A Comprehensive Review of Agricultural Parcel and Boundary Delineation From Remote Sensing Images: Recent progress and future perspectives

Recent advances in diverse remote sensing sensors have enabled the acquisition of high-spatial-resolution imagery, offering unprecedented opportunities for cost-efficient and accurate agricultural inventory and analysis in an automated manner. 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 article 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. We conceptualize existing APBD studies as comprising three hierarchical levels: cropland identification (CI), boundary delineation (BD), and parcel segmentation (PS). 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 metadata analysis, including the algorithm, the study site, the crop type, the sensor type, the evaluation method, and so on. 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 forests (RFs) and decision trees (DTs)], and 3) deep learning-based methods. With deep learning-oriented approaches contributing to a majority, we further discuss deep learning-based APBD methods, organizing them primarily by task into semantic segmentation-based and object detection-based approaches and, within each, distinguishing architectures based on convolutional neural networks (CNNs) and transformers. In addition, we discuss several APBD-related issues to further comprehend the APBD domain using remote sensing data, such as multisensor data in APBD tasks, comparisons between single-task learning and multitask learning in the APBD domain, comparisons among different algorithms and different APBD tasks, and so on. Finally, this article proposes some APBD-related applications and a few exciting prospects and potential hot topics in future APBD research. We hope this review helps researchers involved in the APBD domain to keep track of developments and trends.