I’ve got a paper accepted to the IEEE Geoscience and Remote Sensing Letters. In this paper, we show that freely available crowdsourcing data can be used to accurately (97.11%) train robust deep learning models to perform satellite crosswalk classification on a global scale. Check out the GitHub repository.
In the beginning of this year, we had a paper accepted at IJCNN 2017 for oral presentation. In this project, I had the opportunity to work in collaboration with another lab (NINFA) in a real-world problem from the largest power company of our state. We had access to an amazing database of almost 1,000,000 people, and our goal was to improve the accuracy in the forecast of monthly energy consumption. More details about our deep learning approach can be found in the paper.