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The first geospatial dataset of irrigated fields (2020-2024) in Vojvodina (Serbia)

  • 0BioSense Institute - the Research and Development Institute for Information Technologies in Biosystems, University of Novi Sad, Novi Sad, Serbia. mirjana.radulovic@biosense.rs.
Scientific Data +

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January 18, 2025

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January 18, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Researchers developed a new dataset for mapping irrigated fields, crucial for food security and water management. This resource aids in creating AI models to automatically detect irrigation using satellite imagery, overcoming data collection challenges.

Area Of Science

  • Agricultural Science
  • Remote Sensing
  • Data Science

Background

  • Irrigated agriculture is vital for global food security and sustainable production.
  • Accurate mapping of irrigated fields supports water management and conservation.
  • High-quality training data for irrigation mapping is costly and labor-intensive to collect.

Purpose Of The Study

  • To create a georeferenced dataset for mapping irrigated fields.
  • To provide essential data (location, crop type, irrigation equipment) for AI model development.
  • To facilitate the automatic detection of irrigated fields using satellite imagery.

Main Methods

  • Compiled a dataset of 1256 agricultural parcels in Vojvodina, Serbia.
  • Collected data on crop type (maize, soybean, sugar beet, wheat) and irrigation equipment presence.
  • Focused on the period 2020-2024 for temporal relevance.

Main Results

  • A comprehensive, georeferenced dataset for irrigation mapping has been established.
  • The dataset includes location, crop type, and irrigation equipment occurrence for key crops.
  • The data spans five years (2020-2024) across a major agricultural region.

Conclusions

  • The created dataset addresses the need for accessible, high-quality training data for irrigation mapping.
  • This resource can be utilized for building and refining machine learning models for automated irrigation detection.
  • The dataset will advance sustainable water management and agricultural practices through improved remote sensing applications.

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