Volcanic ash delimitation using Artificial Intelligence based on Pix2Pix

Volcanic eruptions emit ash that can be harmful to human health and cause damage to infrastructure, economic activities and the environment. The delimitation of ash clouds allows to know their behavior and dispersion, which helps in the prevention and mitigation of this phenomenon. Traditional methods take advantage of specialized software programs to process the bands or channels that compose the satellite images. However, their use is limited to experts and demands a lot of time and significant computational resources. In recent years, Artificial Intelligence has been a milestone in the computational treatment of complex problems in different areas. In particular, Deep Learning techniques allow automatic, fast and accurate processing of digital images. The present work proposes the use of the Pix2Pix model, a type of generative adversarial network that, once trained, learns the mapping of input images to output images. The architecture of such a network consisting of a generator and a discriminator provides the versatility needed to produce black and white ash cloud images from multispectral satellite images. The evaluation of the model, based on loss and accuracy plots, a confusion matrix, and visual inspection, indicates a satisfactory solution for accurate ash cloud delineation, applicable in any area of the world and becomes a useful tool in risk management.

Estimación de áreas de cultivo mediante Deep Learning y programación convencional

Artificial Intelligence has enabled the implementation of more accurate and efficient solutions to problems in various areas. In the agricultural sector, one of the main needs is to know at all times the extent of land occupied or not by crops in order to improve production and profitability. The traditional methods of calculation demand the collection of data manually and in person in the field, causing high labor costs, execution times, and inaccuracy in the results. The present work proposes a new method based on Deep Learning techniques complemented with conventional programming for the determination of the area of populated and unpopulated crop areas. We have considered as a case study one of the most recognized companies in the planting and harvesting of sugar cane in Ecuador. The strategy combines a Generative Adversarial Neural Network (GAN) that is trained on a dataset of aerial photographs of natural and urban landscapes to improve image resolution; a Convolutional Neural Network (CNN) trained on a dataset of aerial photographs of sugar cane plots to distinguish populated or unpopulated crop areas; and a standard image processing module for the calculation of areas in a percentage manner. The experiments performed demonstrate a significant improvement in the quality of the aerial photographs as well as a remarkable differentiation between populated and unpopulated crop areas, consequently, a more accurate result of cultivated and uncultivated areas. The proposed method can be extended to the detection of possible pests, areas of weed vegetation, dynamic crop development, and both qualitative and quantitative quality control.

A Large Visual, Qualitative and Quantitative Dataset of Web Pages

The World Wide Web is not only one of the most important platforms of communication and information at present, but also an area of growing interest for scientific research. This motivates a lot of work and projects that require large amounts of data. However, there is no dataset that integrates the parameters and visual appearance of Web pages, because its collection is a costly task in terms of time and effort. With the support of various computer tools and programming scripts, we have created a large dataset of 49,438 Web pages. It consists of visual, textual and numerical data types, includes all countries worldwide, and considers a broad range of topics such as art, entertainment, economy, business, education, government, news, media, science, and environment, covering different cultural characteristics and varied design preferences. In this paper, we describe the process of collecting, debugging and publishing the final product, which is freely available. To demonstrate the usefulness of our dataset, we expose a binary classification model for detecting error Web pages, and a multi-class Web subject-based categorization, both problems using convolutional neural networks.