Detection and Reinforcement of Celiac Communities on Twitter Argentina

Social Networks have shown great growth relating the number of their users and generated content. For example, Twitter is used asa means to gather support, express ideas and opinions on various topicsor interact with users with similar interests. In the latter case, the ideaof community formation appears, that is, groups of users that are moreclosely related to each other than the rest of the nodes in the network.In this work we propose the detection of the community of users ofArgentina interested in the celiac disease. We apply a series of techniques to detect and characterize them. In addition, we propose anduse a methodology for the detection of more influential and active nodes(users), showing how the community can be reinforced by the recommendation of some particular links. The results show that with only a lowpercentage of accepted recommendation the network becomes denser andaverage distance between two users decreases quickly, thus improving thespread of information.Fil: Giordano, Luis Andres. Universidad Nacional de Luján. Departamento de Ciencias Básicas; ArgentinaFil: Banchero, Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján. Departamento de Ciencias Básicas; ArgentinaFil: Cerny, Natacha. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján. Departamento de Ciencias Básicas; ArgentinaFil: de Marzi, Mauricio Cesar. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Tolosa, Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Luján. Departamento de Ciencias Básicas; Argentin

Recent land use and land cover change dinamics in the Gran Chaco Americano

Land transformation is one of the most significant human changes on the Earth’s surface processes. Therefore, land use land cover time series are a key input for environmental monitoring, natural resources management, territorial planning enforcement at national scale. We here capitalize from the MapBiomas initiative to characterize land use land cover (LULC) change in the Gran Chaco between 2010 and 2017. Specifically we sought to a) quantify annual changes in the main LULC classes; b) identify the main LULC transitions and c) relate these transitions to current land use policies. Within the MapBiomas project, Landsat based annual maps depicting natural woody vegetation, natural herbaceous vegetation, dispersed natural vegetation, cropland, pastures, bare areas and water. We used Random Forest machine learning algorithms trained by samples produced by visual interpretation of high resolution images. Annual overall accuracy ranged from 0,73 to 0,74. Our results showed that, between 2010 and 2017, agriculture and pasture lands increased ca. 3.7 Mha while natural forestry decreased by 2.3 Mha. Transitions from forests to agriculture accounted for 1.14% of the overall deforestation while 86% was associated to pastures and natural herbaceous vegetation. In Argentina, forest loss occurred primarily (39%) on areas non considered by the territorial planning Law, followed by medium (33%), high (19%) and low (9%) conservation priority classes. These results illustrate the potential contribution of remote sensing to characterize complex human environmental interactions occurring over extended areas and timeframes.Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: de Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, S.R. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Arevalos, F. Asociación Guyra Paraguay; ParaguayFil: Volante, J.N. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Mosciaro, M.J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; Argentin

Which pixel is a forest? Tree crown delineation using VHR images to estimate tree cover in landsat based classification

Determining the percentage of tree crown cover is extremely important to establish in advance which forest types can be classified with high resolution sensors such as Landsat. This paper describes the determination of a tree crown coverage threshold to define whether a pixel is classified as a forest or not. The methodology consists in the comparison of forest/non-forest classifications generated from Landsat images with tree crown cover maps obtained from PlanetScope very high resolution images, considering those pixels that exceed a given canopy cover threshold (eg. 5-10-15-...90-95-100%) as forest. The canopy coverage threshold was the one that minimized the difference between the Landsat classification and the maps generated from Planet images.Fil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: de Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Ferraina, A. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Propato, T. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires; Facultad de Agronomía; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gomez Taffarel, María Cielo. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Dieguez, Hernán. Universidad de Buenos Aires; Facultad de Agronomía; Argentin

Detección de outliers en muestras de entrenamiento generadas mediante interpretación visual

Las clasificaciones supervisadas son procesos extremadamente sensibles a la calidad de las muestras utilizadas. La presencia de outliers en las muestras de entrenamiento suele ser una fuente de error muy frecuente. El objetivo de este trabajo es presentar una metodología de detección de outliers con Isolation Forest, en muestras recolectadas mediante interpretación visual de imágenes satelitales generadas por el Proyecto MapBiomas Pampa Trinacional. Isolation Forest, el algoritmo no supervisado utilizado puede detectar anomalías directamente basándose en el concepto de aislamiento sin utilizar ninguna métrica. La metodología consiste en la identificación de outliers (preparación de muestras, modelado y definición del umbral) y la validación del método. El modelado permite etiquetar de manera automática cada muestra como outlier o normal a partir del score. Se logró verificar los píxeles de la muestra señalada como outlier y tipificar el error en 6 categorías. Los resultados muestran una cantidad decreciente de outliers a lo largo del periodo analizado. Los años con mayor cantidad de outliers tienen una correspondencia con los años de menor disponibilidad de imágenes para la construcción de los mosaicos y contribuciones importantes del tipo Error del Mosaico. La clase con mayor porcentaje de error fue Bosque cerrado (14.7%) y los tipos de errores con mayor proporción fueron Clase Mal Asignada (20.39%) y Borde (19.57%). La metodología propuesta permitió el mejoramiento de muestras obtenidas mediante interpretación visual de imágenes satelitales de manera automática con un 80% de acierto.Sociedad Argentina de Informática e Investigación Operativ

Detection and Reinforcement of Celiac Communities on Twitter Argentina

Social Networks have shown great growth relating the number of their users and generated content. For example, Twitter is used as a means to gather support, express ideas and opinions on various topics or interact with users with similar interests. In the latter case, the idea of community formation appears, that is, groups of users that are moreclosely related to each other than the rest of the nodes in the network. In this work we propose the detection of the community of users of Argentina interested in the celiac disease. We apply a series of techniques to detect and characterize them. In addition, we propose and use a methodology for the detection of more influential and active nodes(users), showing how the community can be reinforced by the recommendation of some particular links. The results show that with only a low percentage of accepted recommendation the network becomes denser and average distance between two users decreases quickly, thus improving the spread of information.Special Issue dedicated to JAIIO 2018 (Jornadas Argentinas de Informática).Sociedad Argentina de Informática e Investigación Operativ

Savannah land cover characterisation: a quality assessment using Sentinel 1/2, Landsat, PALSAR and PlanetScope

The importance of savannahs worldwide has been widely acknowledged and documented. They are important ecosystems that are found on almost half of the African continent and a fifth of the Earth’s surface. They consist of varying densities of grasses and woody vegetation and,due to their complexity, they have been centre stage for a number of land system science debates, such as, equilibrium dynamics, land degradation and desertification, or their contribution to the global carbon cycle, to name but a few. Their significance is directly linked to the numerous recent efforts to map and monitor their land cover as accurately as possible, most commonly by employing Earth observation technologies. A recent case study by Symeonakis et al. (2018), carried out in a southern African savannah covering an area of ~44,000km2, tested the performance of different combinations of Landsat- and PALSAR-based metrics from the dry and wet seasons. They concluded that the combination of multi-sensor and multi-season data provides the best results when mapping the main land cover types of woody vegetation, grasses, crops and urban/bare. Here, we take this work further by testing the performance of 15 similar models, this time based on a combination of Sentinel-1 and Sentinel-2 metrics from the dry and wet season, using the same study area, training and validation samples (extracted from 0.5 m RGB aerial photography). Our results corroborate the findings of the previous study: the combination of the Sentinel optical and C-band radar data from both seasons yielded the most accurate land cover Random Forests classifications: overall accuracy of 87%, overall k: 0.83. Very high user’s and producer’s accuracies were also found, especially for the woody class (User’s: 93%; Producer’s: 93%), which is of primary concern in these savannah environments, due to its relevance to the land degradation processes that are dominant in the region: bush encroachment, overexploitation for fuelwood and deforestation. Similar to the Landsat/PALSAR study, the models based on SAR data only, were less accurate than the optical models. As anticipated, the L-band PALSAR data used in the previous study performed better in mapping the woody cover than their C-band counterparts, since L-band radiation is able to penetrate through the canopy layer and reach the woody stems and branches more efficiently. However, and interestingly, the Landsat-PALSAR study generally outperformed the Sentinel1/2 study, e.g. reporting the same omission but a 5% lower commission error for the woody class (estimated from the all-parameter model). Additionally, we tested a novel land cover configuration mapping accuracy approach using PlanetScope 3m-pixel data, with the view to assessing the quality of the mapping of landscape configuration (e.g. number of crop paddocks) and not only its composition (e.g. crop vs other cover). For five test areas of 170 km2 each, we performed object-based classifications of the PlanetScope imagery (two images per test area, one from the dry and one from the wet season). The results were less promising than those achieved for land cover composition: as an example, in one of our areas of focus, we determined 30 crop paddocks, 28 of which were of the circular centre pivot type ranging from 0.06 to 0.75 km2. For the same area, we mapped a significantly larger number of paddocks with the Sentinel 2 data, most of which were of the rectangular-shaped type. This discrepancy was attributed to the spectral similarity between the crop and grass land cover types, as well as the averaging effect of the lower spatial resolution of the Sentinel. Our findings, however, suggesting a multi-sensor and multi-seasonal approach, are an important addition to the emerging literature and can be used to guide efforts for achieving a highly accurate savannah land cover characterisation. Future work will investigate the use of the PlanetScope object-based classifications as training of the coarser-resolution models (e.g. Seninel 1- and 2-based metrics), not only for improving the mapping of land cover configuration, but also for mapping fractional cover, e.g. % woody vegetation

Procesamiento eficiente de grafos masivos para aplicaciones en redes sociales

Las redes sociales digitales se han convertido sin dudas en una de las aplicaciones más populares de Internet y atraen a millones de usuarios que, de forma implícita, generan estructuras con propiedades emergentes que surgen del comportamiento global. Existen diversos problemas interesantes a resolver como la formación de comunidades, la recomendación de enlaces y el estudio de la polarización de opiniones. En todos los casos, resulta motivador tanto el proceso de formación como el estudio de algoritmos eficientes para el procesamiento. Estos problemas se pueden abordar estudiando el grafo subyacente, el contenido de las publicaciones o combinaciones de ambos. En este trabajo se proponen diversas líneas de investigación sobre los temas mencionados, con aplicaciones a grafos masivos y problemas reales. Se abordan tanto problemas algorítmicos en cuanto a la eficiencia como las interacciones entre usuarios y diferentes escenarios.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Procesamiento eficiente de grafos masivos para aplicaciones en redes sociales

Las redes sociales digitales se han convertido sin dudas en una de las aplicaciones más populares de Internet y atraen a millones de usuarios que, de forma implícita, generan estructuras con propiedades emergentes que surgen del comportamiento global. Existen diversos problemas interesantes a resolver como la formación de comunidades, la recomendación de enlaces y el estudio de la polarización de opiniones. En todos los casos, resulta motivador tanto el proceso de formación como el estudio de algoritmos eficientes para el procesamiento. Estos problemas se pueden abordar estudiando el grafo subyacente, el contenido de las publicaciones o combinaciones de ambos. En este trabajo se proponen diversas líneas de investigación sobre los temas mencionados, con aplicaciones a grafos masivos y problemas reales. Se abordan tanto problemas algorítmicos en cuanto a la eficiencia como las interacciones entre usuarios y diferentes escenarios.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic

Temporally-Consistent Annual Land Cover from Landsat Time Series in the Southern Cone of South America

The impact of land cover change across the planet continues to necessitate accurate methods to detect and monitor evolving processes from satellite imagery. In this context, regional and global land cover mapping over time has largely treated time as independent and addressed temporal map consistency as a post-classification endeavor. However, we argue that time can be better modeled as codependent during the model classification stage to produce more consistent land cover estimates over long time periods and gradual change events. To produce temporally-dependent land cover estimates—meaning land cover is predicted over time in connected sequences as opposed to predictions made for a given time period without consideration of past land cover—we use structured learning with conditional random fields (CRFs), coupled with a land cover augmentation method to produce time series training data and bi-weekly Landsat imagery over 20 years (1999–2018) across the Southern Cone region of South America. A CRF accounts for the natural dependencies of land change processes. As a result, it is able to produce land cover estimates over time that better reflect real change and stability by reducing pixel-level annual noise. Using CRF, we produced a twenty-year dataset of land cover over the region, depicting key change processes such as cropland expansion and tree cover loss at the Landsat scale. The augmentation and CRF approach introduced here provides a more temporally consistent land cover product over traditional mapping methods.EEA SaltaFil: Graesser, Jordan. Boston University. Department of Earth and Environment; Estados UnidosFil: Stanimirova, Radost. Boston University. Department of Earth and Environment; Estados UnidosFil: Tarrio, Katelyn. Boston University. Department of Earth and Environment; Estados UnidosFil: Copati, Esteban J. Bolsa de Cereales (Buenos Aires); ArgentinaFil: Volante, J. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Verón, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Verón, Sebastian. Universidad de Buenos Aires. Facultad de Agronomía; ArgentinaFil: Verón, Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Banchero, S. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Elena, Hernan Javier. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Salta; ArgentinaFil: Abelleyra, D. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Friedl, Mark A. Boston University. Department of Earth and Environment; Estados Unido

Procesamiento eficiente de grafos masivos para aplicaciones en redes sociales

Las redes sociales digitales se han convertido sin dudas en una de las aplicaciones más populares de Internet y atraen a millones de usuarios que, de forma implícita, generan estructuras con propiedades emergentes que surgen del comportamiento global. Existen diversos problemas interesantes a resolver como la formación de comunidades, la recomendación de enlaces y el estudio de la polarización de opiniones. En todos los casos, resulta motivador tanto el proceso de formación como el estudio de algoritmos eficientes para el procesamiento. Estos problemas se pueden abordar estudiando el grafo subyacente, el contenido de las publicaciones o combinaciones de ambos. En este trabajo se proponen diversas líneas de investigación sobre los temas mencionados, con aplicaciones a grafos masivos y problemas reales. Se abordan tanto problemas algorítmicos en cuanto a la eficiencia como las interacciones entre usuarios y diferentes escenarios.Eje: Bases de Datos y Minería de Datos.Red de Universidades con Carreras en Informátic