To train our CNN we need labelled data of forest paths. Most data online however is meant for autnomous driving, usually on solid (asphalt/compacted gravel) roads/paths.
One (near) exception to this is the Freiburg deepscene forest dataset. While this dataset still mostly contains compacted gravel park roads, it also contains a lot of smaller soft paths.
As such we adapted and used it for the training of our network.
See also Dataprep > Datasets > Freiburg.
Another excellent dataset of forest paths is the data set from Giusti et al.’s A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots.
This data however is unfortunately only labelled as “left”, “straight” or “center”.
See also Dataprep > Datasets > Giusti.
We also collected our own data in a forest in flanders. See Dataprep > Datasets > Steentjes for more info.
Crowd Sourced Labelling¶
To label the Giusti and our own data we employed the help of our fellow students in a crowd sourced labelling effort.
Our Django Labeller instance
Instructional document to coordinate labelling