Landslides are events that cause great impact in different parts of the world. Their destructive capacity generates loss of life and considerable economic damage. In this research, several Machine Learning (ML) methods were explored to select the most important conditioning factors, in order to evaluate the susceptibility to rotational landslides in a sector surrounding the city of Cuenca (Ecuador) and with them to elaborate landslide susceptibility maps (LSM) by means of ML. The methods implemented to analyze the importance of the conditioning factors checked for multicollinearity (correlation analysis and VIF), and, with an ML-based approach called feature selection, the most important factors were determined based on Classification and Regression Trees (CART), Feature Selection with Random Forests (FS RF), and Boruta and Recursive Feature Elimination (RFE) algorithms. LSMs were implemented with Random Forests (RF) and eXtreme Gradient Boosting (XGBoost) methods considering a landslide inventory updated to 2019 and 15 available conditioning factors (topographic (10), land cover (3), hydrological (1), and geological (1)), from which, based on the results of the aforementioned analyses, the six most important were chosen. The LSM were elaborated considering all available factors and the six most important ones, with the previously mentioned ML methods, and were compared with the result generated by an Artificial Neural Network with resilient backpropagation (ANN rprop-) with six conditioning factors. The results obtained were validated by means of AUC-ROC value and showed a good predictive capacity for all cases, highlighting those obtained with XGBoost, which, in addition to a high AUC value (>0.84), obtained a good degree of coincidence of landslides at high and very high susceptibility levels (>72%). Despite the findings of this research, it is necessary to study in depth the methods applied for the development of future research that will contribute to developing a preventive approach in the study area.

Natural hazards generate disasters and huge losses in several aspects, with landslides being one of the natural risks that have caused great impacts worldwide. The aim of this research was to explore a method based on machine learning to evaluate susceptibility to rotational landslides in an area near Cuenca city, Ecuador, which has a high incidence of these phenomena, mainly due to its environmental conditions, and in which, however, such studies are scarce. The implemented method consisted of an artificial neural network multilayer perceptron (ANN MLP), generated with the neuralnet R package, with which, by means of different backpropagation algorithms (RPROP+, RPROP−, SLR, SAG, and Backprop), five landslide susceptibility maps (LSMs) were generated for the study area. A landslide inventory updated to 2019 and 10 conditioning factors, mainly topographical, geological, land cover, and hydrological, were considered. The results obtained, which were validated through the AUC-ROC value and statistical parameters of precision, recall, accuracy, and F-Score, showed a good degree of adjustment and an acceptable predictive capacity. The resulting maps showed that the area has mostly sectors of moderate, high, and very high susceptibility, whose landslide occurrence percentages vary between approximately 63% and 80%. In this research, different variants of the backpropagation algorithm were implemented to verify which one gave the best results. With the implementation of additional methodologies and correct zoning, future analyses could be developed, contributing to adequate territorial planning and better disaster risk management in the area.

Landslide occurrence in Colombia is very frequent due to its geographical location in the Andean mountain range, with a very pronounced orography, a significant geological complexity and an outstanding climatic variability. More specifically, the study area around the Bogotá-Villavicencio road in the central sector of the Eastern Cordillera is one of the regions with the highest concentration of phenomena, which makes its study a priority. An inventory and detailed analysis of 2506 landslides has been carried out, in which five basic typologies have been differentiated: avalanches, debris flows, slides, earth flows and creeping areas. Debris avalanches and debris flows occur mainly in metamorphic materials (phyllites, schists and quartz-sandstones), areas with sparse vegetation, steep slopes and lower sections of hillslopes; meanwhile, slides, earth flows and creep occur in Cretaceous lutites, crop/grass lands, medium and low slopes and lower-middle sections of the hillslopes. Based on this analysis, landslide susceptibility models have been made for the different typologies and with different methods (matrix, discriminant analysis, random forest and neural networks) and input factors. The results are generally quite good, with average AUC-ROC values above 0.7–0.8, and the machine learning methods are the most appropriate, especially random forest, with a selected number of factors (between 6 and 8). The degree of fit (DF) usually shows relative errors lower than 5% and success higher than 90%. Finally, an integrated landslide susceptibility map (LSM) has been made for shallower and deeper types of movements. All the LSM show a clear zonation as a consequence of the geological control of the susceptibility.

The management and identification of forest species in a city are essential tasks for current administrations, particularly in planning urban green spaces. However, the cost and time required are typically high. This study evaluates the potential of RGB point clouds captured by unnamed aerial vehicles (UAVs) for automating tree species classification. A dataset of 809 trees (crowns) for eight species was analyzed using a random forest classifier and deep learning with PointNet and PointNet++. In the first case, eleven variables such as the normalized red–blue difference index (NRBDI), intensity, brightness (BI), Green Leaf Index (GLI), points density (normalized), and height (maximum and percentiles 10, 50, and 90), produced the highest reliability values, with an overall accuracy of 0.70 and a Kappa index of 0.65. In the second case, the PointNet model had an overall accuracy of 0.62, and 0.64 with PointNet++; using the features Z, red, green, blue, NRBDI, intensity, and BI. Likewise, there was a high accuracy in the identification of the species Populus alba L., and Melaleuca armillaris (Sol. ex Gaertn.) Sm. This work contributes to a cost-effective workflow for urban tree monitoring using UAV data, comparing classical machine learning with deep learning approaches and analyzing the trade-offs.

Building and updating tree inventories is a challenging task for city administrators, requiring significant costs and the expertise of tree identification specialists. In Ecuador, only the Trees Inventory of Cuenca (TIC) contains this information, geolocated and integrated with the taxonomy, origin, leaf, and crown structure, phenological problems, and tree images taken with smartphones of each tree. From this dataset, we selected the fourteen classes with the most information and used the images to train a model, using a Transfer Learning approach, that could be deployed on mobile devices. Our results showed that the model based on ResNet V2 101 performed best, achieving an accuracy of 0.83 and kappa of 0.81 using the TensorFlow Lite interpreter, performing better results using the original model, with an accuracy and kappa of 0.912 and 0.905, respectively. The classes with the best performance were Ramo de novia, Sauce, and Cepillo blanco, which had the highest values of Precision, Recall, and F1-Score. The classes Eucalipto, Capuli, and Urapan were the most difficult to classify. Our study provides a model that can be deployed on Android smartphones, being the beginning of future implementations.

Globally, there is a significant trend in the loss of native forests, including those of the Polylepis genus, which are essential for soil conservation across the Andes Mountain range. These forests play a critical role in regulating water flow, promoting soil regeneration, and retaining essential nutrients and sediments, thereby contributing to the soil conservation of the region. In Ecuador, these forests are often fragmented and isolated in areas of high cloud cover, making it difficult to use remote sensing and spectral vegetation indices to detect this forest species. This study developed twelve scenarios using medium- and high-resolution satellite data, integrating datasets such as Sentinel-2 and PlanetScope (optical), Sentinel-1 (radar), and the Sigtierras project topographic data. The scenarios were categorized into two groups: SC1–SC6, combining 5 m resolution data, and SC7–SC12, combining 10 m resolution data. Additionally, each scenario was tested with two target types: multiclass (distinguishing Polylepis stands, native forest, Pine, Shrub vegetation, and other classes) and binary (distinguishing Polylepis from non-Polylepis). The Recursive Feature Elimination technique was employed to identify the most effective variables for each scenario. This process reduced the number of variables by selecting those with high importance according to a Random Forest model, using accuracy and Kappa values as criteria. Finally, the scenario that presented the highest reliability was SC10 (Sentinel-2 and Topography) with a pixel size of 10 m in a multiclass target, achieving an accuracy of 0.91 and a Kappa coefficient of 0.80. For the Polylepis class, the User Accuracy and Producer Accuracy were 0.90 and 0.89, respectively. The findings confirm that, despite the limited area of the Polylepis stands, integrating topographic and spectral variables at a 10 m pixel resolution improves detection accuracy.