Higher spatial/temporal resolution digital imagery and elevation datasets have become easier to acquire. Enhanced resolution DEMs/DTMs, 3D photogrammetric methods, and higher resolution aerial imagery have allowed researchers to map smaller, more subtle slope failures than before, through change-detection methods or enhanced recognition computational modeling. Until recently, visual inspection of data was used to detect mass movements, which introduces bias and human error, making the detection process inherently subjective. Semi-automated/automated computational methods not only reduce the subjective nature of detection and recognition, but can also provide a less biased and more efficient and reliable means of processing and interpreting “big data” - very large, high-resolution datasets. This session focuses on new and innovative methods of semi-automated/automated mass movement detection, recognition, and characterization using a variety of novel datasets and methodologies. We encourage submissions that advance data-collection and/or data-processing methods, and contribute to the enhanced detection, recognition, and characterization of mass movements.