@conference{10902/24386,
year = {2021},
url = {http://hdl.handle.net/10902/24386},
abstract = {Automated crop identification tools are of interest to a wide range of applications related to the environment and agriculture including the monitoring of related policies such as the European Common Agriculture Policy. In this context, this work presents a parcel-based crop classification system which leverages on 1D convolutional neural network supervised learning capacity. For the training and evaluation of the model, we employ open and free data: (i) time series of Sentinel-2 optical data selected to cover the crop season of one year, and (ii) a cadastre-derived database providing detailed delineation of parcels. By considering the most dominant crop types and the temporal features of the optical data, the proposed lightweight approach discriminates a considerable number of crops with high accuracy.},
publisher = {Institute of Electrical and Electronics Engineers, Inc.},
publisher = {IEEE International Geoscience and Remote Sensing Symposium (IGARSS 2021), Brussels, Belgium, 2021, 6500-6503},
title = {Crop classification from Sentinel-2 time series with temporal convolutional neural networks},
author = {Pérez Carabaza, Sara and Syrris, Vasileios and Kempeneers, Pieter and Soille, Pierre},
}