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; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 5460-5469

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.