  Mapping structures such as settlements, roads, individual houses and any
other types of artificial structures is of great importance for the analysis of
urban growth, masking, image alignment and, especially in the studied use case,
the definition of Fuel Management Networks (FGC), which protect buildings from
forest fires. Current cartography has a low generation frequency and their
resolution may not be suitable for extracting small structures such as small
settlements or roads, which may lack forest fire protection. In this paper, we
use time series data, extracted from Sentinel-1 and 2 constellations, over
Santar\'em, Ma\c{c}\~ao, to explore the detection of permanent structures at a
resolution of 10 by 10 meters. For this purpose, a XGBoost classification model
is trained with 133 attributes extracted from the time series from all the
bands, including normalized radiometric indices. The results show that the use
of time series data increases the accuracy of the extraction of permanent
structures when compared using only static data, using multitemporal data also
increases the number of detected roads. In general, the final result has a
permanent structure mapping with a higher resolution than state of the art
settlement maps, small structures and roads are also more accurately
represented. Regarding the use case, by using our final map for the creation of
FGC it is possible to simplify and accelerate the process of delimitation of
the official FGC.
