Multitemporal Sentinel 2-Based Machine Learning Algorithm.

Algorithm for large-scale automatic detection of burial mounds, one of the most common types of archaeological sites globally, using LiDAR and multispectral satellite data. Although previous attempts were able to detect a good proportion of the known mounds in a given area, they still presented high numbers of false positives and low precision values. Our proposed approach combines random forest for soil classification using multitemporal multispectral Sentinel-2 data and a deep learning model using YOLOv3 on LiDAR data previously pre-processed using a multi–scale relief model. The resulting algorithm significantly improves previous attempts with a detection rate of 89.5%, an average precision of 66.75%, a recall value of 0.64 and a precision of 0.97, which allowed, with a small set of training data, the detection of 10,527 burial mounds over an area of near 30,000 km2, the largest in which such an approach has ever been applied. The open code and platforms employed to develop the algorithm allow this method to be applied anywhere LiDAR data or high-resolution digital terrain models are available.

JavaScript code to be implemented in Google Earth Engine(c).

The multi-scale relief model (MSRM) is a new algorithm for the visual interpretation of landforms using DSMs. The significance of this new method lies in its capacity to extract landform morphology from both high- and low-resolution DSMs independently of the shape or scale of the landform under study. This method thus provides important advantages compared to previous approaches as it: (1) allows the use of worldwide medium resolution models, such as SRTM, ASTER GDEM, ALOS, and TanDEM-X; (2) offers an alternative to traditional photograph interpretation that does not rely on the quality of the imagery employed nor on the environmental conditions and time of its acquisition; and (3) can be easily implemented for large areas using traditional GIS/RS software.

The algorithm is tested in the Sutlej-Yamuna interfluve, which is a very large low-relief alluvial plain in northwest India where 10 000 km of palaeoriver channels have been mapped using MSRM.

These folders store the results of the automated extraction of individual potsherd fragments in the soil surface provided by two different approaches. They both used the same orthophotomsaic. The original images were acquired by a DJI Phantom 4 Pro V2.0 flying at a height of around 3m above ground. These were acquired as part of the survey of the city of Abdera and its environments. These images correspond to plot 591. These are preliminary results of work in progress. Future research will provide more developed algorithms and processes. This is only intended to document the provisional results as they were in early summer 2020.

Raw data from a study on the goals and the satisfaction of basic psychological needs regarding the physical activities (PA) practiced, the flow experienced and the perceived life satisfaction. It includes a sample of adults consisting of 953 men and 947 women aged between 18 and 28, who practiced PA at least weekly (N = 1900), who fulfilled Spanish language versions of the Goal Contents in Exercise Questionnaire, the Basic Psychological Need Satisfaction Scale, the Short Flow Scale and the Satisfaction with Life Scale.

Raw data for analysing the relationships between procrastination and time perspectives, testing the moderating effect of BPNS between time perspectives and inter-subject procrastination variations. It includes a sample of 1,188 undergraduate students, aged 17-50 years (M = 20.02, SD = 2.63), who completed The Spanish version of the original 56-item Zimbardo Time Perspective Inventory, the 15-item Spanish version of the Échelle de Satisfaction des Besoins Psychologiques, the conjoint Spanish language adaptations of the General Procrastination Scale, the Decisional Procrastination Questionnaire, the Adult Inventory of Procrastination, ss well as the Pure Procrastination Scale.

"WeAreHere!" Children's questionnaire. This dataset includes: (1) the WaH children's questionnaire (20 questions including 5-point Likert scale questions, dichotomous questions and an open space for comments). The Catalan version (original), and the Spanish and English versions of the questionnaire can be found in this dataset in pdf format. (2) The data frame in xlsx format, with the children's answers to the questionnaire (a total of 3664 answers) and a reduced version of it for doing the regression (with the 5-point likert scale variable "ask for help" transformed into a dichotomous variable). (3) The data frame in xlsx format, with the children's answers to the questionnaire and the categorization of their comments (sheet 1), the data frame with only the MCA variables selected (sheet 2), and the categories and subcategories table (sheet 3). (4) The data analysis procedure for the regression, the component and multiple component analysis (R script).