DICGF-Artículos

URI permanente para esta colecciónhttps://hdl.handle.net/10953/222

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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.
A method for checking the quality of geographic metadata based on ISO 19157
(Taylor & Francis, 2018) Ureña, Manuel Antonio; Nogueras-Iso, Javier; Lacasta, Javier; Ariza-López, Francisco Javier
With recent advances in remote sensing, location-based services and other related technologies, the production of geospatial information has exponentially increased in the last decades. Furthermore, to facilitate discovery and efficient access to such information, spatial data infrastructures were promoted and standardized, with a consideration that metadata are essential to describing data and services. Standardization bodies such as the International Organization for Standardization have defined well-known metadata models such as ISO 19115. However, current metadata assets exhibit heterogeneous quality levels because they are created by different producers with different perspectives. To address quality-related concerns, several initiatives attempted to define a common framework and test the suitability of metadata through automatic controls. Nevertheless, these controls are focused on interoperability by testing the format of metadata and a set of controlled elements. In this paper, we propose a methodology of testing the quality of metadata by considering aspects other than interoperability. The proposal adapts ISO 19157 to the metadata case and has been applied to a corpus of the Spanish Spatial Data Infrastructure. The results demonstrate that our quality check helps determine different types of errors for all metadata elements and can be almost completely automated to enhance the significance of metadata.
Abandoned Farmland Location in Areas A ected by Rapid Urbanization Using Textural Characterization of High Resolution Aerial Imagery
(MDPI, 2020-03-25) Ruiz Lendínez, Juan José
Several studies have demonstrated that farmland abandonment occurs not only in rural areas, but is also closely interlinked with urbanization processes. Therefore, the location of abandoned land and the registration of the spatial information referring to it play important roles in urban land management. However, mapping abandoned land or land in the process of abandonment is not an easy task because the limits between the di erent land uses are not clear and precise. It is therefore necessary to develop methods that allow estimating and mapping this type of land as accurately as possible. As an alternative to other geomatics methods such as satellite remote sensing, our approach proposes a framework for automatically locating abandoned farmland in urban landscapes using the textural characterization and segmentation of aerial imagery. Using the city of Pozna´n (Poland) as a case study, results demonstrated the feasibility of applying our approach, reducing processing time and workforce resources. Specifically and by comparing the results obtained with the data provided by CORINE Land Cover, 2275 ha (40.3%) of arable land within the city limits were abandoned, and the area of abandoned arable land was almost 9.2% of the city’s area. Finally, the reliability of the proposed methodology was assessed from two di erent focuses: (i) the accuracy of the segmentation results (from a positional point of view) and (ii) the e ciency of locating abandoned land (as a specific type of land use) in urban areas particularly a ected by rapid urbanization.
Accuracy Assessment of Digital Elevation Models (DEMs): A Critical Review of Practices of the Past Three Decades
(MDPI, 2020) Mesa-Mingorance, José Luis; Ariza-López, Francisco Javier
An analysis of almost 200 references has been carried out in order to obtain knowledge about the DEM (Digital Elevation Model) accuracy assessment methods applied in the last three decades. With regard to grid DEMs, 14 aspects related to the accuracy assessment processes have been analysed (DEM data source, data model, reference source for the evaluation, extension of the evaluation, applied models, etc.). In the references analysed, except in rare cases where an accuracy assessment standard has been followed, accuracy criteria and methods are usually established according to the premises established by the authors. Visual analyses and 3D analyses are few in number. The great majority of cases assess accuracy by means of point-type control elements, with the use of linear and surface elements very rare. Most cases still consider the normal model for errors (discrepancies), but analysis based on the data itself is making headway. Sample size and clear criteria for segmentation are still open issues. Almost 21% of cases analyse the accuracy in some derived parameter(s) or output, but no standardization exists for this purpose. Thus, there has been an improvement in accuracy assessment methods, but there are still many aspects that require the attention of researchers and professional associations or standardization bodies such as a common vocabulary, standardized assessment methods, methods for meta-quality assessment, and indices with an applied quality perspective, among others.
Active Shortening Simultaneous to Normal Faulting Based on GNSS, Geophysical, and Geological Data: The Seismogenic Ventas de Zafarraya Fault (Betic Cordillera, Southern Spain)
(Willey, 2024) Madarieta-Txurruka, Asier; González-Castillo, Lourdes; Peláez, José Antonio; Galindo, Jesús; Borque, María Jesús; de-Lacy, Maria Clara; Ruiz-Armenteros, Antonio Miguel; Henares, Jesús; Ruano, Patricia; Sánchez-Alzola, Alberto; Avilés-Moreno, Manuel; Rodríguez-Caderot, Gracia; Martínez-Moreno, Francisco José; Tendero-Salmerón, Víctor; Vinardell-Peña, Raquel; Gil-Cruz, Antonio José
The central Betic Cordillera, southern Spain, is affected by an uplift related to the NNW–SSE Eurasia-Nubia convergence and shallow ENE–WSW orthogonal extension accommodated by the extensional system of the Granada Basin. The combination of geophysical, geodetic, and geological data reveals that the southwestern boundary of this extensional system is a seismically active compressional front extending from the W to the SW of the Granada Basin. The near-field Global Navigation Satellite System data determine NNE–SSW shortening of up to 2 mm/yr of the compressional front in the Zafarraya Polje. In this setting, the normal Ventas de Zafarraya Fault developed as a result of the bending-moment extension of the Sierra de Alhama antiform and was last reactivated during the 1884 Andalusian earthquake (Mw 6.5). The uplift in the central Betic Cordillera together with the subsidence in the Western Alborán Basin may facilitate a westward to southwestward gravitational collapse through the extensional detachment of the Granada Basin. The heterogeneous crust of the Betic Cordillera would generate the compressional front, which is divided into two sectors: thrusting to the west, and folding associated with buttressing to the south. Our results evidence that basal detachments, linking extensional fault activity with compressional fronts, may determine the activity of local surface structures and the geological hazard in densely populated regions.
An Analysis of Existing Production Frameworks for Statistical and Geographic Information: Synergies, Gaps and Integration
(MDPI, 2021) Ariza-López, Francisco Javier; Rodríguez-Pascual, Antonio; López-Pellicer, Francisco Javier; Vilches-Blázquez, Luis Manuel; Villar-Iglesias, Agustín; Masó, Joan; Díaz-Díaz, Efrén; Ureña, Manuel Antonio; González-Yanes, Alberto
The production of official statistical and geospatial data is often in the hands of highly specialized public agencies that have traditionally followed their own paths and established their own production frameworks. In this article, we present the main frameworks of these two areas and focus on the possibility and need to achieve a better integration between them through the interoperability of systems, processes, and data. The statistical area is well led and has well-defined frameworks. The geospatial area does not have clear leadership and the large number of standards establish a framework that is not always obvious. On the other hand, the lack of a general and common legal framework is also highlighted. Additionally, three examples are offered: the first is the application of the spatial data quality model to the case of statistical data, the second of the application of the statistical process model to the geospatial case, and the third is the use of linked geospatial and statistical data. These examples demonstrate the possibility of transferring experiences/advances from one area to another. In this way, we emphasize the conceptual proximity of these two areas, highlighting synergies, gaps, and potential integration.
Analysing the positional accuracy of GNSS multi-tracks obtained from VGI sources to generate improved 3D mean axes
(Taylor & Francis, 2019) Mozas-Calvache, Antonio T.
The sharing of Global Navigation Satellite System (GNSS) tracks on the Internet is increasing enormously. Every day a great number of users capture routes using different devices and share these data. Individually these tracks present a poor positional accuracy because these devices obtain positions with accuracy of about 5-10 metres. In addition, they are usually captured for navigation and not for surveying. However, we can take advantage of the great quantity of tracks of the same linear element in order to obtain a more accurate solution. This study analyses this possibility using a wide set of tracks obtained in known conditions. We emulated those tracks obtained by Volunteered Geographic Information (VGI) users and we compared the mean axis obtained using all tracks with others obtained from a more accurate source. Additionally, we analyse the displacement of other axes obtained by varying several parameters such as the number of tracks and their length or by dividing the route into sections in function of sinuosity, etc. The results have shown an improved 3D mean axis and the viability of the method proposed in this study in order to use axes obtained from several tracks in maps at certain scales.
Analysis of Conditioning Factors in Cuenca, Ecuador, for Landslide Susceptibility Maps Generation Employing Machine Learning Methods
(MDPI, 2023) Bravo-López, Esteban; Fernández-del-Castillo, Tomás; Sellers, Chester; Delgado-García, Jorge
Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area
Analysis of Environmental and Atmospheric Influences in the Use of SAR and Optical Imagery from Sentinel-1, Landsat-8, and Sentinel-2 in the Operational Monitoring of Reservoir Water Level. Remote Sensing
(MDPI, 2022) de Oliveira Souza, Wendson; de Moura Reis, Luis Gustavo; Ruiz-Armenteros, Antonio M.; Veleda, Doris; Ribeiro Neto, Alfredo; Fragoso Jr., Carlos Ruberto; da Silva Pereira Cabral, Jaime Joaquim; Gico Lima Montenegro, Suzana Maria
Analysis of Spectral Separability for Detecting Burned Areas using Landsat-8 OLI/TIRS images under different biomes in Brazil and Portugal
(MDPI, 2023) da Penha Pacheco, Admilson; da Silva Junior, Juarez Antonio; Ruiz-Armenteros, Antonio M.; Faria Henriques, Renato Filipe; de Oliveira Santos, Ivaneide
Analysis of Urbanization-Induced Land Subsidence in the City of Recife (Brazil) Using Persistent Scatterer SAR Interferometry
(MDPI, 2024) de Oliveira Souza, Wendson; de Moura Reis, Luis Gustavo; da Silva Pereira Cabral, Jaime Joaquim; Ruiz-Armenteros, Antonio M.; Quental Coutinho, Roberto; da Penha Pacheco, Admilson; Ramos Aragão Junior, Wilson
Applying active learning by contextualizing robotic applications to historical heritage
(WILEY, 2023-09-17) Quesada Real, Francisco José; Pérez Peña, Fernando; Morgado Estévez, Arturo; Ruiz Lendínez, Juan José
Optional university courses are designed to allow undergraduate students to specialize in relevant fields to enhance their skills and knowledge for their future careers. However, there are some cases in which students prioritize enrolling in courses that are easy to pass. This choice results in having students with low motivation and commitment, who mainly focus on doing just enough to pass the course, missing the opportunity to boost their skills. In this study, an eclectic approach is proposed, applying a mixture of active learning methods together with the theory of multiple intelligences to improve students' performance, motivation, and commitment throughout the course. The study was applied to the 56 students enrolled in the optional Micro‐ Robotics Application spring course in the year 2021 at the University of Cádiz (Spain). Results demonstrate that this combination of active learning methodologies increased students' motivation, prompting them to give their best in terms of commitment, performance, and creativity. Furthermore, they were convinced that during the course they not only learned relevant robotic knowledge but also acquired essential skills needed for their future. Finally, this study highlights the benefits and future directions for implementing active learning methodologies in science, technology, engineering, and mathematics courses.
Assessing the accuracy of NRTK altimetric positioning for precision agriculture: test results in an olive grove environment in Southeast Spain
(Springer, 2018-07-24) Garrido-Carretero, María Selmira; de-Lacy, María Clara; Ramos-Galán, Maria Isabel; Borque, María Jesús; Susi, M
Soil erosion modeling in olive groves requires precise and accurate spatial data for the representation of topography associated with each time epoch considered. The precision and accuracy of altimetric values affect the quality of the digital elevation model (DEM) and therefore these requirements must be added to the necessity to generate high resolution DEMs. The increase of quality implies: 1. Improving the quality of the instrumentation and methodology applied in the field data collection and 2. Minimizing errors from the interpolation algorithm used to generate the digital terrain model. Currently, RTK networks are an indispensable complement to global navigation satellite systems (GNSS) precise positioning. The availability of highly accurate three-dimensional real time positioning has opened the door to new applications, making network-based real time kinematic (NRTK) positioning an attractive spatial data source for modeling soil erosion in small areas. This paper analyzes the quality of NRTK altimetric positioning supported by a local active network and its application in a test olive grove in SE Spain for soil erosion modeling. An evaluation procedure was implemented at several test sites distributed throughout an olive grove environment with special emphasis on filtering and checking the NRTK solutions in the vertical component. The precision in this component revealed a mean value of 15 mm and the vertical accuracy reached maximum values of 30 mm. In order to generate high resolution and accuracy DEM from the NRTK data, cross sections on the test olive grove were surveyed. The average altimetric quality value (CQ1D) of points surveyed was 0.017 m, according to the standard deviation estimated at test points. Based on the quality results, NRTK positioning is an accurate and reliable methodology for monitoring the erosion processes of small areas in an olive grove environment.
Assessment of k-Nearest Neighbor and Random Forest Classifiers for Mapping Forest Fire Areas in Central Portugal Using Landsat-8, Sentinel-2, and Terra Imagery
(MDPI, 2021) da Penha Pacheco, Admilson; da Silva Junior, Juarez Antonio; Ruiz-Armenteros, Antonio Miguel; Faria Henriques, Renato Filipe
Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
(MDPI, 2021-06-23) Ruiz Lendínez, Juan José; Ureña Cámara, Manuel Antonio; Mesa Mingorance, José Luis; Quesada Real, Francisco José
There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.
Automatic Positional Accuracy Assessment of Imagery Segmentation Processes: A Case Study
(MDPI, 2021) Ruiz-Lendínez, Juan J.; Ureña-Cámara, Manuel A.; Mesa-Mingorance, José L.; Quesada-Real, Francisco J.
There are many studies related to Imagery Segmentation (IS) in the field of Geographic Information (GI). However, none of them address the assessment of IS results from a positional perspective. In a field in which the positional aspect is critical, it seems reasonable to think that the quality associated with this aspect must be controlled. This paper presents an automatic positional accuracy assessment (PAA) method for assessing this quality component of the regions obtained by means of the application of a textural segmentation algorithm to a Very High Resolution (VHR) aerial image. This method is based on the comparison between the ideal segmentation and the computed segmentation by counting their differences. Therefore, it has the same conceptual principles as the automatic procedures used in the evaluation of the GI’s positional accuracy. As in any PAA method, there are two key aspects related to the sample that were addressed: (i) its size—specifically, its influence on the uncertainty of the estimated accuracy values—and (ii) its categorization. Although the results obtained must be taken with caution, they made it clear that automatic PAA procedures, which are mainly applied to carry out the positional quality assessment of cartography, are valid for assessing the positional accuracy reached using other types of processes. Such is the case of the IS process presented in this study.
Comparative Analysis between Remote Sensing Burned Area Products in Brazil: A Case Study in an Environmentally Unstable Watershed
(MDPI, 2024) da Silva Junior, Juarez Antonio; da Penha Pacheco, Admilson; Ruiz-Armenteros, Antonio M.; Faria Henriques, Renato Filipe
Dataset of three-dimensional traces of roads
(Nature Research, 2019) Ariza-López, Francisco Javier; Mozas, Antonio Tomás; Ureña, Manuel Antonio; Gil-de-la-Vega, Paula
We present a dataset consisting of three-dimensional traces, captured by Global Navigation Satellite System techniques with three-dimensional coordinates. It offers 138 traces (69 going and 69 returning), in addition to the actual mean axis of the road determined by precise surveying techniques to be used as ground truth for research activities. These data may serve as a test bed for research on data mining applications related to Global Navigation Satellite System multitraces, particularly the development and testing of algorithms intended for mining mean axis data from road multitraces. The data are suitable for the statistical analysis of both single-trace and multitrace datasets (e.g., outliers and biases).
Deep learning methods applied to digital elevation models: state of the art
(Taylor&Francis, 2023-09-06) Ruiz-Lendínez, Juan José; Ariza-López, Francisco Javier; Reinoso, Juan Francisco; Ureña, Manuel Antonio; Quesada-Real, Francisco José
Deep Learning (DL) has a wide variety of applications in various thematic domains, including spatial information. Although with limitations, it is also starting to be considered in operations related to Digital Elevation Models (DEMs). This study aims to review the methods of DL applied in the field of altimetric spatial information in general, and DEMs in particular. Void Filling (VF), Super-Resolution (SR), landform classification and hydrography extraction are just some of the operations where traditional methods are being replaced by DL methods. Our review concludes that although these methods have great potential, there are aspects that need to be improved. More appropriate terrain information or algorithm parameterisation are some of the challenges that this methodology still needs to face.

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