DICGF-Artículos

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

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Land Cover Transformations in Mining-Influenced Areas Using PlanetScope Imagery, Spectral Indices, and Machine Learning: A Case Study in the Hinterlands de Pernambuco, Brazil
(MDPI, 2025-02-06) da Penha Pacheco, Admilson; Silva do Nascimento, João Alexandre; Ruiz-Armenteros, Antonio Miguel; da Silva Junior, Ubiratan Joaquim; da Silva Junior, Juarez Antonio; Maciel de Oliveira, Leidjane Maria; Melo dos Santos, Sylvana; Dacal Reis Filho, Fernando; Pessoa Mello Galdino, Carlos Alberto
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 Gordo, Juan Francisco; Ureña Cámana, 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.
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.
Expert Knowledge as Basis for Assessing an Automatic Matching Procedure
(MDPI, 2021-05-02) Ruiz Lendínez, Juan José; Ariza López, Francisco Javier; Ureña Cámara, Manuel Antonio
The continuous development of machine learning procedures and the development of new ways of mapping based on the integration of spatial data from heterogeneous sources have resulted in the automation of many processes associated with cartographic production such as positional accuracy assessment (PAA). The automation of the PAA of spatial data is based on automated matching procedures between corresponding spatial objects (usually building polygons) from two geospatial databases (GDB), which in turn are related to the quantification of the similarity between these objects. Therefore, assessing the capabilities of these automated matching procedures is key to making automation a fully operational solution in PAA processes. The present study has been developed in response to the need to explore the scope of these capabilities by means of a comparison with human capabilities. Thus, using a genetic algorithm (GA) and a group of human experts, two experiments have been carried out: (i) to compare the similarity values between building polygons assigned by both and (ii) to compare the matching procedure developed in both cases. The results obtained showed that the GA—experts agreement was very high, with a mean agreement percentage of 93.3% (for the experiment 1) and 98.8% (for the experiment 2). These results confirm the capability of the machine-based procedures, and specifically of GAs, to carry out matching tasks.
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.
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.
Method for an automatic alignment of imagery and vector data applied to cadastral information in Poland
(Taylor&Francis, 2017-10-20) Ruiz Lendínez, Juan José; Maćkiewicz, Barbara; Motek, Pavel; Stryjakiewicz, Tadeusz
Nowadays, an important problem in combining vector data and imagery is that they rarely align. This problem can become particularly acute in the case of cadastral systems. In this study, and as part of the partnership between the Universities of Jaén and Adam Mickiewicz (Poznań), we provide a methodological proposal to assess the conflation procedures between cadastral vector data and imagery, improving the alignment between both data sets. To do this, we use an automatic alignment algorithm which detects road intersections from both data sets as control points by using image texture characterisation. With this method, we first train the system on the imagery to learn the road texture distribution, then we can obtain its segmentation according to its texture, and finally the system locates road intersection points. The last step is to align vector data and imagery by using different techniques. This algorithm is based on an earlier one, detailed in [Ruiz, J.J., Rubio, T.J., and Ureña, M.A., 2011b. Automatic extraction of road intersections from images in conflation processes based on texture characterization. Survey review, 43 (321), 212–225.]. However, in the updated version we have solved the problem of not-well-defined intersection points, resulting in a substantial increase in the number of intersection points employed for the final adjustment to align both products and in a reduction of the computation time. On the other hand, the positional uncertainty assessment of parcel boundary lines both before and after applying our alignment procedure between them is provided. With regard to the experimental results, in the case of Polish cadastral data this procedure allows for significant improvement in the alignment between imagery and cadastral parcels boundaries.
Study of NSSDA Variability by Means of Automatic Positional Accuracy Assessment Methods
(MDPI, 2019-12-02) Ruiz Lendínez, Juan José; Ariza López, Francisco Javier; Ureña Cámara, Manuel Antonio
Point-based standard methodologies (PBSM) suggest using ‘at least 20’ check points in order to assess the positional accuracy of a certain spatial dataset. However, the reason for decreasing the number of checkpoints to 20 is not elaborated upon in the original documents provided by the mapping agencies which develop these methodologies. By means of theoretical analysis and experimental tests, several authors and studies have demonstrated that this limited number of points is clearly insu cient. Using the point-based methodology for the automatic positional accuracy assessment of spatial data developed in our previous study Ruiz-Lendínez, et al (2017) and specifically, a subset of check points obtained from the application of this methodology to two urban spatial datasets, the variability of National Standard for Spatial Data Accuracy (NSSDA) estimations has been analyzed according to sample size. The results show that the variability of NSSDA estimations decreases when the number of check points increases, and also that these estimations have a tendency to underestimate accuracy. Finally, the graphical representation of the results can be employed in order to give some guidance on the recommended sample size when PBSMs are used.
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
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
Empowering intermediate cities: cost-effective heritage preservation through satellite remote sensing and deep learning
(Taylor & Francis, 2024) Rodríguez-Antuñano, I.; Sousa, Joaquim J.; Bakon, M.; Ruiz-Armenteros, Antonio M.; Martínez-Sánchez, J.; Riveiro, B.
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 A.; Galindo-Zaldívar, Jesús; Borque, María J.; Lacy, María C.; Ruiz-Armenteros, Antonio M.; Henares, Jesús; Ruano, Patricia; Sánchez-Alzola, Alberto; Avilés, Manuel; Rodríguez-Caderot, Gracia; Martínez-Moreno, Francisco José; Tendero-Salmerón, Víctor; Vinardell-Peña, Raquel; Gil, Antonio J.
Study of Recent Deformations in the Bogotá Savanna and the City of Bogotá (Colombia) Using Multi-Temporal Satellite Radar Interferometry
(MDPI, 2023) Tamayo Duque, Juan S.; Ruiz-Armenteros, Antonio M.; Ávila Álvarez, Guillermo E.; Matiz, Gustavo; Sousa, Joaquim J.
MT-InSAR and Dam Modeling for the Comprehensive Monitoring of an Earth-Fill Dam: The Case of the Benínar Dam (Almería, Spain)
(MDPI, 2023) Marchamalo-Sacristán, Miguel; Ruiz-Armenteros, Antonio M.; Lamas Fernández, Francisco; González-Rodrigo, Beatriz; Martínez-Marín, Rubén; Delgado-Blasco, José Manuel; Bakon, Matus; Milan, Lazecky; Daniele, Perissin; Juraj, Papco; Sousa, Joaquim J.
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
Risk Evaluation of the Sanalona Earthfill Dam Located in Mexico Using Satellite Geodesy Monitoring and Numerical Modeling
(MDPI, 2023) Vázquez-Ontiveros, J. René; Ruiz-Armenteros, Antonio M.; de Lacy, M Clara; Gaxiola-Camacho, J. Ramon; Anaya-Díaz, Miguel; Vázquez-Becerra, G. Esteban
Evaluation of the Ability of SLSTR (Sentinel-3B) and MODIS (Terra) Images to Detect Burned Areas Using Spatial-Temporal Attributes and SVM Classification
(MDPI, 2023) da Silva Junior, Juarez Antonio; da Penha Pacheco, Admilson; Ruiz-Armenteros, Antonio M.; Faria Henriques, Renato Filipe
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
Uncertainties Involved in the Use of Thresholds for the Detection of Water Bodies in Multitemporal Analysis from Landsat-8 and Sentinel-2 Images
(MDPI, 2021) de Moura Reis, Luis Gustavo; de Oliveira Souza, Wendson; Ribeiro Neto, Alfredo; Fragoso, Carlos Ruberto Jr.; Ruiz-Armenteros, Antonio M.; da Silva Pereira Cabral, Jaime Joaquim; Gico Lima Montenegro, Suzana Maria
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

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