Distance/correlation metrics have emerged as a robust and simplified tool for assessing the spectral characteristics of hyperspectral image pixels and effectively categorizing vegetation within a specific study area. Correlation methods provide a readily deployable and computationally efficient approach, rendering them particularly advantageous for applications in developing nations or regions with limited resources. This article presents a comparative investigation of correlation/distance metrics for the identification of vegetation pixels in hyperspectral imagery. The study facilitates a comprehensive evaluation of five distance and/or correlation metrics, namely, direct correlation, cosine similarity, normalized Euclidean distance, Bray–Curtis distance, and Pearson correlation. Direct correlation and Pearson correlation emerged as the two metrics that demonstrated the highest accuracy in vegetation pixel identification. Using the selected methodologies, a vegetation detection algorithm was implemented and validated using a hyperspectral image of the Manga neighborhood in Cartagena de Indias, Colombia. The spectral library facilitated image processing, while the mathematical calculation of correlations was performed using the numpy and scipy libraries in the Python programming language. Both the approach adopted in this study and the implemented algorithm aim to serve as a point of reference for conducting detection studies on diverse material types in hyperspectral imagery using open-access programming platforms.

Resumen: Mediante este artículo se presenta una propuesta de arquitectura para la agricultura de precisión soportada en tecnologías abiertas de Internet de las Cosas (IoT) para la obtención, monitoreo y análisis de variables climáticas. La propuesta se basa en la estructura de la arquitectura Lambda, considerando diferentes capas como son: La capa de captura de datos, cuya función es la obtención de variables asociadas a un cultivo. La capa de almacenamiento, cuya función es recopilar la información en tiempo real desde los sensores. La capa de procesamiento, genera predicciones y recomendaciones, la cual es evaluada mediante pruebas de carga con el fin de determinar su capacidad y tiempo de respuesta. La capa de consulta, permite a los usuarios finales visualizar en una interfaz web los datos climáticos y las predicciones. Así, esta arquitectura pretende servir de referencia para la implementación de servicios basados en IoT en el área de la agricultura.

The port, fortresses, and monuments of Cartagena were included in the UNESCO World Heritage list in 1984. Nevertheless, since 2017 they are threatened to be categorized as “in Danger” by UNESCO. This research analyzes two main critical aspects, governance and the current state of the site. This study applies an innovative interdisciplinary methodology that merges empirical and scientific approaches to evaluate the governance of Cartagena’s heritage (causes) and its impact on the current state of the tangible cultural heritage (effects). Methodologically, secondary information is used to study governance. The study aims to identify strengths, weaknesses, opportunities, and threats in the UNESCO site governance. Exemplary heritage management systems are studied to propose an adaptable management approach specifically for Cartagena. Over the past five years, qualitative data collected through field observations, along with a photographic report, have revealed significant issues stemming from insufficient heritage management. These problems have rarely been addressed in the existing literature. Furthermore, a quantitative approach was taken by analyzing hyperspectral images obtained from a previous study to identify vegetation and asbestos-cement roofs within the cultural heritage site. It takes a unique perspective by investigating the root causes rather than superficial issues. The study comprehensively analyzes various factors, including material degradation, vehicular traffic, environmental elements, urban structure, and architectural integrity, forming an interconnected framework with governance problems. This research offers a broader perspective that considers complex reality and goes beyond isolated problems and solutions. The study reveals that the ambiguity surrounding decision-making authority for heritage management, distributed between the Mayor’s Office and the Ministry of Culture, is a primary challenge. The lack of coordinated action significantly compromises the conservation and protection of the cultural heritage Site. Moreover, twelve current shortcomings of cultural heritage in Cartagena are identified through the authors’ five-year on-site regular visits, photographic reports, and observation. To address the prevailing concerns, a new line of command for cultural heritage management is proposed as the most effective means of tackling these challenges. Additionally, general recommendations are presented to mitigate the existing problems and prevent the classification of Cartagena’s heritage as “at risk” by UNESCO in the near future. Although, each problem identified would need a research project ad hoc to be fully addressed. This research provides a scientific perspective, drawing upon years of experience studying heritage and residing in the city, devoid of political influences or conflicts of interest.

This study introduces an innovative approach to evaluate the condition of asbestos–cement (AC) roofs by integrating field data with five distinct supervised learning models: decision trees, KNN, logistic regression, support vector machine, and random forest. A novel methodology for assessing the importance of 380 reflectance bands was employed, offering fresh insights into the key indicators of AC roof deterioration. The research systematically organized and prioritized reflectance bands based on their information gain, optimizing both the selection of relevant bands and the performance of the models in differentiating between low and high intervention priority (LIP and HIP) roofs. The decision tree model, when applied to the top 10 most relevant bands, achieved the highest cross-validation accuracy of 76.047%, making it the most effective tool for identifying AC roof conditions. Additionally, the random forest model demonstrated strong performance across various band groups, further validating its utility. Utilizing the open-source software Weka (version 3.8.6), this study adeptly executed relevance evaluation and model implementation, providing a practical and scalable solution for material characterization, especially in regions where resources for spectral and hyperspectral image analysis are limited. The findings of this study offer valuable tools for government and environmental authorities, particularly in developing countries, where efficient and cost-effective AC roof assessment is crucial for public health and safety. The methodology is adaptable to different urban environments and climatic conditions, supporting global efforts in asbestos management, especially in countries where asbestos regulations are newly implemented. Organized within the CRISP-DM framework, this paper details the methodological phases, presents compelling results on reflectance band relevance, evaluates machine learning models, and concludes with prospects for future research aimed at enhancing asbestos detection and removal strategies.

A partir de la creciente tendencia de la agricultura urbana, este trabajo tiene por objetivo la construcción de un sistema IoT para la monitorización y el análisis de variables climatológicas de interés en cultivos de agricultura urbana. La metodología considerada para el desarrollo de la presente investigación está constituida por cuatro fases: selección de herramientas y tecnologías, diseño de arquitectura del sistema IoT, construcción de prototipo del sistema y estudio de caso en el contexto del cultivo de lechuga. Como resultado de la presente investigación, se construyó un sistema IoT basado en herramientas de hardware y software libre, el cual está articulado dentro de la arquitectura convencional de cuatro capas de IoT (captura, almacenamiento, análisis y visualización). Con respecto a las soluciones existentes, la ventaja del sistema es el uso de plataformas portables SBC, así como la inclusión de modelos de aprendizaje automático dentro de la capa de análisis. A partir del estudio de caso, desarrollado sobre un cultivo de lechuga casero, se concluye que las herramientas escogidas permiten realizar de manera adecuada la captura, la monitorización y el análisis de variables climatológicas de interés en cultivos de agricultura urbana. Del mismo modo, se concluye que los modelos de análisis considerados pueden ser personalizados teniendo en cuenta las características agroclimáticas de cada cultivo y que resultan útiles para la toma de decisiones relacionadas con la fisiología de los cultivos.

En los últimos años una de las temáticas más difundidas en diferentes contextos de aplicación es la de los juegos serios, por tanto, a medida que su uso se masifica es conveniente articular de mejor forma los juegos serios en el contexto educativo, de tal manera que, posibiliten el desarrollo de competencias definidas en los planes de estudio y se diseñen centrados en el usuario. En este artículo, se propone como contribución un conjunto de 51 directrices para el diseño, construcción y evaluación de videojuegos serios educativos, las cuales incluyen aspectos relacionados con usabilidad, aprendizaje y el juego. Las directrices fueron empleadas para inspeccionar el videojuego lightbot, con el fin de determinar los aspectos de diseño a mejorar en dicho videojuego. Las directrices propuestas en este trabajo pretenden vincularse en el proceso de desarrollo y en las metodologías para la construcción de videojuegos, de cara a propiciar la generación de videojuegos serios educativos centrados en el usuario y más pertinentes al contexto educativo deseado.