Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría

URI permanente para esta comunidadhttps://hdl.handle.net/10953/36

En esta Comunidad se recogen los documentos generados por el Departamento de Ingeniería Cartográfica, Geodésica y Fotogrametría y que cumplen los requisitos de Copyright para su difusión en acceso abierto.

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Mostrando 1 - 7 de 7

A high detail UAS-based 3D model of the Torre Benzalá in Jaén, Spain
(Springer, 2022-12-21) Lerma-Cobo, F.; Romero-Manchado, A.; Enríquez, C.; Ramos, M. I.
The constant development of geomatics tools has driven the opening of their applications to multiple disciplines, including archaeology. The possibility of performing a 3D reconstruction of archaeological remains as well as a semantic classification of the 3D surface facilitates not only a better knowledge of the historical heritage but also an essential aid to the planning and development of restoration and preservation projects of this legacy. Different data exploitation strategies are needed to take advantage of the geospatial data provided by geomatics tools. In this paper, we have studied the current state of conservation of a medieval tower, Torre Benzalá in Jaén, southern Spain. The interesting thing about this study is that very high resolution RGB images, taken by a drone, have been used in order to show the current degree of deterioration of the tower, providing accurate and precise documentation of the current state. Thus, a highly detailed 3D reconstruction of the tower has been carried out. A dense point cloud was generated to obtain a digital elevation model (DEM) to identify and quantify the most critically deteriorated areas. The results are useful for the development of an architectural maintenance and restoration project to preserve this archaeological legacy.
A Machine Learning Model for Early Prediction of Crop Yield, Nested in a Web Application in the Cloud: A Case Study in an Olive Grove in Southern Spain
(MDPI, 2022-08-31) Cubillas, Juan J.; Ramos, María I.; Jurado, Juan M.; Feito, Francisco R.
Predictive systems are a crucial tool in management and decision-making in any productive sector. In the case of agriculture, it is especially interesting to have advance information on the profitability of a farm. In this sense, depending on the time of the year when this information is available, important decisions can be made that affect the economic balance of the farm. The aim of this study is to develop an effective model for predicting crop yields in advance that is accessible and easy to use by the farmer or farm manager from a web-based application. In this case, an olive orchard in the Andalusia region of southern Spain was used. The model was estimated using spatio-temporal training data, such as yield data from eight consecutive years, and more than twenty meteorological parameters data, automatically charged from public web services, belonging to a weather station located near the sample farm. The workflow requires selecting the parameters that influence the crop prediction and discarding those that introduce noise into the model. The main contribution of this research is the early prediction of crop yield with absolute errors better than 20%, which is crucial for making decisions on tillage investments and crop marketing.
Methodology for the study of the traceability of runoff water feeding reservoirs
(IWA PUBLISHING, 2023-08-29) Ortega, Lidia M.; Ramos, M. Isabel; Enríquez Turiño, Carlos; Cubillas Mercado, Juan José
Water reservoirs are essential to ensure water supply to both the population and agriculture, especially in the Mediterranean basin. In some cases, analyses of water intended for human consumption have detected high levels of agrochemicals. Knowing the possible origin of these products is complex because there may be many agricultural plots within the reservoir basin. In this paper, we introduce a methodology to obtain the set of agricultural plots whose rainwater reaches the reservoir and in what proportion they affect the points where chemical analyses are performed. The method implements an extension of the D8 algorithm for the calculation of the drainage network, in which additional information about the land-use type of the area, as well as rainfall maps, are also considered. In order to facilitate the user's analysis of the data, a plugin has been implemented in QGIS. This allows usability and easy interaction with the visual information. The Rumblar reservoir basin, located in Andalusia (Spain) has been studied as a use case, surrounded by olive orchards. The result is a replicable methodology for any other water reservoir and for carrying out an individualized study of agricultural plots.
Prediction of the increase in health services demand based on the analysis of reasons of calls received by a customer relationship management
(Wiley, 2019-03-15) Ramos, Mª Isabel; Cubillas, Juan José; Jurado, Juan Manuel; Lopez, Wilfredo; Feito, Francisco R.; Quero, Manuel; Gonzalez, José María
Currently, customer relationship management (CRM) tools are very important in our society because they provide a comunication channel to the healthcare system for patients. Salud Responde is a CRM that provides many health services for the entire population of Andalusia, in southern Spain. The number and frequenzy of phone calls received change along the year. They depend on many factors, such as weekdays, seasons, vaccination campaigns, environmental factors, pandemic periods, etc. All these are the main reasons number of health calls changes along the year. This variability makes that the current management of resources for offering emergency services based on historical data is inefficient. The factors, which influence the phone calls along the year, are different from one period to another. Therefore, it is clear to demand an improved in the current management system. In this context, the main goal for this research is to develop an expert system able to identify and analyze, using different data mining algorithms, the most relevant factors to predict the variability of health service demand. Thus, here, it is proposed a methodology in which using reasons calls received in the CRM as input data, it is possible to predict in advance the healthcare resources demand.
The Impact of Canopy Reflectance on the 3D Structure of Individual Trees in a Mediterranean Forest
(MDPI, 2020-05-01) Jurado, J; Ramos, M.I; Enríquez, C; Feito, F
The characterization of 3D vegetation structures is an important topic, which has been addressed by recent research in remote sensing. The forest inventory requires the proper extraction of accurate structural and functional features of individual trees. This paper presents a novel methodology to study the impact of the canopy reflectance on the 3D tree structure. A heterogeneous natural environment in a Mediterranean forest, in which various tree species (pine, oak and eucalyptus) coexist, was covered using a high-resolution digital camera and a multispectral sensor. These devices were mounted on an Unmanned Aerial Vehicle (UAV) in order to observe the tree architecture and the spectral reflectance at the same time. The Structure from Motion (SfM) method was applied to model the 3D structures using RGB images from the high-resolution camera. The geometric accuracy of the resulting point cloud was validated by georeferencing the study area through multiple ground control points (GCPs). Then, the point cloud was enriched with the reflected light in four narrow-bands (green, near-infrared, red and red-edge). Furthermore, the Normalized Difference Vegetation Index (NDVI) was calculated in order to measure the tree vigor. A comprehensive analysis based on structural and spectral features of individual trees was proposed. A spatial segmentation was developed to detect single-trees in a forest and for each one to identify the crown and trunk. Consequently, structural parameters were extracted, such as the tree height, the diameter at breast height (DBH) and the crown volume. The validation of these measurements was performed by field data, which were taken using a Total Station (TS). In addition, these characteristics were correlated with the mean reflectance in the tree canopy. Regarding the observed tree species, a statistical analysis was carried out to study the impact of reflectance on the 3D tree structure. By applying our method, a more detailed knowledge of forest dynamics can be gained and the impact of available solar irradiance on single-trees can be analyzed.
The UAS-Based 3D Image Characterization of Mozarabic Church Ruins in Bobastro (Malaga), Spain
(MDPI, 2020-07-24) Enríquez, Carlos; Jurado-Rodríguez, Juan Manuel; Bailey, Alexandro; Callén, Danilo; Collado, María José; Espina, Gabriel; Marroquín, Pablo; Oliva, Erick; Osla, Edgar; Ramos-Galán, Maria Isabel; Sarceño, Scarlett; Feito, Francisco Ramón
In recent years, the application of geomatics tools in archaeology has proved to be very useful to obtain meaningful knowledge of the 3D reconstruction of archaeological remains and semantic classification of the 3D surface. These techniques have proven to be an effective solution for the 3D modeling and the extraction of many spatial features on an archaeological site. However, novel methodologies as well as new data exploitation strategies are required to exploit these geospatial data for natural and cultural heritage documentation, monitoring, and preservation. In this paper, we have studied unique archaeological ruins, a Mozarab church in Al-Andalus, using high-resolution RGB images, which was taken by a drone. Thus, a 3D reconstruction of the ruins and the surrounding environment is carried out in order to characterize it on a dense point cloud. Then, a digital elevation model (DEM) was calculated in order to identify critical slope lines, which are significant to determine where the structure of the church was built. Our results can be used for the development of an architectural project and thus a virtual recreation of these archaeological ruins was performed
Use of Data Mining to Predict the Influx of Patients to Primary Healthcare Centres and Construction of an Expert System
(MDPI, 2022-11-11) Cubillas, Juan J.; Ramos, María I.; Feito, Francisco R.
In any productive sector, predictive tools are crucial for optimal management and decision-making. In the health sector, it is especially important to have information available in advance, as this not only means optimizing resources, but also improving patient care. This work focuses on the management of healthcare resources in primary care centres. The main objective of this work is to develop a model capable of predicting the number of patients who will demand health care in a primary care centre on a daily basis. This model is integrated into a decision support system that is accessible and easy to use by the manager through a web application. In this case, data from a primary care centre in the city of Jaén, Spain, were used. The model was estimated using spatial-temporal training data, the daily health demand data in that centre for five years, and a series of meteorological data. Different regression algorithms have been employed. The workflow requires selecting the parameters that influence the health demand prediction and discarding those that distort the model. The main contribution of this research is the daily prediction of the number of patients attending the health centre with absolute errors better than 3%, which is crucial for decision-making on the sizing of health resources in a primary care health centre

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