Deep learning solutions for mapping contour levee rice production systems from very high resolution imagery.

The construction of contour levees for rice irrigation represents a major landscape management activity with impacts on irrigation water use efficiency, crop management decisions, and food production. However, levee distribution information traditionally relies on local field surveys because remote sensing approaches are complicated by irregular spacing, shape, and landscape variability within the field. In this paper the authors develop a deep learning approach capable of identifying rice fields with contour style levee irrigation practices from open-source aerial imagery. To generate a levee-identification scheme, a hybrid ResNet/Unet model is built from the commonly known Residual Network (ResNet) architecture for multi-layer deep learning strategies. The model takes a 320 × 320 RGB aerial landscape image from the US National Agricultural Imagery Program as input along with label data to then generate a probability map of the distribution of farm fields that use contour levees within the image. In performing this task, the model generates a 0.991 receiver operating characteristic curve score. The model continues to perform well under the introduction of clouds, data augmentation, or minor reductions in spatial resolution. Throughout these tests, the model performed within 0.2 of its original score, except for when the image quality was reduced to 60 m wherein the model score dropped to 0.691. Via these tests the model demonstrates potential to function well given different spatial extents or potential satellite remote sensing with moderate (10 m) resolutions. This model provides a proof-of-concept for the use of aerial imagery and a deep learning strategy for irrigation-type mapping practices. • Contour levee identification method using aerial images yields ROC score of 0.991. • A ResNet/UNet hybrid provides ample performance for remote sensing applications. • The method is insensitive to simulated cloud cover up to 40% opacity. • Performance degrades with a decrease in resolution from 1 m to less than 10 m. • The introduction of dense static noise could present a challenge for the model. [ABSTRACT FROM AUTHOR]