La cartografía andaluza originada en el padrón real

Págs. 179-19

El Meridiano y el Antimeridiano de Tordesillas en la Geografía, la Náutica y la Cartografía

This is a technical study of the criteria and historical development, from the end of the fifteenth century, of attempts to measure and define correctly the meridian agreed in the treaty of Tordesillas. On the criteria adopted, an important role was played by the spaniard Jaime Ferres de Blanes in his studies on the calculation of degrees and leagues. A new phase of the problem began after the discovery of the Maluca Islands, the first intent to fix the position of the antemeridian being in 1512.Estudio técnico sobre los criterios y vicisitudes que se siguieron, desde finales del siglo XV, para medir y delimitar correctamente el meridiano aceptado en Tordesillas. Dentro de aquellos criterios se sitúa el importante papel del español Jaime Ferres de Blanes en sus estudios sobre cálculos de grados y de la legua. El meridiano de Tordesillas adquiere un nuevo significado después del descubrimiento de las islas Molucas, siendo en 1512 el primer intento de determinación de la posición del antemeridiano

SAR Nets: An Evaluation of Semantic Segmentation Networks with Attention Mechanisms for Search and Rescue Scenes.

This paper evaluates four semantic segmentation models in Search-and-Rescue (SAR) scenarios obtained from ground vehicles. Two base models are used (U-Net and PSPNet) to compare different approaches to semantic segmentation, such as skip connections between encoder and decoder stages and using a pooling pyramid module. The best base model is modified by including two attention mechanisms to analyze their performance and computational cost. We conduct a quantitative and qualitative evaluation using our SAR dataset defining eleven classes in disaster scenarios. The results demonstrate that the attention mechanisms increase model performance while minimally affecting the computation time.This work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021-122944OB-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Object Detection from Thermal Infrared and Visible Light Cameras in Search and Rescue Scenes

Visual object recognition is a fundamental challenge for reliable search and rescue (SAR) robots, where vision can be limited by lighting and other harsh environmental conditions in disaster sites. The goal of this paper is to explore the use of thermal and visible light images for automatic object detection in SAR scenes. With this purpose, we have used a new dataset consisting of pairs of thermal infrared (TIR) and visible (RGB) video sequences captured from an all-terrain vehicle moving through several realistic SAR exercises participated by actual first response organisations. Two instances of the open source YOLOv3 convolutional neural network (CNN) architecture are trained from annotated sets of RGB and TIR images, respectively. In particular, frames are labelled with four representative classes in SAR scenes comprising both persons civilian and first-responder) and vehicles (Civilian-car and response-vehicle). Furthermore, we perform a comparative evaluation of these networks that can provide insight for future RGB/TIR fusion.This work has been done in the framework of the TRUST-ROB project, funded by the Spanish Government (RTI2018-093421-B-I00). Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Disaster area recognition from aerial images with complex-shape class detection.

This paper presents a convolutional neural network (CNN) model for event detection from Unmanned Aerial Vehicles (UAV) in disaster environments. The model leverages the YOLOv5 network, specifically adapted for aerial images and optimized for detecting Search and Rescue (SAR) related classes for disaster area recognition. These SAR-related classes are person, vehicle, debris, fire, smoke, and flooded areas. Among these, the latter four classes lead to unique challenges due to their lack of discernible edges and/or shapes in aerial imagery, making their accurate detection and performance evaluation metrics particularly intricate. The methodology for the model training involves the adaptation of the pre-trained model for aerial images and its subsequent optimization for SAR scenarios. These stages have been conducted using public datasets, with the required image labeling in the case of SAR-related classes. An analysis of the obtained results demonstrates the model’s performance while discussing the intricacies related to complex-shape classes. The model and the SAR datasets are publicly available.This work has been partially funded by the Spanish Ministerio de Ciencia, Innovación y Universidades, Gobierno de España, project PID2021- 122944OB-I00. Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Eficacia del programa «(Des)venturas de Testas» para la promoción de un enfoque profundo de estudio

En este artículo se aporta información sobre la efi cacia de un programa tutorial para la mejora de los procesos de estudio y promoción de enfoques profundos de aprendizaje. El programa «(Des)venturas de Testas» se organiza en torno a un conjunto de libros que relatan las experiencias vividas por Testas, un alumno típico, a lo largo de su escolaridad. Estas historias constituyen la oportunidad para trabajar un amplio repertorio de estrategias de aprendizaje y procesos de autorregulación, pretendiendo capacitar a los alumnos para aprendizajes actuales y futuros más profundos. El programa se llevó a cabo durante un curso académico, una hora semanal en clases de tutoría. El diseño utilizado fue cuasi-experimental, con grupo experimental (n= 50) y grupo control (n= 49), y medidas pre y postest (conocimiento declarativo de estrategias de aprendizaje, enfoques de aprendizaje y rendimiento académico). Los datos obtenidos muestran que los alumnos que participan en el programa de capacitación, en relación a sus compañeros del grupo control, mejoran signifi cativamente en cuanto al dominio de conocimiento declarativo respecto de las estrategias de aprendizaje y del enfoque profundo, y disminuye el uso de un enfoque de estudio superfi cial, aunque no se obtienen mejoras estadísticamente signifi cativas en el rendimiento académico.Effi cacy of the program «Testas’s (mis)adventures» to promote the deep approach to learning. This paper provides information about the effi cacy of a tutorial training program intended to enhance elementary fi fth graders’ study processes and foster their deep approaches to learning. The program «Testas’s (mis) adventures» consists of a set of books in which Testas, a typical student, reveals and refl ects upon his life experiences during school years. These life stories are nothing but an opportunity to present and train a wide range of learning strategies and self-regulatory processes, designed to insure students’ deeper preparation for present and future learning challenges. The program has been developed along a school year, in a one hour weekly tutorial sessions. The training program had a semi-experimental design, included an experimental group (n=50) and a control one (n=50), and used pre- and posttest measures (learning strategies’ declarative knowledge, learning approaches and academic achievement). Data suggest that the students enrolled in the training program, comparing with students in the control group, showed a signifi cant improvement in their declarative knowledge of learning strategies and in their deep approach to learning, consequently lowering their use of a surface approach. In spite of this, in what concerns to academic achievement, no statistically signifi cant differences have been found

Dynamic path planning for reconfigurable rovers using a multi-layered grid

Autonomy on rovers is desirable in order to extend the traversed distance, and therefore, maximize the number of places visited during the mission. It depends heavily on the information that is available for the traversed surface on other planet. This information may come from the vehicle’s sensors as well as from orbital images. Besides, future exploration missions may consider the use of reconfigurable rovers, which are able to execute multiple locomotion modes to better adapt to different terrains. With these considerations, a path planning algorithm based on the Fast Marching Method is proposed. Environment information coming from different sources is used on a grid formed by two layers. First, the Global Layer with a low resolution, but high extension is used to compute the overall path connecting the rover and the desired goal, using a cost function that takes advantage of the rover locomotion modes. Second, the Local Layer with higher resolution but limited distance is used where the path is dynamically repaired because of obstacle presence. Finally, we show simulation and field test results based on several reconfigurable and non-reconfigurable rover prototypes and a experimental terrain

Análisis de técnicas de aumento de datos y entrenamiento en YOLOv3 para detección de objetos en imágenes RGB y TIR del UMA-SAR Dataset

Este trabajo ha recibido financiación del proyecto nacional RTI2018-093421-B-I00El uso de imágenes de los espectros visible (RGB) e infrarrojo térmico (TIR) para la detección de objetos puede resultar crucial en aplicaciones donde las condiciones de visibilidad están limitadas, como la robótica para búsqueda y rescate en catástrofes. Para ello resulta beneficioso analizar cómo las técnicas de aprendizaje profundo basadas en redes neuronales convolucionales (CNN) pueden aplicarse a ambas modalidades. En este artículo se analizan diferentes configuraciones y parámetros para el entrenamiento de CNN tanto para imágenes térmicas como para imágenes equivalentes del espectro visible. En concreto, se aborda el problema del sobre-entrenamiento para determinar una configuración eficaz de técnicas de aumento de datos y parada temprana. El caso de estudio se ha realizado con la red de código abierto YOLOv3, pre-entrenada con el dataset RGB COCO y optimizada (o re-entrenada) con el conjunto público de datos UMA-SAR dataset, que incluye pares de imágenes RGB y TIR obtenidas en ejercicios realistas de rescate.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech