  Monitoring land cover using remote sensing is vital for studying
environmental changes and ensuring global food security through crop yield
forecasting. Specifically, multitemporal remote sensing imagery provides
relevant information about the dynamics of a scene, which has proven to lead to
better land cover classification results. Nevertheless, few studies have
benefited from high spatial and temporal resolution data due to the difficulty
of accessing reliable, fine-grained and high-quality annotated samples to
support their hypotheses. Therefore, we introduce a temporal patch-based
dataset of Canadian croplands, enriched with labels retrieved from the Canadian
Annual Crop Inventory. The dataset contains 78,536 manually verified
high-resolution (10 m/pixel, 640 x 640 m) geo-referenced images from 10 crop
classes collected over four crop production years (2017-2020) and five months
(June-October). Each instance contains 12 spectral bands, an RGB image, and
additional vegetation index bands. Individually, each category contains at
least 4,800 images. Moreover, as a benchmark, we provide models and source code
that allow a user to predict the crop class using a single image (ResNet,
DenseNet, EfficientNet) or a sequence of images (LRCN, 3D-CNN) from the same
location. In perspective, we expect this evolving dataset to propel the
creation of robust agro-environmental models that can accelerate the
comprehension of complex agricultural regions by providing accurate and
continuous monitoring of land cover.
