Landslides are the fourth most common natural disasters in the world, with Costa Rica and southern Mexico being the most affected regions of Central America (Froude and Petley, 2018). In this work, we propose a semi-automated method to detect earthquake-triggered landslides for rapid mapping after a disaster event using open Sentinel-1 data. We used high-resolution TerraSAR-X data and very high-resolution Spot-7 images to compare and evaluate the accuracy of landslide distribution maps generated from the semi-automated method, applied to the M 7.1 earthquake on June 23, 2017, in Oaxaca, Mexico. The outcomes showed better accuracy in descending orbits due to ‘windward-leeward’ physiographic conditions, with a 50.56% quality percentage. This shows a reasonably good capacity to detect co-seismic landslides. However, the breaching factor was also high because several features, such as bare soils and agricultural areas, were incorrectly identified as co-seismic landslides. Finally, this semi-automated method establishes a basis for future improvements in methodologies applied to construct rapid mapping inventories using medium SAR scales.