# IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagery

arXiv: https://arxiv.org/abs/2404.11762 · HTML: https://mlsanity.com/paper/2404.11762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 54 references that are in the map.
- First posted on arXiv 17 Apr 2024.

## Builds on (most cited first, 22 of 22)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [Progressive Growing of GANs for Improved Quality, Stability, and Variation](https://mlsanity.com/paper/1710.10196/) (2017)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation](https://mlsanity.com/paper/1606.02147/) (2016)
- [ICNet for Real-Time Semantic Segmentation on High-Resolution Images](https://mlsanity.com/paper/1704.08545/) (2017)
- [Fixing the train-test resolution discrepancy](https://mlsanity.com/paper/1906.06423/) (2019)
- [FlexiViT: One Model for All Patch Sizes](https://mlsanity.com/paper/2212.08013/) (2022)
- [ESPNet: Efficient Spatial Pyramid of Dilated Convolutions for Semantic Segmentation](https://mlsanity.com/paper/1803.06815/) (2018)
- [ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network](https://mlsanity.com/paper/1811.11431/) (2018)
- [TorchGeo: Deep Learning With Geospatial Data](https://mlsanity.com/paper/2111.08872/) (2021)
- [You Only Segment Once: Towards Real-Time Panoptic Segmentation](https://mlsanity.com/paper/2303.14651/) (2023)
- [Learning scale-variant and scale-invariant features for deep image classification](https://mlsanity.com/paper/1602.01255/) (2016)
- [Fast Panoptic Segmentation Network](https://mlsanity.com/paper/1910.03892/) (2019)
- [Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency](https://mlsanity.com/paper/1908.08986/) (2019)
- [GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates](https://mlsanity.com/paper/2304.07750/) (2023)
- [Anomaly Segmentation for High-Resolution Remote Sensing Images Based on Pixel Descriptors](https://mlsanity.com/paper/2301.13422/) (2023)

## Built on by (most cited first, 1 of 1)

- [Knowledge-Informed Deep Learning for Irrigation Type Mapping from Remote Sensing](https://mlsanity.com/paper/2505.08302/) (2025)

## Abstract (arXiv)

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.