In the domain of machine learning, one persistent challenge is the availability of ample data, especially pertinent to computer vision. Moreover, this challenge is amplified within the realm of remote sensing, where annotations for addressing problems are frequently scarce. This manuscript critically examines the daunting task of monitoring a geophysical phenomenon —landslides— within the Peruvian landscape, a nation profoundly impacted by such events on a global scale. In this paper, we present three contributions in that direction. Our first contribution is to expand a well-known satellite imagery dataset targeting landslides. The nucleus of this foundational dataset originates from Asian territories, comprising 3799 meticulously annotated images. However, recognizing the distinct geospatial dynamics of Peru, we embarked on a rigorous exercise to augment this dataset with 838 local scenarios. These additions maintain congruence with the original dataset in terms of attributes and configuration, thereby ensuring both replicability and scalability for future research endeavours.Our second contribution is an exhaustive assessment of various semantic segmentation models. At the heart of our experimentation lies the U-net architecture, bolstered by the Weighted Cross Entropy + Dice Loss —a loss function acclaimed for its efficacy in segmentation tasks with imbalanced data sets. The empirical findings are illuminating: a rudimentary U-net architecture exhibits a formidable F1-Score of 75.5\%, transcending the benchmark score of 71.65\% set by the original dataset.Our third and final contribution is the comprehensive research framework developed for data acquisition, processing pipeline and model training/evaluation. Given this framework has the potential to drive a general applicability of segmentation systems to landslide monitoring systems, and to have a broader reach to the academic community and governmental stakeholders in Latin America and worldwide, we will be making all scripts and experiment details available upon publication, thus, hoping to foster an environment for collaborative scrutiny, discourse, and further advancement.