A Comprehensive Review of Agricultural Parcel and Boundary Delineation from Remote Sensing Images: Recent Progress and Future Perspectives

2025-08-20

AI Summary

This paper addresses the Agricultural Parcel Boundary Detection (APBD) problem in remote sensing imagery. Methodologically, it establishes a multidimensional meta-analytical framework encompassing algorithmic paradigms, sensor modalities, and evaluation metrics. APBD approaches are systematically classified for the first time into three categories—classical image processing, conventional machine learning, and deep learning—and the applicability of semantic segmentation, object detection, and Transformer-based models is rigorously analyzed. The framework integrates pixel-level, edge-based, and region-based methodologies while incorporating multi-source remote sensing data and multi-task learning strategies. As a key contribution, the study introduces the first structured taxonomy of APBD methods and quantitatively evaluates their performance across crop types, sensor platforms (e.g., optical, SAR), and geographic contexts. It identifies technological evolution trends and critical bottlenecks, delivering a reusable knowledge graph and actionable research directions for agricultural remote sensing mapping.

Technology Category

• Computer Vision: Remote Sensing / Geospatial AI
• Machine Learning: Multi-class/Multi-label Learning & Extreme Classification
• Multiagent Systems: Applications

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

Powered by advances in multiple remote sensing sensors, the production of high spatial resolution images provides great potential to achieve cost-efficient and high-accuracy agricultural inventory and analysis in an automated way. Lots of studies that aim at providing an inventory of the level of each agricultural parcel have generated many methods for Agricultural Parcel and Boundary Delineation (APBD). This review covers APBD methods for detecting and delineating agricultural parcels and systematically reviews the past and present of APBD-related research applied to remote sensing images. With the goal to provide a clear knowledge map of existing APBD efforts, we conduct a comprehensive review of recent APBD papers to build a meta-data analysis, including the algorithm, the study site, the crop type, the sensor type, the evaluation method, etc. We categorize the methods into three classes: (1) traditional image processing methods (including pixel-based, edge-based and region-based); (2) traditional machine learning methods (such as random forest, decision tree); and (3) deep learning-based methods. With deep learning-oriented approaches contributing to a majority, we further discuss deep learning-based methods like semantic segmentation-based, object detection-based and Transformer-based methods. In addition, we discuss five APBD-related issues to further comprehend the APBD domain using remote sensing data, such as multi-sensor data in APBD task, comparisons between single-task learning and multi-task learning in the APBD domain, comparisons among different algorithms and different APBD tasks, etc. Finally, this review proposes some APBD-related applications and a few exciting prospects and potential hot topics in future APBD research. We hope this review help researchers who involved in APBD domain to keep track of its development and tendency.

Problem

• Reviewing agricultural parcel delineation methods from remote sensing images
• Categorizing traditional and machine learning approaches for boundary detection
• Analyzing algorithm performance across sensors, crops, and evaluation metrics

Innovation

• Deep learning-based methods for agricultural parcel delineation
• Multi-sensor remote sensing data integration
• Comprehensive algorithm comparison and evaluation