# Accurate Parcel Extraction Combined with Multi-Resolution Remote Sensing Images Based on SAM
## 1 Introduction  
Cropland constitutes a fundamental resource and is essential for human survival and development. The advent of precision agriculture technologies facilitates personalized management for individual cropland parcels, thereby significantly augmenting their productivity [1]. In this context, the precise delineation of farmland boundaries becomes a critical task in precision agriculture. Currently, farm boundaries are divided into two categories: one is arable land boundaries, which are fixed and considered hard boundaries, such as forest networks, field ridges, and roads. The other category is crop boundaries, which change with the variation in planted crops and are classified as soft boundaries [2]. Traditional farmland mapping methods, which are reliant on ground surveys, are notablytime-intensive and laborious. Currently, satellite observation can provide a vast amount of remote sensing imagery, with medium-to-high-resolution satellite imagery emerging as a primary data source for accurately extracting farmland parcels [3]. On remote sensing images, differences in spectral and textural characteristics exist between farmland boundaries and the interior of farmland, providing theoretical support for the extraction of farmland parcels. Conventional methods of parcel extraction primarily involve characteristic feature thresholds [4], region extraction [5; 6], and edge detection [7; 8]. While these methodologies have yielded certain advancements, their limitations are pronounced. For instance, region-based extraction requires repeated experimentation to determine the optimal threshold. Edge detection is highly sensitive to the type of filters used [9]. Additionally, these algorithms cannot generate closed parcel boundaries, necessitating post-processing for parcel generation [3]. Moreover, as the resolution of remote sensing imagery continues to increase, the complexity and uncertainty render these methods ineffective in obtaining satisfactory results. In recent years, deep learning has made significant advances in feature extraction and image segmentation, providing a new approach for parcel extraction [10; 11]. Researchers deploying deep learning models for this task generally align with two predominant paradigms. The first paradigm approaches parcel extraction from the perspective of edge detection, with the aim of delineating parcel boundaries [12; 13; 14]. The second paradigm describes parcel extraction as an image segmentation problem, directly characterizing boundaries by extracting farmland parcels [15; 16]. Additionally, some studies have developed multi-task deep learning frameworks [17; 18]. These frameworks handle the extraction of parcels and their boundaries at the same time. Then, specific post-processing techniques are used to synthesize these predictive outputs, so as to construct complete parcel representations. At present, deep learning models have demonstrated superior performance compared to traditional parcel extraction methods. However, training such models typically requires large numbers of samples, and building a sample dataset is undoubtedly a time-consuming and labor-intensive task [19]. During the process of annotating samples, the limitations imposed by image clarity and the absence of ground references can easily lead to boundary omission, undoubtedly impacting the effectiveness of the model [20; 21]. Furthermore, due to the limited training data, models trained often only apply to specific regions, or even specific temporal conditions. When applied to different regions or time periods, or when using different sensors, the predictive performance of the model will decrease [22; 23]. In addition, current parcel extraction methods tend to work only with remote sensing imagery of a specific resolution, yet parcel sizes vary across landscapes, necessitating varying resolutions for accurate extraction. In the realm of image segmentation, the Segment Anything Model (SAM), as established by Meta AI, represents a breakthrough in the field.