  Current datasets for vehicular applications are mostly collected in North
America or Europe. Models trained or evaluated on these datasets might suffer
from geographical bias when deployed in other regions. Specifically, for scene
classification, a highway in a Latin American country differs drastically from
an Autobahn, for example, both in design and maintenance levels. We propose
VWise, a novel benchmark for road-type classification and scene classification
tasks, in addition to tasks focused on external contexts related to vehicular
applications in LatAm. We collected over 520 video clips covering diverse urban
and rural environments across Latin American countries, annotated with six
classes of road types. We also evaluated several state-of-the-art
classification models in baseline experiments, obtaining over 84% accuracy.
With this dataset, we aim to enhance research on vehicular tasks in Latin
America.
