Floods occur throughout the world and are becoming increasingly frequent and dangerous. This is due to different factors, among which climate change and land use stand out. In Mexico, they occur every year in different areas. Tabasco is a periodically flooded region, causing losses and negative consequences for the rural, urban, livestock, agricultural, and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the Earth’s surface and geospatial information processing tools useful for environmental and forest monitoring, climate change impacts, risk analysis, and natural disasters. This paper presents a strategy for the classification of flooded areas using satellite images obtained from synthetic aperture radar, as well as the U-Net neural network and ArcGIS platform. The study area is located in Los Rios, a region of Tabasco, Mexico. The results show that U-Net performs well despite the limited number of training samples. As the training data and epochs increase, its precision increases.

Floods are the most common phenomenon and cause the most significant economic and social damage to the population. They are becoming more frequent and dangerous. Consequently, it is necessary to create strategies to intervene effectively in the mitigation and resilience of the affected areas. Different methods and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the earth's surface, and geospatial information processing tools help manage different natural disasters. Likewise, deep learning is an approach capable of forecasting time series that can be applied to satellite images for flood prediction and mapping. This article presents an approach for flood segmentation and visualization using the U-Net architecture and Sentinel-1 synthetic aperture radar (SAR) satellite imagery. The U-Net architecture can capture relevant features in SAR images. The approach comprises various phases, from data loading and preprocessing to flood inference and visualization. For the study, the georeferenced dataset Sen1Floods11 is used to train and validate the model through different epochs and training. A study area in southeastern Mexico that presents frequent floods was chosen. The results demonstrate that the segmentation model achieves high accuracy in detecting flooded areas, with promising metrics regarding loss, precision, and F1-score.

There is a large amount of information in the form of unstructured documents which pose challenges in the information storage, search, and retrieval. This situation has given rise to several information search approaches. Some proposals take into account the contextual meaning of the terms specified in the query. Semantic annotation technique can help to retrieve and extract information in unstructured documents. We propose a semantic annotation strategy for unstructured documents as part of a semantic search engine. In this proposal, ontologies are used to determine the context of the entities specified in the query. Our strategy for extracting the context is focused on concepts similarity. Each relevant term of the document is associated with an instance in the ontology. The similarity between each of the explicit relationships is measured through the combination of two types of associations: the association between each pair of concepts and the calculation of the weight of the relationships.

Floods are common in much of the world, this is due to different factors among which climate change and land use stand out. In Mexico they happen every year in different entities. Tabasco is an entity that is periodically flooded, causing losses and negative consequences for the rural, urban, livestock, agricultural and service industries. Consequently, it is necessary to create strategies to intervene effectively in the affected areas. Therefore, different strategies and techniques have been developed to mitigate the damage caused by this phenomenon. Satellite programs provide a large amount of data on the earth’s surface as well as geospatial information processing tools that are useful for environmental and forest monitoring, climate change impacts, risk analysis, natural disasters, among others. This paper presents a strategy for the classification of flooded areas using satellite images radar of synthetic aperture and the U-NET neural network. The study area is centered on Los R´ıos, region of Tabasco, Mexico. The partial results show that U-NET performs well despite the limited amount in the training samples. As training data and epochs increased, its accuracy increased.

Crops and bodies of water are a topic of interest to countries. Having information about growing areas, water sources and their behaviour in the different seasons of the year is useful for agricultural production and decision-making. On the other hand. a large amount of Earth satellite data and tools for the processing of large Volumes of satellite images have now been generated that are critical for forest monitoring, multiteinporal analysis of growing areas and bodies of water, classification of land use, among others. Sentinel-2 is an Earth observation program consisting of 13 spectral bands that provide high spatial resolution and radiometric quality images. This article presents a multitemporal analysis based on the Standardized Difference Vegetation Index and Standardized Difference Water Index obtained with images of the Sentinel-2 satellite, for the identification of changes that occur in an area of south-eastern Mexico in the period 2018-2020. The results obtained demonstrate a low range of radiometric indexes in the study areas during 2018. In addition, the biggest changes during the rainy season were recorded in 2018; this shows the provocation in the decrease in crop quality and in the body of water.

There are two main approaches to automatic text classification: content-based classification and style-based classification. With content-based text classification, the topic of a document (politics, sports, health) or fake news is detected. On the other hand, Style-based text classification is used to detect the gender or age of an author, author identification, and authorship attribution. In style-based classification, the set of words defines the author’s vocabulary, which contains several hundred words. In this work, the words are known as dimensions. Texts generate high-dimensional vectors. Multiple works have shown that a large number of dimensions decreases the performance of classifiers. To reduce dimensions there are selection and extraction techniques. This article discusses the use of extraction techniques, which create low-dimensional vectors from combinations of the high-dimensional vector. Due to the development of Deep Learning networks, the use of dimension reduction techniques has decreased because these networks perform dimension reduction automatically. However, in Machine Learning such techniques are still used intensively. Motivated by the above, in this paper, the Principal Component Analysis (PCA) and Latent Semantic Analysis (LSA) dimension reduction algorithms are proposed for the identification of texts written by 14 authors of the Corpus PAN 2012. The texts were divided into sequences of 10, 20, and 30 words called sentences. Likewise, blocks of texts made up of 100 sentences were created. The supervised classification was performed with the Nearest Neighbors (KNN), Support Vector Machines (SVM) and Logistic Regression (LR) algorithms using the accuracy metric. The results showed that the reduction of dimensions with PCA and the LR and SVM classifiers achieved better results than other similar works of the state of the art using the same corpus.