  High-resolution satellite imagery have been increasingly used on remote
sensing classification problems. One of the main factors is the availability of
this kind of data. Even though, very little effort has been placed on the zebra
crossing classification problem. In this letter, crowdsourcing systems are
exploited in order to enable the automatic acquisition and annotation of a
large-scale satellite imagery database for crosswalks related tasks. Then, this
dataset is used to train deep-learning-based models in order to accurately
classify satellite images that contains or not zebra crossings. A novel dataset
with more than 240,000 images from 3 continents, 9 countries and more than 20
cities was used in the experiments. Experimental results showed that freely
available crowdsourcing data can be used to accurately (97.11%) train robust
models to perform crosswalk classification on a global scale.
