Understanding actions in videos remains a significant challenge in computer vision, which has been the subject of several pieces of research in the last decades. Convolutional neural networks (CNN) are a significant component of this topic and play a crucial role in the renown of Deep Learning. Inspired by the human vision system, CNN has been applied to visual data exploitation and has solved various challenges in various computer vision tasks and video/image analysis, including action recognition (AR). However, not long ago, along with the achievement of the transformer in natural language processing (NLP), it began to set new trends in vision tasks, which has created a discussion around whether the Vision Transformer models (ViT) will replace CNN in action recognition in video clips. This paper conducts this trending topic in detail, the study of CNN and Transformer for Action Recognition separately and a comparative study of the accuracy-complexity trade-off. Finally, based on the performance analysis’s outcome, the question of whether CNN or Vision Transformers will win the race will be discussed.

Groundwater is one of the important determinants of human health in many regions of Morocco. Indeed, it is one of the government’s top concerns. However, slow and wrong decisions are hindering the advancement of the quality of groundwater in Morocco. The traditional monitoring methods are costly, time-consuming, and human-resource-intensive, especially in such a developing country. An exploratory study was conducted in the Jouamaa Hakama region in Morocco to test and compare groundwater quality using in situ measurements, water quality indices (WQIs), and a fuzzy logic (FL) method. The methodology followed in this study involves using and comparing four WQIs and FL based on in situ measurements at nine different wells along the Ouljat Echatt River downstream of the Chrafate wastewater treatment station. Twelve physical–chemical and bacteriological parameters: potential of hydrogen (pH), temperature (T°), turbidity (TURB), chemical oxygen demand (COD), biochemical oxygen demand in five days (BOD₅), suspended matter (SM), phosphate (PO₄³⁻), nitrite $(N{O}_2^{-}$), nitrate $(N{O}_3^{-}$), ammonium $(N{H}_4^{+}$), dissolved oxygen (DO), electrical conductivity (EC), and fecal coliforms (FC) were measured in 2016 and 2017. The results show that all wells are of poor quality according to FL and WQIs; the Weighted Geometric WQI and Oregon WQI indicate that the groundwater is below the acceptable standard for human consumption, while the Weighted Arithmetic WQI and Logarithmic WQI indicate that the majority of wells are of good quality. These tested indices represent an excellent tool to support decision making and can be highly helpful in monitoring groundwater quality in vulnerable areas such as the Jouamaa Hakama region in the north of Morocco.

Wildfires pose a growing threat to Mediterranean ecosystems. This study employs advanced classification techniques for shrub fractional cover mapping from satellite imagery in a fire-prone landscape in Quinta da França (QF), Portugal. The study area is characterized by fine-grained heterogeneous land cover and a Mediterranean climate. In this type of landscape, shrub encroachment after land abandonment and wildfires constitutes a threat to ecosystem resilience—in particular, by increasing the susceptibility to more frequent and large fires. High-resolution mapping of shrub cover is, therefore, an important contribution to landscape management for fire prevention. Here, a 20 cm resolution land cover map was used to label 10 m Sentinel-2 pixels according to their shrub cover percentage (three categories: 0%, >0%–50%, and >50%) for training and testing. Three distinct algorithms, namely Support Vector Machine (SVM), Artificial Neural Networks (ANNs), and Random Forest (RF), were tested for this purpose. RF excelled, achieving the highest precision (82%–88%), recall (77%–92%), and F1 score (83%–88%) across all categories (test and validation sets) compared to SVM and ANN, demonstrating its superior ability to accurately predict shrub fractional cover. Analysis of confusion matrices revealed RF’s superior ability to accurately predict shrub fractional cover (higher true positives) with fewer misclassifications (lower false positives and false negatives). McNemar’s test indicated statistically significant differences (p value < 0.05) between all models, consolidating RF’s dominance. The development of shrub fractional cover maps and derived map products is anticipated to leverage key information to support landscape management, such as for the assessment of fire hazard and the more effective planning of preventive actions.