The Canadian Cropland Dataset: A New Land Cover Dataset for Multitemporal Deep Learning Classification in Agriculture
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 13 of its 59 references that are in the map. First posted on arXiv 31 May 2023.
Builds on
13 of 59 references.
References · 13
- 2015Deep Residual Learning for Image Recognitioncited by 52,096
- 2016Densely Connected Convolutional Networkscited by 6,720
- 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
- 2017EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classificationcited by 1,496
- 2015Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcastingcited by 1,436
- 2014Long-term Recurrent Convolutional Networks for Visual Recognition and Descriptioncited by 484
- 2018Deep learning in agriculture: A surveycited by 160
- 2017Deep learning in remote sensing: a reviewcited by 128
- 2018Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learningcited by 22
- 2020Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Predictioncited by 18
- 2017DeepUNet: A Deep Fully Convolutional Network for Pixel-level Sea-Land Segmentationcited by 10
- 2020Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)cited by 10
Built on by
Citing papers per year: 2024: 1; 2025: 1.
2 papers.