  Monitoring dam failures using satellite images provides first responders with
efficient management of early interventions. It is also equally important to
monitor spatial and temporal changes in the inundation area to track the
post-disaster recovery. On January 25th, 2019, the tailings dam of the
C\'orrego do Feij\~ao iron ore mine, located in Brumadinho, Brazil, collapsed.
This disaster caused more than 230 fatalities and 30 missing people leading to
damage on the order of multiple billions of dollars. This study uses Sentinel-2
satellite images to map the inundation area and assess and delineate the land
use and land cover impacted by the dam failure. The images correspond to data
captures from January 22nd (3 days before), and February 02 (7 days after the
collapse). Satellite images of the region were classified for before and
aftermath of the disaster implementing a machine learning algorithm. In order
to have sufficient land cover types to validate the quality and accuracy of the
algorithm, 7 classes were defined: mine, forest, build-up, river, agricultural,
clear water, and grassland. The developed classification algorithm yielded a
high accuracy (99%) for the image before the collapse. This paper determines
land cover impact using two different models, 1) by using the trained network
in the "after" image, and 2) by creating a second network, trained in a subset
of points of the "after" image, and then comparing the land cover results of
the two trained networks. In the first model, applying the trained network to
the "after" image, the accuracy is still high (86%), but lower than using the
second model (98%). This strategy can be applied at a low cost for monitoring
and assessment by using openly available satellite information and, in case of
dam collapse or with a larger budget, higher resolution and faster data can be
obtained by fly-overs on the area of concern.
