The Application of Artificial Intelligence and Deep Learning in Extracting Agricultural Parcel Boundaries and Its Role in Enhancing Spatial Data Infrastructure (SDI)

Document Type : Original Article

Authors

Faculty of Civil, Water, and Environmental Engineering, Shahid Beheshti University, Tehran 1983969411, Iran

Abstract

Accurate delineation of agricultural land parcels is a key requirement for implementing precision agriculture, natural resource management, and the development of Spatial Data Infrastructures (SDI). Considering the diversity of planting patterns, vegetation changes, and challenges such as occlusions and manual interpretation methods lack sufficient efficacy. In recent years, deep learning models have emerged as innovative solutions for extracting parcel boundaries from satellite imagery and spatial data due to their high capability in extracting complex features and processing large-scale data. This study aims to investigate the role of deep learning models in accurately delineating agricultural land parcel boundaries based on the analysis of satellite images and spatial data within the framework of Spatial Data Infrastructure (SDI). The study focuses on analyzing various deep learning architectures, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs). These models are evaluated from the perspectives of technical structure, input data types, performance metrics, and adaptability to operational challenges in precision agriculture. For comparative analysis, a set of recent studies and selected models is reviewed regarding accuracy, limitations, and compatibility with real-world conditions such as heterogeneous landscapes and scarce labeled data. The results indicate that CNN-based models perform well in processing satellite imagery but have limitations in capturing contextual dependencies, which can be improved by combining them with RNNs. Additionally, GAN models are effective in augmenting training data and generating synthetic images. The findings of this study can serve as a foundation for developing more intelligent and hybrid models in future SDI and smart agriculture systems.

Keywords

References

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