The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI’s predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.