The increasing global trend in reported disasters and economic damage shows that the most adversely affected is the Asia-Pacific region, especially developing countries like the Philippines. The Philippines is located in the Circum-Pacific Ring of Fire, a volcanically and seismically active zone. It lies in the Western North Pacific Basin, where tropical cyclogenesis is most active. While volcanic eruptions and earthquakes have long recurrence intervals (ranging from decades to centuries), rainfall induced landslides (RIL) and the damage they cause are dealt with almost every month of the rainy season. Locally and globally, the northern Philippines is among the most landslide prone and is among the most adversely affected.<br/> In order to foster community resilience, more landslide-related science-based information over space and time is essential. This study investigated the timing and impact of RIL, the amount of 24-hour and antecedent rainfall associated with RIL, and the weather conditions that enhance landslide triggering rainfall (LTR). The mountainous region of the Baguio district, where the highest 24-hour rainfall has been recorded, was chosen as the area of study. A threshold for LTR as basis for early warning was then established. The results showed that early warning for landslides may be based on one or a combination of the following: (1) 24-hour rainfall of 70 mm, (2) intensity (I) – duration (D) equation: I = 6.46 D -0.28, (3) normalized ID equation: NI = 0.002 D -0.28, (4) 24-hour rainfall that is 0.02%-28% of the mean annual precipitation, and (5) antecedent rainfall of 500 mm over a 60-day period. During a tropical cyclone event, the knowledge of accumulated rainfall can provide immediate and real-time information to signal needed action, e.g. mobilization of emergency crews, road closure, work suspension and evacuation of those at highest risk.<br/> The study constructed a landslide inventory from high resolution satellite imagery (HRSI), field observations and local knowledge in the southern area of the municipality of Tublay in Benguet province, some 20 kilometers north of Baguio city. Utilizing remote sensing and GIS software, a semi-automated method combined with a manual method was adopted to highlight 853 landslides, most of which were slides and debris flows.<br/> With available satellite imagery and access to remote sensing, GIS and statistical software, robust estimates of landslide susceptibility were generated in a process that is expeditious, straightforward, evidence-based and cost-effective. A methodology for estimating attributes of selected landslide-conditioning factors and modelling landslide susceptibility was developed. The bivariate and multivariate statistical methods of frequency ratio and binary logistic regression, respectively, were applied. A five-fold cross-validation approach in the application of the frequency ratio method demonstrated that the five factors most closely associated with RIL were NDVI < 0.38, slope is 50-60 degrees, elevation is 1800-2000 meters, aspect is south and distance to drainage is >500 m. The landslide susceptibility models that were generated using DEM, scanned maps, and HRSI factor sets separately and in combination showed consistent results. The combination of the HRSI factor set with the DEM or scanned map factor sets improved model performance significantly. The landslide susceptibility models using all factor sets provided the best results. The average success and prediction rates were 90% and 89%, respectively.<br/> The effect of training and validation data size was investigated in the application of the binary logistic regression. The training–validation proportions were 80%-20%, 60%-40%, 40%-60% and 20%-80%. Ten sample data sets in each proportion group were examined. Based on the coefficients obtained, the factors NDVI, LULC, aspect, lithology and slope showed strong influence on landslide occurrence. The factors plan curvature, distance to fault/lineament and distance to road were also important contributors to the models generated. Training and validation accuracy, ranging 89%-95% and 84%-95%, respectively, were best obtained using the 80%-20% data proportion. Validation accuracy diminished with decreasing training data size.<br/> The use of cross-validation and multiple training and validation data sets confirmed the model consistency and generated robust results. They are thus advocated in future assessments of landslide susceptibility. The landslide susceptibility maps could serve as reference for identifying no-build, high maintenance and safe zones, complementing information from the available landslide hazard maps (1:50000 and 1:10000) of national government agencies and community stakeholders. The methodology and data products could be refined and replicated in similar landslide-prone regions. It is hoped that the findings reported would contribute to ongoing efforts towards building a more landslide disaster resilient community.