IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping

We introduce IrrMap, the first large-scale dataset (1.1 million patches) for irrigation method mapping across regions. IrrMap consists of multi-resolution satellite imagery from LandSat and Sentinel, along with key auxiliary data such as crop type, land use, and vegetation indices. The dataset spans 1,687,899 farms and 14,117,330 acres across multiple western U.S. states from 2013 to 2023, providing a rich and diverse foundation for irrigation analysis and ensuring geospatial alignment and quality control. The dataset is ML-ready, with standardized 224x224 GeoTIFF patches, the multiple input modalities, carefully chosen train-test-split data, and accompanying dataloaders for seamless deep learning model training andbenchmarking in irrigation mapping. The dataset is also accompanied by a complete pipeline for dataset generation, enabling researchers to extend IrrMap to new regions for irrigation data collection or adapt it with minimal effort for other similar applications in agricultural and geospatial analysis. We also analyze the irrigation method distribution across crop groups, spatial irrigation patterns (using Shannon diversity indices), and irrigated area variations for both LandSat and Sentinel, providing insights into regional and resolution-based differences. To promote further exploration, we openly release IrrMap, along with the derived datasets, benchmark models, and pipeline code, through a GitHub repository:this https URLand Data repository:this https URL, providing comprehensive documentation and implementation details.

@article{mandal2025_2505.08273,
  title={ IrrMap: A Large-Scale Comprehensive Dataset for Irrigation Method Mapping },
  author={ Nibir Chandra Mandal and Oishee Bintey Hoque and Abhijin Adiga and Samarth Swarup and Mandy Wilson and Lu Feng and Yangfeng Ji and Miaomiao Zhang and Geoffrey Fox and Madhav Marathe },
  journal={arXiv preprint arXiv:2505.08273},
  year={ 2025 }
}