Las visualizaciones de mapas son usadas en diferentes a ‘reas para mostrar datos geográficos (por ejemplo, datos climatológicos u oceanográficos, resultados de análisis empresariales, entre otros). Estas visualizaciones se pueden encontrar en artículos de noticias, artículos científicos y en la Web; sin embargo, muchas de ellas están disponibles como imágenes en mapa de bits, lo que dificulta que el computador interprete los datos visualizados para su indexación y reutilización. En este trabajo proponemos una secuencia de pasos para recuperar la codificación visual a partir de imágenes en mapa de bits de mapas geográficos que utilizan el color para codificar los valores de los datos. Nuestros resultados fueron analizados usando mapas extraídos de documentos científicos, logrando una alta precisión en cada paso propuesto. Adicionalmente presentamos a iGeoMap, nuestro si...

Map visualizations are used in diverse domains to show geographic data (e.g., climate research, oceanography, business analyses, etc.). These visualizations can be found in news articles, scientific papers, and on the Web. However, many map visualizations are available only as bitmap images, hindering machine interpretation of the visualized data for indexing and reuse. In this work, we propose a pipeline to recover the visual encodings from bitmap images of geographic maps with color-encoded scalar values. We evaluate our results using map images from scientific documents, achieving high accuracy along each step of the pipeline. In addition, we present iGeoMap, our web-based system that uses the extracted visual encoding to enable user-interaction over bitmap images of map visualizations.

Stress in cattle is one of the main factors that generate economic losses in the livestock sector (e.g., reduction in the quality of milk or meat). In this field, heat stress has been considered as one of the main types of stress that negatively affects cattle. In addition, thanks to the arising of the Internet of Things in Animal Health, some researchers have proposed systems and models for the detection of this type of stress in an automated way, collecting and using data from meteorological variables (e.g., temperature, humidity), heart rate and others. However, the proposed models are mainly focused on heat stress detection that uses threshold-based estimation to determine the presence of stress; but, the level of stress experienced by cows can vary depending on their breed, or their ability to adapt to the environment where they are located. Therefore, in this project we propose an ...