IrrNet: Advancing Irrigation Mapping with Incremental Patch Size
  Training on Remote Sensing Imagery
Oishee Bintey Hoque Samarth Swarup Abhijin Adiga Sayjro Kossi Nouwakpo Madhav Marathe
Irrigation mapping plays a crucial role in effective water management,
essential for preserving both water quality and quantity, and is key to
mitigating the global issue of water scarcity. The complexity of agricultural
fields, adorned with diverse irrigation practices, especially when multiple
systems coexist in close quarters, poses a unique challenge. This complexity is
further compounded by the nature of Landsat's remote sensing data, where each
pixel is rich with densely packed information, complicating the task of
accurate irrigation mapping. In this study, we introduce an innovative approach
that employs a progressive training method, which strategically increases patch
sizes throughout the training process, utilizing datasets from Landsat 5 and 7,
labeled with the WRLU dataset for precise labeling. This initial focus allows
the model to capture detailed features, progressively shifting to broader, more
general features as the patch size enlarges. Remarkably, our method enhances
the performance of existing state-of-the-art models by approximately 20%.
Furthermore, our analysis delves into the significance of incorporating various
spectral bands into the model, assessing their impact on performance. The
findings reveal that additional bands are instrumental in enabling the model to
discern finer details more effectively. This work sets a new standard for
leveraging remote sensing imagery in irrigation mapping.