  Landslides are destructive and recurrent natural disasters on steep slopes
and represent a risk to lives and properties. Knowledge of relict landslides
location is vital to understand their mechanisms, update inventory maps and
improve risk assessment. However, relict landslide mapping is complex in
tropical regions covered with rainforest vegetation. A new CNN framework is
proposed for semi-automatic detection of relict landslides, which uses a
dataset generated by a k-means clustering algorithm and has a pre-training
step. The weights computed in the pre-training are used to fine-tune the CNN
training process. A comparison between the proposed and the standard framework
is performed using CBERS-04A WPM images. Three CNNs for semantic segmentation
are used (Unet, FPN, Linknet) with two augmented datasets. A total of 42
combinations of CNNs are tested. Values of precision and recall were very
similar between the combinations tested. Recall was higher than 75% for every
combination, but precision values were usually smaller than 20%. False
positives (FP) samples were addressed as the cause for these low precision
values. Predictions of the proposed framework were more accurate and correctly
detected more landslides. This work demonstrates that there are limitations for
detecting relict landslides in areas covered with rainforest, mainly related to
similarities between the spectral response of pastures and deforested areas
with Gleichenella sp. ferns, commonly used as an indicator of landslide scars.
