Using our custom visualization tool for multitemporal Landsat satellite imagery we discovered, to our knowledge, an undocumented mega-landslide in far-east Siberia, which occurred in summer 2017 (https://bit.ly/2WYRLM1; 61.55°N; 170.01°E). To create and visualize this unique dataset, we processed temporal trends of multispectral indices of >100,000 Landsat images for a period from 2000-2019 using the freely available Google Earth Engine cloud processing platform (https://ingmarnitze.users.earthengine.app/view/hotspottcvisapp). The megaslide has a size of 3.66 km² and using the ArcticDEM data we estimate a volume movement of ~20 Mm³. With this size and volume, the landslide is among the largest globally known in recent decades. The landslide is accompanied by a smaller one (0.31 km², 1 Mm³) about 600 m further east, which already occurred in summer 2015. The large landslide caused the formation of several small lakes by blocking two valleys with debris and within newly formed crevasses near the hilltop, which are still persisting as of August 2021. As this event occurred in a remote valley far from any settlement, no visible damage to infrastructure or human livelihoods was detected. The remoteness has likely led to being not detected, like many similar, albeit a lot smaller, erosion features in the Arctic permafrost region. In this presentation we will show the main properties of the landslide, potential trigger mechanisms in the traditional sense. As this region is located along the fringes of permafrost presence we will discuss its potential connection to the rapidly warming climate in the high latitudes. Further, we will discuss how such a large event remained undetected for several years. We discuss and highlight the value of our landscape change visualization tool based on Landsat trend analysis (see Nitze et al., AGU 2020), which helped us to identify this extreme event. With more and more available data sources, this tool in addition to automated image analysis (e.g. deep-learning) or seismic analysis will help to uncover the hidden processes and dynamics of the Earth’s surface.