  Landslide inventory maps are crucial to validate predictive landslide models;
however, since most mapping methods rely on visual interpretation or expert
knowledge, detailed inventory maps are still lacking. This study used a fully
convolutional deep learning model named U-net to automatically segment
landslides in the city of Nova Friburgo, located in the mountainous range of
Rio de Janeiro, southeastern Brazil. The objective was to evaluate the impact
of patch sizes, sampling methods, and datasets on the overall accuracy of the
models. The training data used the optical information from RapidEye satellite,
and a digital elevation model (DEM) derived from the L-band sensor of the ALOS
satellite. The data was sampled using random and regular grid methods and
patched in three sizes (32x32, 64x64, and 128x128 pixels). The models were
evaluated on two areas with precision, recall, f1-score, and mean intersect
over union (mIoU) metrics. The results show that the models trained with 32x32
tiles tend to have higher recall values due to higher true positive rates;
however, they misclassify more background areas as landslides (false
positives). Models trained with 128x128 tiles usually achieve higher precision
values because they make less false positive errors. In both test areas, DEM
and augmentation increased the accuracy of the models. Random sampling helped
in model generalization. Models trained with 128x128 random tiles from the data
that used the RapidEye image, DEM information, and augmentation achieved the
highest f1-score, 0.55 in test area one, and 0.58 in test area two. The results
achieved in this study are comparable to other fully convolutional models found
in the literature, increasing the knowledge in the area.
