Examinando por Autor "Feito, Francisco R."
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Ítem A formal framework for the representation of stack-based terrains(Taylor & Francis, 2018-05) Graciano-Segura, Alejandro; Rueda, Antonio J.; Feito, Francisco R.This paper presents a formal framework for the representation of three-dimensional geospatial data and the definition of common geographic information system (GIS) spatial operations. We use the compact stack-based representation of terrains (SBRT) in order to model geological volumetric data, both at the surface and subsurface levels, thus preventing the large storage requirements of regular voxel models. The main contribution of this paper is fitting the SBRT into the geo-atom theory in a seamless way, providing it with a sound formal geographic foundation. In addition we have defined a set of common spatial operations on this representation using the tools provided by map algebra. More complex geoprocessing operations or geophysical simulations using the SBRT as representation can be implemented as a composition of these fundamental operations. Finally a data model and an implementation extending the coverage concept provided by the Geography Markup Language standard are suggested. Geoscientists and GIS professionals can take advantage of this model to exchange and reuse geoinformation within a well-specified framework.Ítem 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.Ítem An efficient method for acquisition of spectral BRDFs in real-world scenarios(ELSEVIER, 2022-02) Jurado, Juan M.; Jiménez-Pérez, J. Roberto; Luís, Pádua; Feito, Francisco R.; Sousa, Joaquim J.Modelling of material appearance from reflectance measurements has become increasingly prevalent due to the development of novel methodologies in Computer Graphics. In the last few years, some advances have been made in measuring the light-material interactions, by employing goniometers/reflectometers under specific laboratory’s constraints. A wide range of applications benefit from data-driven appearance modelling techniques and material databases to create photorealistic scenarios and physically based simulations. However, important limitations arise from the current material scanning process, mostly related to the high diversity of existing materials in the real-world, the tedious process for material scanning and the spectral characterisation behaviour. Consequently, new approaches are required both for the automatic material acquisition process and for the generation of measured material databases. In this study, a novel approach for material appearance acquisition using hyperspectral data is proposed. A dense 3D point cloud filled with spectral data was generated from the images obtained by an unmanned aerial vehicle (UAV) equipped with an RGB camera and a hyperspectral sensor. The observed hyperspectral signatures were used to recognise natural and artificial materials in the 3D point cloud according to spectral similarity. Then, a parametrisation of Bidirectional Reflectance Distribution Function (BRDF) was carried out by sampling the BRDF space for each material. Consequently, each material is characterised by multiple samples with different incoming and outgoing angles. Finally, an analysis of BRDF sample completeness is performed considering four sunlight positions and 16x16 resolution for each material. The results demonstrated the capability of the used technology and the effectiveness of our method to be used in applications such as spectral rendering and real-word material acquisition and classification.Ítem An Efficient Method for Generating UAV-Based Hyperspectral Mosaics Using Push-Broom Sensors(IEEE, 2021-06) Jurado, Juan M.; Pádua, Luís; Hruska, Jonas; Feito, Francisco R.; Sousa, Joaquim J.Hyperspectral sensors mounted in unmanned aerial vehicles offer new opportunities to explore high-resolution multitemporal spectral analysis in remote sensing applications. Nevertheless, the use of hyperspectral data still poses challenges mainly in postprocessing to correct from high geometric deformation of images. In general, the acquisition of high-quality hyperspectral imagery is achieved through a time-consuming and complex processing workflow. However, this effort is mandatory when using hyperspectral imagery in a multisensor data fusion perspective, such as with thermal infrared imagery or photogrammetric point clouds. Push-broom hyperspectral sensors provide high spectral resolution data, but its scanning acquisition architecture imposes more challenges to create geometrically accurate mosaics from multiple hyperspectral swaths. In this article, an efficient method is presented to correct geometrical distortions on hyperspectral swaths from push-broom sensors by aligning them with an RGB photogrammetric orthophoto mosaic. The proposed method is based on an iterative approach to align hyperspectral swaths with an RGB photogrammetric orthophoto mosaic. Using as input preprocessed hyperspectral swaths, apart from the need of introducing some control points, the workflow is fully automatic and consists of: adaptive swath subdivision into multiple fragments; detection of significant image features; estimation of valid matches between individual swaths and the RGB orthophoto mosaic; and calculation of the best geometric transformation model to the retrieved matches. As a result, geometrical distortions of hyperspectral swaths are corrected and an orthomosaic is generated. This methodology provides an expedite solution able to produce a hyperspectral mosaic with an accuracy ranging from two to five times the ground sampling distance of the high-resolution RGB orthophoto mosaic, enabling the hyperspectral data integration with data from other sensors for multiple applications.Ítem An optimized approach for generating dense thermal point clouds from UAV-imagery(ELSEVIER, 2021-12) López, Alfonso; Jurado, Juan M.; Ogayar, Carlos J.; Feito, Francisco R.Thermal infrared (TIR) images acquired from Unmanned Aircraft Vehicles (UAV) are gaining scientific interest in a wide variety of fields. However, the reconstruction of three-dimensional (3D) point clouds utilizing consumer-grade TIR images presents multiple drawbacks as a consequence of low-resolution and induced aberrations. Consequently, these problems may lead photogrammetric techniques, such as Structure from Motion (SfM), to generate poor results. This work proposes the use of RGB point clouds estimated from SfM as the input for building thermal point clouds. For that purpose, RGB and thermal imagery are registered using the Enhanced Correlation Coefficient (ECC) algorithm after removing acquisition errors, thus allowing us to project TIR images into an RGB point cloud. Furthermore, we consider several methods to provide accurate thermal values for each 3D point. First, the occlusion problem is solved through two different approaches, so that points that are not visible from a viewing angle do not erroneously receive values from foreground objects. Then, we propose a flexible method to aggregate multiple thermal values considering the dispersion from such aggregation to the image samples. Therefore, it minimizes error measurements. A naive classification algorithm is then applied to the thermal point clouds as a case study for evaluating the temperature of vegetation and ground points. As a result, our approach builds thermal point clouds with up to 798,69% more point density than results from other commercial solutions. Moreover, it minimizes the build time by using parallel computing for time-consuming tasks. Despite obtaining larger point clouds, we report up to 96,73% less processing time per 3D point.Ítem Multispectral mapping on 3D models and multi-temporal monitoring for individual characterization of olive trees(MDPI, 2020-02) Jurado, Juan M.; Ortega, Lidia; Cubillas, Juan J.; Feito, Francisco R.3D plant structure observation and characterization to get a comprehensive knowledge about the plant status still poses a challenge in Precision Agriculture (PA). The complex branching and self-hidden geometry in the plant canopy are some of the existing problems for the 3D reconstruction of vegetation. In this paper, we propose a novel application for the fusion of multispectral images and high-resolution point clouds of an olive orchard. Our methodology is based on a multi-temporal approach to study the evolution of olive trees. This process is fully automated and no human intervention is required to characterize the point cloud with the reflectance captured by multiple multispectral images. The main objective of this work is twofold: (1) the multispectral image mapping on a high-resolution point cloud and (2) the multi-temporal analysis of morphological and spectral traits in two flight campaigns. Initially, the study area is modeled by taking multiple overlapping RGB images with a high-resolution camera from an unmanned aerial vehicle (UAV). In addition, a UAV-based multispectral sensor is used to capture the reflectance for some narrow-bands (green, near-infrared, red, and red-edge). Then, the RGB point cloud with a high detailed geometry of olive trees is enriched by mapping the reflectance maps, which are generated for every multispectral image. Therefore, each 3D point is related to its corresponding pixel of the multispectral image, in which it is visible. As a result, the 3D models of olive trees are characterized by the observed reflectance in the plant canopy. These reflectance values are also combined to calculate several vegetation indices (NDVI, RVI, GRVI, and NDRE). According to the spectral and spatial relationships in the olive plantation, segmentation of individual olive trees is performed. On the one hand, plant morphology is studied by a voxel-based decomposition of its 3D structure to estimate the height and volume. On the other hand, the plant health is studied by the detection of meaningful spectral traits of olive trees. Moreover, the proposed methodology also allows the processing of multi-temporal data to study the variability of the studied features. Consequently, some relevant changes are detected and the development of each olive tree is analyzed by a visual-based and statistical approach. The interactive visualization and analysis of the enriched 3D plant structure with different spectral layers is an innovative method to inspect the plant health and ensure adequate plantation sustainability.Ítem 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 MaríaCurrently, 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.Ítem Real-time visualization of 3D terrains and subsurface geological structures(Elsevier, 2018-01) Graciano, Alejandro; Rueda Ruiz, Antonio Jesús; Feito, Francisco R.Geological structures, both at the surface and subsurface levels, are typically represented by means of voxel data. This model presents a major drawback: its large storage requirements. In this paper, we address this problem and pro- pose the use of a stack-based representation for geological surface-subsurface structures. Although this representation has been mainly used for volumetric terrain visualization in previous works, it has been used as an auxiliary data structure. Therefore, our main contribution in this work is its use as a first-class representation for both processing and visualization of surface and subsurface in- formation. The proposed solution provides real-time visualization of volumetric terrains and subsurface geological structures represented as stacks using a com- pact data representation in the GPU. Different GPU memory implementations of the stacks have been described, discussing the tradeoffs between performance and storage efficiency. We also introduce a novel algorithm for the calculation of the surface normal vectors using a hybrid object-image space strategy. More- over, important features for geoscientific applications such as visualization of boreholes or geological cross sections, and selective attenuation of strata have also been implemented in a straightforward way.Ítem 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 centreÍtem Web-based GIS application for real-time interaction of underground infrastructure through virtual reality(ACM, 2017-11) Jurado, Juan M.; Graciano, Alejandro; Ortega, Lidia; Feito, Francisco R.Real-time visualization in web-based system remains challenging due to the amount of information associated to a 3D urban models. However, these 3D models are not able to provide advanced management of urban infrastructures, such as underground facilities. Nowadays, 3D GIS is considered the appropriate tool to provide accurate analysis and decision support based on spatial data. This paper presents a web-GIS application for 3D visualization, navigation, interaction and analysis of underground infrastructures through virtual reality. The growth of underground cities is a complex problem without easy solutions. In general, these infrastructures cannot be directly visualized. Thus, subsoil mapping can help us to develop a clearer representation of underground's pipes, cables or water mains. In addition, the approach of virtual reality provides an immersive experience and novelty interaction to acquire a complete knowledge about underground city structures. Experimental results show an integral application for the efficient management of underground infrastructure in real-time.