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Gully erosion is a serious threat to the state of ecosystems all around the world. As a result, safeguarding the soil for our own benefit and from our own actions is a must for guaranteeing the long-term viability of a variety of ecosystem services. As a result, developing gully erosion susceptibility maps (GESM) is both suggested and necessary. In this study, we compared the effectiveness of three hybrid machine learning (ML) algorithms with the bivariate statistical index frequency ratio (FR), named random forest-frequency ratio (RF-FR), support vector machine-frequency ratio (SVM-FR), and naïve Bayes-frequency ratio (NB-FR), in mapping gully erosion in the GHISS watershed in the northern part of Morocco. The models were implemented based on the inventory mapping of a total number of 178 gully erosion points randomly divided into 2 groups (70% of points were used for training the models and 30% of points were used for the validation process), and 12 conditioning variables (i.e., elevation, slope, aspect, plane curvature, topographic moisture index (TWI), stream power index (SPI), precipitation, distance to road, distance to stream, drainage density, land use, and lithology). Using the equal interval reclassification method, the spatial distribution of gully erosion was categorized into five different classes, including very high, high, moderate, low, and very low. Our results showed that the very high susceptibility classes derived using RF-FR, SVM-FR, and NB-FR models covered 25.98%, 22.62%, and 27.10% of the total area, respectively. The area under the receiver (AUC) operating characteristic curve, precision, and accuracy were employed to evaluate the performance of these models. Based on the receiver operating characteristic (ROC), the results showed that the RF-FR achieved the best performance (AUC = 0.91), followed by SVM-FR (AUC = 0.87), and then NB-FR (AUC = 0.82), respectively. Our contribution, in line with the Sustainable Development Goals (SDGs), plays a crucial role for understanding and identifying the issue of "where and why" gully erosion occurs, and hence it can serve as a first pathway to reducing gully erosion in this particular area. [ABSTRACT FROM AUTHOR]

The relevance of local knowledge in cultural heritage is by now acknowledged. It helps to determine many community-based projects by identifying the material to be digitally maintained in multimedia collections provided by communities of volunteers, rather than for-profit businesses or government entities. Considering that the search and browsing of texts, images, video, and 3D models related to places is more essential than using a simple text-based search, an interactive multimedia map was implemented in this study. The map, which is loaded on a single HyperText Markup Language (HTML) page using AJAX (Asynchronous JavaScript and XML), with a client-side control mechanism utilising jQuery components that are both freely available and ad-hoc developed, is updated according to user interaction. To simplify the publication of geo-referenced information, the application stores all the data in a Geographic JavaScript Object Notation (GeoJSON) file rather than in a database. The multimedia contents--associated with the selected Points of Interest (PoIs)--can be selected through text search and list browsing as well as by viewing their previews one by one in a sequence all together in a scrolling window (respectively: "Table", "Folder", and "Tile" functions). PoIs--visualised on the map with multi-shape markers using a set of unambiguous colours--can be filtered through their categories and types, accessibility status and timeline, thus improving the system usability. The map functions are illustrated using data collected in a Comenius project. Notes on the application software and architecture are also presented in this paper. [ABSTRACT FROM AUTHOR]

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments. [ABSTRACT FROM AUTHOR]

"Entering" a canvas to examine and learn about the work from unexplored points of view is an experiential "journey" in an environment reconstructed through the use and integration of innovative technologies, such as descriptive geometry and digital photogrammetry, solid modeling and immersive photography. Generating a "sense of presence" in the viewer means connecting it with immediacy to the artist's message and grasping even the most subtle elements of the painting that are difficult to understand, such as architectural inconsistencies or the play of perspectives that, very often, bring out the situations scripted, characterized by discoveries that prelude to the aesthetic pleasure as the multiplicity of meanings and the "stylistic overcoding" of the work is revealed. The research hypotheses were applied to a case study, or to the splendid "Last Supper" by the Flemish artist Gaspar Hovic, a canvas painted in oil (late 15th century AD) and kept in the Matrice SM Veterana Church di Triggiano (BA), where the representation of the suggestive moment of Jesus with the Apostles is carried out through numerous symbols, in an evocative architectural context rich in details. The pictorial subject provides a series of very interesting ideas suitable for research of the role of perspective. The inverse method of linear perspective was used to reveal the plants and sections corresponding to the perspective space of the painting, used as the basis for the reconstruction of the 3D model of the entire scenic composition. Although the painting represents the apparently rigorous application of the perspective technique, by "entering" the canvas it is possible to observe some exceptions to the geometric rules deliberately introduced by the artist, thus making the perspective restitution process an effective interpretative act of the work. [ABSTRACT FROM AUTHOR]