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A robust and scalable crop mapping framework using advanced machine learning and optical and SAR imageries
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DOI：10.1016/j.atech.2025.101354.png)
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Abstract
En 中文
Monitoring agricultural systems is increasingly essential as we address the pressing challenges of climate change, biodiversity loss, population growth, and rising food demands. High-resolution, large-scale maps of agricultural lands are fundamental for creating sustainable strategies but mapping extensive and diverse croplands over time remains complex. To tackle this, our study presents an efficient and reproducible framework for annual crop type mapping using multi-temporal satellite data and deep learning.
Keywords:
Crop classification
Remote sensing
Machine learning models
Agricultural monitoring
Geospatial analysis
Big data fusion
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