DETECCIÓN AUTOMÁTICA DE CUERPOS DE AGUA DEL BAJÍO UTILIZANDO PARÁMETROS MORFOMÉTRICOS OBTENIDOS DE IMÁGENES SATELITALES Y PROCESADOS CON REDES NEURONALES (AUTOMATIC DETECTION OF WATER BODIES OF EL BAJÍO USING MORPHOMETRIC PARAMETERS OBTAINED FROM SATELLITE IMAGES AND PROCESSED WITH NEURONAL NETWORKS)

Resumen La conservación de las masas de agua superficiales es un tema crucial para el desarrollo socioeconómico de los asentamientos humanos. Por tanto, el seguimiento constante de la distribución territorial del agua es cada vez más una prioridad. Las imágenes procesadas fueron obtenidas de la misión satelital SENTINEL 2 que provee su información en base a 13 bandas multiespectrales, de las cuales se usaron las bandas B3 (verde) y B8 (infrarrojo cercano) en el cálculo del parámetro de identificación de agua NDWI. En este trabajo se propone un sistema de identificación de masas de agua mediante redes neuronales a partir de una base de datos de clasificadores que permite a la red discriminar píxel a píxel si existe o no presencia de agua. La red se implementa en dos ambientes diferentes: Matlab y Google Colaboratory, dos plataformas que logran obtener buenos resultados en el diseño de modelos de redes neuronales. En las pruebas realizadas queda demostrada la capacidad predictiva del sistema implementado, logrando un rendimiento por encima del 90% de precisión, cumpliendo con los objetivos de identificación. Palabras Clave: cuerpos de agua, visión artificial, Google Colaboratory, Matlab, red neuronal. Abstract The conservation of surface water bodies is a crucial issue for the socioeconomic development of human settlements. Therefore, the constant monitoring of territorial water distribution is increasingly a priority. The processed images were obtained from the SENTINEL 2 satellite mission that provides its information based on 13 multispectral bands, of which bands B3 (green) and B8 (near infrared) were used in the calculation of the water identification parameter NDWI. In this work, a system for identifying bodies of water using neural networks is proposed from a database of classifiers that allows the network to discriminate pixel by pixel whether it is in the presence of water or not. The network is implemented in two different environments: Matlab and Google Colaboratory, two platforms that achieve good results in the design of neural network models. In the tests carried out, the predictive capacity of the implemented system is demonstrated, achieving a performance above 90% accuracy, meeting the identification objectives. Keywords: water bodies, artificial vision, Google Colaboratory, Matlab, neural network

GPS Data Correction Based on Fuzzy Logic for Tracking Land Vehicles

GPS sensors are widely used to know a vehicle’s location and to track its route. Although GPS sensor technology is advancing, they present systematic failures depending on the environmental conditions to which they are subjected. To tackle this problem, we propose an intelligent system based on fuzzy logic, which takes the information from the sensors and correct the vehicle’s absolute position according to its latitude and longitude. This correction is performed by two fuzzy systems, one to correct the latitude and the other to correct the longitude, which are trained using the MATLAB ANFIS tool. The positioning correction system is trained and tested with two different datasets. One of them collected with a Pmod GPS sensor and the other a public dataset, which was taken from routes in Brazil. To compare our proposal, an unscented Kalman filter (UKF) was implemented. The main finding is that the proposed fuzzy systems achieve a performance of 69.2% higher than the UKF. Furthermore, fuzzy systems are suitable to implement in an embedded system such as the Raspberry Pi 4. Another finding is that the logical operations facilitate the creation of non-linear functions because of the ‘if else’ structure. Finally, the existence justification of each fuzzy system section is easy to understand