@inproceedings{723fbb41f03b488799b1c2f954aa0be4,
title = "Deep learning models to count buildings in high-resolution overhead images",
abstract = "This paper addresses the problem of counting buildings in very high-resolution overhead true color imagery. We study and discuss the relevance of deep-learning based methods to this task. Two architectures and two loss functions are proposed and compared. We show that a model enforcing equivariance to rotations is beneficial for the task of counting in remotely sensed images. We also highlight the importance of robustness to outliers of the loss function when considering remote sensing applications.",
keywords = "counting, Deep learning, equivariance, loss functions, regression, remote sensing",
author = "Sylvain Lobry and Devis Tuia",
year = "2019",
month = aug,
day = "22",
doi = "10.1109/JURSE.2019.8809058",
language = "English",
isbn = "9781728100104",
series = "Joint Urban Remote Sensing Event (JURSE)",
publisher = "IEEE",
booktitle = "2019 Joint Urban Remote Sensing Event, JURSE 2019",
address = "United States",
note = "2019 Joint Urban Remote Sensing Event, JURSE 2019 ; Conference date: 22-05-2019 Through 24-05-2019",
}