# Comparative study of agricultural parcel delineation deep learning methods using satellite images: Validation through parcels complexity

Amine Hadir, Mohamed Adjou, Olga Assainova, Gaëtan Palka, Marwa Elbouz

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Amine Hadir, Mohamed Adjou, Olga Assainova, Gaëtan Palka, Marwa Elbouz. Comparative study of agricultural parcel delineation deep learning methods using satellite images: Validation through parcels complexity. Smart Agricultural Technology, 2025, 10, pp.100833. 10.1016/j.atech.2025.100833. hal-05314895

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# Comparative study of agricultural parcel delineation deep learning methods using satellite images: Validation through parcels complexity

Amine Hadir<sup>a, ID,*</sup>, Mohamed Adjou<sup>a, ID</sup>, Olga Assainova<sup>a</sup>, Gaëtan Palka<sup>b, ID</sup>, Marwa Elbouz<sup>a, ID</sup>

## ARTICLE INFO

**Keywords:**

Deep learning

Remote sensing images

Parcel delineation

Segmentation

Classification

Parcel complexity

## ABSTRACT

Accurate delineation of agricultural parcels is crucial for applications ranging from resource management to policy decisions, with a direct impact on agricultural productivity and sustainability. Parcel delineation is the subject of numerous studies, most of which focus on the development of more efficient methods or ones better adapted to specific cases. In addition, various methods exist in the literature for delineating agricultural fields from satellite images. Deep learning, in particular, has revolutionized the field. Many state-of-the-art methods now utilize deep learning, often incorporating segmentation and classification techniques to define agricultural parcel boundaries. While recent research has led to a surge in deep learning methods for this task, evaluating their effectiveness goes beyond simply comparing outputs. This paper emphasizes the critical role of parcel complexity as a powerful lens for assessing the performance of deep learning methods in agricultural parcel delineation. We categorize 14 evaluation metrics into three main groups, global, boundary, and structure metrics, respectively. Global metrics assess the overall accuracy of the delineated parcels, boundary metrics focus on the precision of the parcel boundaries, and structure metrics examine the topological relationships between the parcels. Our goal is to compare these deep learning methods based on these metrics and their performance across varying levels of parcel complexity. We systematically evaluate nine state-of-the-art methods using a public database, explicitly analyzing how their strengths and weaknesses are affected by different levels of parcel complexity. This approach ensures that future deep learning techniques are robust and accurate enough to meet the demands of accurately defining the agricultural landscape and provides important insights for the development and refinement of future deep learning techniques.

## 1. Introduction

The evolution of remote sensing technology, particularly its enhanced spatial resolution capabilities, has transformed agricultural land management practices worldwide. At the heart of this transformation lies the critical task of accurately delineating and characterizing Cultivated Land Parcels (CLPs), a fundamental component in modern agricultural systems that supports informed decision-making and efficient land management. This technological advancement has become increasingly vital for both economic sustainability and ecological stewardship in agricultural landscapes that vary dramatically in complexity across different global regions.

The diversity in agricultural parcel complexity presents a unique challenge in the field. In Europe, particularly in countries like France, agricultural landscapes are characterized by intricate patterns of frag-

mented parcels, shaped by centuries of inheritance laws and traditional farming practices [1]. This contrasts sharply with regions such as North America or Australia, where industrial farming techniques have led to larger, more consolidated parcels. These regional variations, rooted in historical, legal, and geographical factors, significantly influence the effectiveness of parcel delineation methods and underscore the importance of developing robust, adaptable approaches.

Over recent years, the advancement of agricultural technology has catalyzed both an expansion of agricultural fields and substantial increases in production efficiency. This evolution has elevated the significance of agricultural parcel delineation across multiple critical applications: facilitating land registration procedures, determining agricultural subsidy eligibility, enabling precise monitoring of agricultural land, and supporting environmental impact assessment. The accuracy of these delineation processes has become crucial for both governmental policy im-

<footnote><sup>a</sup> L@bISEN, 20 Rue Cuirassé Bretagne, Brest, 29200, France</footnote>

<footnote><sup>b</sup> CEDETE, Université d'Orléans, Château de la Source, Orléans, 45100, France</footnote>

<footnote>* Corresponding author.
E-mail address: amine.hadir@isen-ouest.yncrea.fr (A. Hadir).</footnote>
plementation and private sector agricultural management, particularly given the varying complexity of agricultural landscapes across different regions.

The challenges in accurate parcel delineation are multifaceted, reflecting the diverse nature of agricultural landscapes worldwide. Beyond the fundamental complexities of irregular shapes, varying sizes, and parcel proximity, regional variations introduce additional layers of complexity. In areas with historically fragmented land ownership, such as in European agricultural regions, the intricate mosaic of small, irregularly shaped parcels poses particular challenges for automated delineation methods. These challenges are further complicated by the varying quality and accessibility of datasets used for boundary extraction across different geographical regions.

The advancement of satellite remote sensing technology has enabled new approaches to agricultural parcel delineation. Research in this area has explored various methods, ranging from classical machine learning techniques like Random Forest (RF) [2], to deep learning approaches such as Convolutional Neural Networks (CNN) [2], UNet [3–5], and ResUNet [6]. More advanced architectures, including domain adaptation methods like Mask R-CNN [7] and SAM (Segment Anything Model) [8], have also been investigated. These methods offer varying degrees of accuracy and applicability, each with its own strengths and limitations in addressing the complexities of agricultural parcel delineation.

Despite these technological advances, a significant research gap persists in understanding which methods are most effective for specific types of agricultural landscapes, particularly given the stark contrasts in parcel complexity between regions. Our research addresses this gap through a comprehensive evaluation of various deep learning methods for agricultural parcel delineation, with particular attention to their performance across different landscape complexities. We introduce a multifaceted evaluation framework that assesses global accuracy, boundary precision, and structural relationships between parcels, using a French dataset that exemplifies high parcel complexity typical of European agricultural landscapes.

This paper is organized to first provide a detailed problem statement and introduce our evaluation study. We then review relevant literature and existing methodologies, followed by a description of the datasets and methodologies employed in our study. The subsequent sections present our results and analysis, discuss the implications of our findings and their relevance to the field, and conclude with suggestions for future research directions.

## 2. Problem statement

The challenge of selecting the most appropriate methodologies for parcel delineation from a wide range of available techniques is a complex task faced by researchers in this field. The effectiveness of these methods can vary significantly based on the specific characteristics of the agricultural landscape and the quality of available data. Therefore, a comprehensive evaluation framework is necessary to assess the performance of different approaches and their suitability for real-world applications.

To address this challenge, we propose a comprehensive evaluation framework based on 14 metrics categorized into three main groups: global metrics, boundary metrics, and structure metrics. Global metrics assess the overall accuracy of the delineated parcels, while boundary metrics focus on the precision of parcel boundaries. Structure metrics delve deeper, examining the topological relationships between parcels, including connectivity, adjacency, and spatial arrangement. This multifaceted approach allows for a nuanced understanding of how different deep learning methods perform across varying levels of parcel complexity.

By applying this study to a range of state-of-the-art techniques, we aim to provide insights that will guide researchers and practitioners in selecting the most appropriate methods for specific agricultural landscapes and data conditions. Our study seeks to answer several key research questions:

How do different deep learning methods compare in their ability to accurately delineate agricultural parcels across various landscape complexities? What are the specific strengths and weaknesses of each method in terms of global accuracy, boundary precision, and structural relationships? How does the performance of these methods vary with different types of input data and agricultural landscape characteristics?

The complexity of agricultural parcels, characterized by factors like irregular shapes, varying sizes, and close proximity to other parcels, poses significant challenges for accurate delineation. These challenges are particularly pronounced in regions with intricate land-use patterns or where parcels are fragmented due to natural features or historical land management practices. By addressing these questions through our comprehensive evaluation study, our research contributes to the ongoing effort to improve agricultural land management through more accurate and efficient parcel delineation techniques.

This study not only aims to evaluate existing methods but also to provide a foundation for future research in the field of agricultural parcel delineation. By identifying the strengths and limitations of current approaches, we hope to inspire the development of more robust and adaptable methodologies that can handle the diverse and complex nature of agricultural landscapes worldwide.

To the best of our knowledge, no existing review comprehensively analyzes techniques for agricultural parcel delineation using both machine learning and deep learning with remote sensing images, while also presenting a set of evaluation metrics specifically tailored to the varying complexities of parcels. An illustrative diagram in Fig. 1 visually represents our problem statement.

## 3. Existing methods

Categorizing the contemporary landscape of land parcel delineation via remote sensing imagery reveals a spectrum of methodologies. This spectrum encompasses machine learning techniques, contour-based models exemplified by Growing Snakes, deep learning paradigms, including classical CNN models, diverse UNet extensions, and a selection of hybrid models. With such diversity, we find a rich tapestry of approaches, ranging from classical techniques to the cutting-edge realms of advanced machine and deep learning. This section comprehensively explores these existing methodologies, each contributing unique insights and capabilities to the field of parcel delineation. Our analysis reveals the breadth of available techniques and establishes a strong foundation for rigorous evaluation and comparative analysis. To aid this, we provide a concise yet extensive summarizing 20 methods, including classical machine learning, deep learning and hybrid methodologies. Furthermore, it highlights the datasets commonly used with these approaches and the corresponding evaluation metrics. This reference consolidates valuable insights for researchers and practitioners regarding method characteristics, data sources, and performance criteria. Subsequently, we delve into the advantages of utilizing classification and segmentation-based approaches for parcel delineation, shedding light on the potential benefits and applications of these methods. As we undertake this review, our main emphasis is on deep learning methods to land parcel delineation using remote sensing images. We acknowledge the crucial contribution of deep learning in improving performance and its growing use in this domain.

### 3.1. *Baseline and backbone models*

As previously mentioned, our primary focus is on deep learning methods for land parcel delineation using remote sensing imagery. In addition, it is important to describe these networks and investigate their effectiveness with remote sensing images.

Exploring models like CNNs designed for remote sensing imagery, extended CNNs like UNet architectures, and other adaptations of deep learning methods tailored to remote sensing challenges, such as ResNet
<figure><img src="image_4.png" /><figcaption>Fig. 1. Illustrative diagram of the problem statement.</figcaption></figure>

architectures. These models play a central role in our discussion, show-
casing the core concepts behind deep learning techniques in the context
of land parcel delineation.

CNNs are a class of deep learning models specifically designed for
processing grid-like data, such as images. They have revolutionized var-
ious computer vision tasks, including image classification, image seg-
mentation, and object detection. In image classification, that involves
assigning a label or category to an input image, CNNs excel in captur-
ing hierarchical features within images, making them ideal for tasks like
image classification. Among popular CNN models used for image clas-
sification, AlexNet [24] gained attention for its success in the ImageNet
Large Scale Visual Recognition Challenge (ILSVRC) in 2012, VGGNet
[25], known for its simplicity and uniform architecture, consists of many
stacked convolutional layers with small-sized filters, ResNet (Residual
Network) [26] introduces the concept of residual blocks, allowing for
very deep networks that are easier to optimize, Inception (GoogLeNet)
[27] is notable for its inception modules that use multiple filter sizes
in parallel, capturing features at different scales, and MobileNet [28],
designed for mobile and embedded vision applications, uses depth-wise
separable convolutions to reduce computation. In image segmentation,
that involves dividing an image into segments or regions based on cer-
tain criteria, CNNs are used for semantic segmentation, where each pixel
is labelled with its corresponding class. Among widely used CNN mod-
els for image segmentation include, UNet [29], originally designed for
biomedical image segmentation, features a U-shaped architecture with
skip connections to retain spatial information, SegNet [30] focuses on
efficient segmentation using an encoder-decoder structure and pooling
indices for upsampling, DeepLab [31] is known for its atrous (dilated)
convolutions that enable capturing multi-scale context while preserv-
ing resolution, and Mask R-CNN [32] combines object detection with
instance segmentation, allowing identification of individual object in-
stances within a scene In recent years, CNNs have found applications in
agricultural tasks like field boundary extraction and agricultural par-
cel delineation, segmenting satellite or drone images to identify and
separate fields or parcels. These applications can greatly benefit from
CNNs due to their ability to learn complex features directly from im-
ages. However, the successful application also requires domain-specific
data preprocessing, model fine-tuning, and appropriate evaluation met-
rics. While specific models can vary, the general architecture involves
adapting segmentation models to the agricultural context. While there
may not be models dedicated exclusively to agricultural field delineation
using remote sensing imagery, the field can benefit from the transfer of
knowledge and the adoption of hybrid architectures. Researchers derive

inspiration from various domains and adapt and apply methodologies to
fulfill the specific requirements of agricultural parcel delineation. These
methods, cited in Table 1, highlight the interdisciplinary nature of the
use of remote sensing in agriculture where contributions from different
fields advance methodologies. As the field of deep learning continues to
evolve, CNNs are likely to play an even more significant role in revolu-
tionizing various aspects of agriculture, from crop monitoring to yield
prediction and precision farming.

Furthermore, it is of significant importance to explain technically
these architectures shown in 3.

A CNN is generally made up of three layers as demonstrate sub-figure
(b) in Fig. 3. CNN architectures can we used as a baseline in deep learn-
ing. A baseline model serves as a reference point for evaluating the
performance of other models. A convolutional layer, a pooling layer,
and a fully connected layer. Convolution makes use of three important
ideas that have motivated computer vision researchers, sparse interac-
tion, parameter sharing, and equivariant representation.<fnref n="1" /> Trivial layers
of a neural network use a matrix multiplication by a matrix of parame-
ters that describe the interaction between the input unit and the output
unit. This means that each output unit interacts with each input unit.
Convolutional neural networks, however, have sparse interaction. This
is achieved by making the kernel smaller than the input - for example,
an image may have millions or thousands of pixels, but when we process
the kernel we can see meaningful information in tens or hundreds of pix-
els. Thus, fewer parameters must be stored, which not only reduces the
model's memory footprint, but also improves the model's statistical effi-
ciency. Due to parameter sharing, the layers of the convolutional neural
network will have a property of equivariance to translation. This means
that if we change the input in one way, the output will change in the
same way.

UNet or U-shape models, have become a widely adopted baseline
architecture in deep learning, particularly for biomedical image segmen-
tation. Initially proposed for this domain, U-Net's distinctive architec-
ture features encoder-decoder paths as shown in Fig. 3 (a), allowing it to
effectively capture both global context and local details within images.
The encoder path acts as a feature extractor and learns an abstract repre-
sentation of the input image through a sequence of encoder blocks. Each
encoder block consists of two 3x3 convolutions, where each convolution
is followed by a Rectified Linear Unit (ReLU) activation function. The
ReLU function introduces non-linearity into the network, which helps to

<footnote n="1">¹ CNN motivation.</footnote>
<table><caption>**Table 1**
Datasets, metrics utilized in parcel delineation methods. Seg, Clsf, and Del are Segmentation, Classification, and Delineation tasks.</caption><thead><tr><th>Methods</th><th>DataSet Utilized</th><th>Evaluation Metrics</th><th>Seg</th><th>Clsf</th><th>Del</th></tr></thead><tbody><tr><td>UNet [3]</td><td>SIGPA</td><td>BDE</td><td></td><td></td><td>✓</td></tr><tr><td>U-Net & Random Forest [4]</td><td>Sentinel-2</td><td>OA, Precision, F1-Score, Jaccard index</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>Spatial UNet [9]</td><td>Sentinel-2</td><td>Acc, F1-Score</td><td></td><td></td><td>✓</td></tr><tr><td>RR-UNet [10]</td><td>Sentinel-2</td><td>OA, F1-Score, OE, CE, Acc, Recall</td><td></td><td></td><td>✓</td></tr><tr><td>Adversarial ResUNet-a [11]</td><td>Sentinel-2</td><td>OA, F1-Score, MCC</td><td>✓</td><td></td><td>✓</td></tr><tr><td>ResUNet-a: D6 and D7 [6]</td><td>Sentinel-2</td><td>OA, MCC, F1-Score</td><td>✓</td><td></td><td>✓</td></tr><tr><td>FracTALResUNet [12]</td><td>Sentinel-2</td><td>Geometric Acc and Thematic Acc</td><td>✓</td><td></td><td>✓</td></tr><tr><td>DSCUnet [13]</td><td>Gaofen-2</td><td>ACC, R, F1-Score, IoU</td><td></td><td></td><td>✓</td></tr><tr><td>UNet vs ENVIUNet [14]</td><td>UAV</td><td>OA, R, P, F1-Score</td><td></td><td></td><td>✓</td></tr><tr><td>CNN vs RF [2]</td><td>UAV</td><td>Acc, Overlap, R, P</td><td></td><td></td><td>✓</td></tr><tr><td>Mask R-CNN [7]</td><td>Sentinel-2</td><td>OA, F1-Score, Recall, Fragmented-Error, Under-seg Error</td><td></td><td></td><td>✓</td></tr><tr><td>UTAE-Paps [15]</td><td>Sentinel-1-Sentinel-2</td><td>OA, mIoU, SQ, RQ, PQ</td><td>✓</td><td>✓</td><td>✓</td></tr><tr><td>MD-FCN [16]</td><td>Sentinel-2</td><td>Acc, Recall, Type-1 and 2 error</td><td></td><td></td><td>✓</td></tr><tr><td>FCN [17]</td><td>WV-2</td><td>Acc, R, F1-Score</td><td></td><td></td><td>✓</td></tr><tr><td>GGC [18]</td><td>Sentinel-2</td><td>Jaccard index</td><td>✓</td><td></td><td>✓</td></tr><tr><td>DL-GGC [19]</td><td>Sentinel-2</td><td>Acc, F1-Score, AUC</td><td>✓</td><td></td><td>✓</td></tr><tr><td>RUSBoost [20]</td><td>WV-2</td><td>Acc</td><td>✓</td><td></td><td>✓</td></tr><tr><td>consensus Seg [21]</td><td>Pléades-1</td><td>BDE</td><td>✓</td><td></td><td>✓</td></tr><tr><td>MSAOS [22]</td><td>GF-2</td><td>Acc, F1-Score</td><td>✓</td><td></td><td>✓</td></tr><tr><td>SAM [8] (Fine tuning) LoRA SAM</td><td>Sentinel-2</td><td>Acc, F1-Score, mIoU, BDE, Under-seg and Over-seg Errors</td><td>✓</td><td></td><td>✓</td></tr><tr><td>BASNet [23] (Domain Adaptation)</td><td>Sentinel-2</td><td>Acc, F1-Score, mIoU, BDE, Under-seg and Over-seg Errors</td><td>✓</td><td></td><td>✓</td></tr></tbody></table>

better generalise the training data. The output of the ReLU is used as a skip connection for the corresponding block of the decoder. This is followed by 2x2 max-pooling, which halves the spatial dimensions (height and width) of the feature maps. By reducing the number of trainable parameters, this reduces the computational cost. These skip connections help in better flow of gradient while back-propagation, which in turn helps the network to learn better representation. The bridge connects the encoder and decoder networks and completes the information flow. It consists of two 3x3 convolutions, each followed by a ReLU activation function. The decoder path is responsible for the extraction of the abstract representation and the generation of a semantic segmentation mask. The decoder block starts with a 2x2 transpose convolution. The next step is to concatenate it with the corresponding feature map of the skip connections from the encoder block. These skip connections provide features from earlier layers that are sometimes lost due to the depth of the network. This is followed by two 3x3 convolutions, where each convolution is followed by a ReLU activation function. Finally, a 1x1 convolution with sigmoid activation is applied to the output of the last decoder. The segmentation mask, which represents the pixel-wise classification, is obtained from the sigmoid activation function. Furthermore, for both CNN and UNet models, we can add some suggestions to improve the efficiency of the methods, such as Use a batch normalisation layer between the convolution layer and the ReLU activation function. Batch normalisation reduces the internal shift of the covariance and makes the network more stable during the training process. Some time after the ReLU activation function, the dropout is also used. By dropping (ignoring) some randomly selected neurons, it forces the network to learn a different representation. This helps the network to become less dependent on certain neurons. This helps the network generalise better and prevents over-fitting.

Another type of deep learning networks, ResNet is a powerful image classification model, often used as a backbone for other deep learning tasks, capable of being trained on large amounts of data to achieve state-of-the-art results. A backbone model is a pre-trained network that is used as the foundation for a more complex model, providing the initial feature extraction capabilities. One of ResNet's key innovations is the use of residual connections, which allow the network to learn a set of residual functions that map the input to the desired output. These residual connections allow the network to learn much deeper architectures than was previously possible, without suffering from the problem of vanishing gradients.

In addition, in the state-of-the-art, we can find hybrid models, by combining UNet and ResNets architectures. The idea is to use a ResNet, as shown in schema (b) of Fig. 2 as the encoder in the UNet architecture.

In a ResUNet, the encoder part is a pre-trained ResNet. The model benefits from the representational power of a deep network trained on a large dataset by using a pre-trained ResNet. The decoder part of the ResUNet is similar to the original UNet, but with one additional modification. Skip connections from encoder to decoder pass not only raw features, but also features after being processed by ResNet blocks. For example, Zhengxin Zhang et al. in [33] presents a semantic segmentation neural network that combines the strengths of residual learning (from ResNet) and UNet for road area extraction. Same thing in [10,11,6,12], authors propose new derivatives of ResUNet architectures are Recurrent Residual UNet, Adversarial ResUNet, ResUNet-a, and a FracTALResUNet respectively. For the Recurrent Residual UNet, we find recurrent connections in a neural network allow the network to maintain a form of "memory" by using its output as an additional input for the next step. This is particularly useful when dealing with sequential data, where the order of data points is important. In the context of image segmentation, recurrent connections can help the model capture temporal dependencies in image sequences, such as changes in an agricultural field over time. Other concept is presented in FracTALResUNet called FracTALNet [34] as a backbone as shown in Fig. 2 (a). This network involves the repeated application of a simple expansion rule to generate deep networks whose structural layouts are precisely truncated fractals, as demonstrates this paper [35], authors propose an efficient paddy field mapping method using Landsat-8 imagery and object-based image analysis based on FracTALNet. However, the common element between these networks is the use of skip connections that are very useful in terms of:

* Recover fine details: Skip connections help to recover fine details in the prediction 1. They allow the network to use both the input image and some very fine-grained deep features, which would not be possible without skip connections [26].

* Preventing information loss: Without skip connections, there can be a loss of information from layer to layer [26]. Skip connections help the network to "remember" what it was trying to learn in the first place [26].

* Facilitation of feature re-use: Concatenate skip connections provide an alternative way to ensure the re-usability of features of the same dimensionality from previous layers [26].

* Spatial information recovery: Long skip links are used to pass features from the encoder path to the decoder path to recover spatial information lost during downsampling [26].

* Network performance enhancement: Skip connections can improve network performance by facilitating the flow of gradients during the
<figure><img src="image_5.png" /><figcaption>Fig. 2. Most backbones used in state-of-the-art.</figcaption></figure>

training process, which can help overcome the vanishing gradient problem.

**Mask R-CNN** is a state-of-the-art method for object detection and instance segmentation [36]. The architecture of Mask R-CNN is based on a Feature Pyramid Network (FPN) and a ResNet101 backbone. It

adds a branch for predicting an object mask in parallel with the existing branch for bounding box recognition [36]. This allows Mask R-CNN to efficiently detect objects while simultaneously generating a high-quality segmentation mask for each instance [36]. For automating field boundary detection process, Meyer et al. propose in [7] a method based on Mask R-CNN. The paper adapts the Mask R-CNN architecture, origi-
<figure><img src="image_6.png" /><figcaption>Fig. 3. Most baselines used in state-of-the-art.</figcaption></figure>

nally designed for object detection and instance segmentation, for field boundary detection. This allows for automatic generation of closed polygon boundaries without extensive post-processing. The default Mask R-CNN configuration is adjusted to better handle the large variation in field sizes and shapes observed in satellite imagery. Specifically, the number of areas of interest and the range of possible bounding box sizes and aspect ratios are modified.

We can see that all the methods we have presented so far take advantage of segmentation (semantic segmentation or instance segmentation) to perform delimitation. Segmentation provides information about the entire parcel area, not just the boundary, enabling further analysis like crop type classification or area calculation. Unlike deep learning-based segmentation methods, in the literature we can find traditional segmentation techniques. There are Three main categories, edge-based detection methods, region-based growing methods, and wathershed-based segmentation methods. [37,38], [20,39], and [40,41] are respectively some works presented of the three segmentation categories in state-of-the-art. In addition, edge-based detection methods are traditional techniques frequently depend on edge detection algorithms such as Canny or Sobel [42] to identify abrupt alterations in pixel values, which may indicate potential parcel boundaries. However, these methods may be susceptible to noise and may encounter difficulties with complex or ambiguous boundaries. Region-based growing methods begin with the selection of seed points within a given parcel. These seed points are then iteratively augmented by neighbouring pixels that exhibit similar characteristics, thereby forming a region. While this method can be effective, it is important to carefully select the seed points and similarity criteria in order to ensure optimal results. Watershed-based Segmentation methods treat the image as a map of the landscape. The intensity of pixel colours

is used to represent elevation, and the boundaries separating the different areas of land are delineated using Watershed lines. These lines are used to separate catchment basins. However, over-segmentation is a potential issue with this method, requiring additional processing.

Moving now to another segmentation types called panoptic segmentation. Aims to provide a complete and unified understanding of a scene by combining the strengths of both semantic segmentation and instance segmentation. It involves assigning each pixel in an image with two labels. Semantic Label to identify the class of the object or region that the pixel belongs to (e.g., person, car, road, building). Instance Label to differentiate individual object instances within the same class (e.g., person 1, person 2, car 1, car 2). In this paper [15] authors propose a new panoptic segmentation-based method of Satellite Image Time Series (SITS). The proposed method, **U-TAE + PaPs** (U-net with Temporal Attention Encoder and Parcels-as-Points (PaPs) Segmentation Module), aims to identify and classify individual agricultural parcels across multiple time steps and leverages both spatial and temporal information to achieve accurate and efficient segmentation. First, the U-TAE encoder extracts features at multiple resolutions from each image in the sequence using a UNet-like architecture. Simultaneously, it employs a temporal attention mechanism that identifies the most informative time steps for each location, allowing the network to focus on relevant temporal dynamics across different scales. This results in rich and adaptive spatio-temporal feature maps that capture the complex patterns within the SITS data. Second, the PaPs segmentation module utilizes these features to detect and delineate individual parcels. It efficiently predicts potential parcel centres, estimates their sizes and crop types, and refines pixel-wise masks by combining a predicted shape patch with a global saliency map.
<figure><img src="image_7.png" /><figcaption>Fig. 4. LoRA-SAM architecture overview.</figcaption></figure>

Before finishing with encoder-decoder (or like U-shape) architectures, we introduce the **BASNet** method for boundary-aware segmentation [23]. A deep learning model designed for highly accurate image segmentation with a specific focus on generating sharp and precise boundaries. BASNet method is a Predict-Refine Architecture. Prediction Module like an encoder-decoder network with dense supervision predicts initial segmentation probability maps. The Residual Refinement Module (RRM) is a lightweight encoder-decoder network refines the predicted probability map by learning the residuals between the coarse prediction and the ground truth. This refines both regional probabilities and boundary details. In our experiments part we adapt the core architecture and loss functions of BASNet to the task of parcel delineation.

Recently, the latest research is based on Transformers [43], which have been shown to perform well with satellite images for various tasks. This study [44] showed that more peer-reviewed studies used transformers in Remote Sensing (RS). This paper [45] proposes a new method Multi-Swin Mask Transformer (MSMTransformer) based on Vision Transformers (ViT) like a backbone phase as shown in (c) of Fig. 2. A backbone model, like ViT (Vision Transformer), is a pre-trained network used as the foundation for more complex models, providing initial feature extraction capabilities. MSMTransformer is for boundary detection of agricultural fields take advantage of the instance segmentation, it builds on the foundation of Mask2Former, an end-to-end instance segmentation framework. In addition, it introduces the concept of multi-scale into the Swin Transformer to better extract features with widely different scales. Furthermore, we can take advantage of crop classification to extract field boundaries. In [46], the authors present a CNN Transformer method that combines a CNN model and transformers to unlock the secrets hidden in multitemporal multisensor data by using multi-layer encoder modules derived from the transformer to mine deep correlation patterns in the multitemporal data. In addition, it's worth mentioning the famous Transformers-based model, **SAM** (Segment Any Things) [8]. SAM model is a state-of-the-art framework for image segmentation, leverages a ViT backbone optimized for promptable segmentation tasks. Trained on the expansive SA-1B dataset—comprising 11 million images and 1.1 billion masks—SAM achieves remarkable generalization capabilities. Its prompt-driven architecture allows users to guide segmentation through input prompts such as points, bounding boxes, text, or masks, with iterative refinement enabling precise adjustments. Notably, SAM operates in a zero-shot manner, segmenting objects without task-specific training and handling ambiguous scenarios by generating multiple valid masks (e.g., overlapping instances). The model excels in speed and scalability, processing images in real-time (~50 ms per mask on modern GPUs), making it efficient for large-scale applica-

tions like satellite imagery analysis or video processing. Recent studies underscore SAM's potential in remote sensing (RS), particularly its ability to reduce reliance on manual annotation while adapting to diverse geospatial tasks [47]. For instance, SAM's deployment in RS applications demonstrates promising adaptability for infrastructure mapping and land cover classification, though challenges persist with lower spatial resolution imagery, where fine-grained details may be compromised.

The SAMed method [48] is a Customized Segment Anything Model for medical image segmentation that adapts and customizes SAM through a low-rank-based (LoRA) fine-tuning strategy. LoRA-SAM introduces lightweight, low-rank trainable layers into SAM's architecture, selectively fine-tuning these layers while keeping the pre-trained weights frozen as demonstrated in Fig. 4. This strategy significantly reduces computational costs and memory requirements, enabling SAM to specialize for specific tasks or datasets without compromising its general segmentation capabilities. While SAM excels in general-purpose segmentation, its zero-shot performance often falters in domain-specific scenes, whether in medical imaging or remote sensing, where unique challenges diverge significantly from natural images. LoRA [49] addresses these limitations by enabling parameter-efficient adaptation of SAM's ViT to downstream tasks. Unlike full fine-tuning, which risks overfitting or catastrophic forgetting, LoRA injects lightweight low-rank matrices into SAM's attention layers, approximating task-specific adjustments while preserving its foundational segmentation capabilities. The benefits of LoRA-based approaches include: (1) computational efficiency, reducing trainable parameters by > 90% and memory overhead; (2) domain adaptability, overcoming SAM's inherent biases toward natural image statistics; and (3) preservation of zero-shot utility, ensuring SAM remains deployable across diverse tasks. This approach has proven successful in medical imaging, where SAMed performs semantic segmentation effectively, and in agricultural remote sensing, where it enhances SAM's ability to discern subtle features like crop boundaries and soil patterns without requiring extensive annotated datasets. By bridging SAM's generalization gaps, LoRA unlocks its potential for various domain-specific applications, demonstrating how foundation models can be tailored to resource-constrained domains.

## 3.2. Evaluation metrics

In the context of land parcel delineation, evaluating methods performance is important and a crucial point. To accomplish this, evaluation metrics are utilized to measure accuracy and effectiveness. This section explores several evaluation metrics tailored to parcel delineation. These metrics provide valuable insights into method performance and guide the quest for more accurate delineation solutions. Among the 14 evalu-
ation metrics mentioned in Table 1, 14 metrics were selected and gathered in three categories: global metrics, boundary metrics, and structure metrics

### 3.2.1. Global metrics

The most of used global metrics like accuracy, recall, F1-Score, Jaccard Index (JI) [50], also known as mIoU (mean Intersection over Union), MCC (Matthews Correlation Coefficient) [51], are calculated with the confusion matrix [52,53]. Those metrics offer a global view of method performances. Accuracy measures the overall correctness of the model's predictions. It quantifies the proportion of predictions that were correct, regardless of whether they were positive or negative.

$$Accuracy = \frac{TP + TN}{TP + TN + FP + FN} \qquad (1)$$

Recall measures the model's ability to correctly identify actual parcel pixels. It quantifies the proportion of real parcel pixels that were correctly classified as parcel boundaries.

$$Recall = \frac{TP}{TP + FN} \qquad (2)$$

Precision measures the model's ability to avoid false positives, meaning it quantifies the proportion of predicted parcel pixels that were actually correct.

$$Precision = \frac{TP}{TP + FP} \qquad (3)$$

The F1 score is the harmonic mean of precision and recall, providing a balanced measure that takes into account both false positives and false negatives. The F1 score is particularly valuable when dealing with unbalanced data or when both precision and recall are important. It helps to identify models that achieve a good balance between capturing the most parcel areas and minimising false positive classifications.

$$F1-Score = \frac{2TP}{2TP + FP + FN} \qquad (4a) \\ F1-Score = 2 * \frac{Precision * Recall}{Precision + Recall} \qquad (4b)$$

IoU, also called Jaccard Index, measures the overlap between the predicted parcel segmentation mask and the ground truth mask. It provides a more granular assessment of segmentation accuracy than pixel-wise metrics like accuracy or recall. A higher IoU indicates a better match between the predicted and actual parcel boundaries, signifying a more accurate delineation. IoU is calculated as the ratio of the intersection area between the predicted segmentation mask and the ground truth mask to their union area. Area of Intersection corresponds to the pixels that were correctly classified as part of the parcel in both the prediction and the ground truth. This is equivalent to the True Positives (TP), and area of Union includes all pixels that were either predicted or labelled as part of the parcel. This can be calculated as the sum of True Positives (TP), False Positives (FP), and False Negatives (FN).

$$IoU = \frac{Intersection}{Union} \qquad (5)$$

Or,

$$JaccardIndex = \frac{TP}{TP + FP + FN} \qquad (6)$$

Global metrics as presented above, are typically measured on a scale from 0 to 1, with higher values indicating better performance. A score of 0.9 to 1.0 represents very good performance, while 0.7 to 0.85 indicates good performance. A moderate performance falls within the range of 0.5 to 0.6, and scores below 0.45 are considered poor.

MCC is a valuable metric for evaluating binary classification problems, especially when dealing with unbalanced data sets. It provides a balanced measure that takes into account all four categories of the confusion matrix: True Positives (TP), True Negatives (TN), False Positives (FP) and False Negatives (FN). When dealing with agricultural datasets

where the number of non-parcel boundaries pixels far exceeds the number of parcel boundaries pixels, MCC provides a more reliable evaluation metric than accuracy or F1-score. MCC values range from -1 to +1. +1 is a perfect prediction. 0 represents random guessing, meaning the model's predictions are no better than chance. -1 indicates total mismatch between predicted and ground truth.

$$MCC = \frac{(TP \times TN) - (FP \times FN)}{\sqrt{(TP + FN)(TP + FP)(TN + FP)(TN + FN)}} \qquad (7)$$

### 3.2.2. Boundary metrics

Whereas global metrics assess the global performance, boundary metrics like BDE (Boundary Displacement Error) [54] focusing on pixels closed to the limits can offer more relevant information about method performances in the case of parcels delineation.

BDE measures specifically the average displacement between the predicted parcel boundary and the ground truth boundary. It quantifies the spatial accuracy of the delineated parcel boundaries. Lower BDE values indicate a closer match between the predicted and actual boundaries, signifying better precision in boundary delineation. BDE is valuable when the precise location of parcel boundaries is critical. It helps to assess the model's ability to accurately capture the spatial extent of parcels, which is important for tasks like land registration. Where:

* the distance ($d_E$) between a boundary pixel ($b_s$) in the obtained boundary image ($B_s$) and the closest pixel ($b_{gt}$) in the ground truth boundary image ($B_{gt}$) is used to define the disagreement (error) of each boundary pixel.

* The lower the BDE value, the more accurate the segmentation results. By minimizing BDE, parcel delineation algorithms can improve the accuracy and reliability of the resulting parcel boundaries [3]. More information can be found in [55].

$$BDE = \frac{1}{2} \left( \frac{1}{|B_s|} \sum_{ps_s \in B_s} \sum_{p_{gt} \in B_{gt}} \min\{d_E(ps_s, p_{gt})\} + \frac{1}{|B_{gt}|} \sum_{ps_{gt} \in B_{gt}} \sum_{p_s \in B_s} \min\{d_E(p_{gt}, ps_s)\} \right) \qquad (8)$$

Boundary metric, is typically measured on a scale from 0 to 1, with higher values indicating better performance. A score of 0.9 to 1.0 represents very good performance, while 0.7 to 0.85 indicates good performance. A moderate performance falls within the range of 0.5 to 0.6, and scores below 0.45 are considered low.

### 3.2.3. Structure metrics

Structure metrics highlight the discordance in terms of over- or under-segmentation. Over-segmentation occurs when a single object or region is incorrectly divided into multiple segments. In the context of parcel delineation, this means a single parcel is fragmented into several smaller segments. Fragmentation error is a metric quantifies the degree of over-segmentation, specifically measuring how fragmented the predicted parcel segments are compared to the ground truth parcels. Under-segmentation occurs when multiple objects or regions are incorrectly merged into a single segment. In parcel delineation, this means multiple parcels are grouped together as one segment. This metric quantifies the degree of under-segmentation, measuring the proportion of ground truth parcel area that is missed due to merging with other parcels.

$$Over Segmentation Error = \frac{FSP}{TP} Under Segmentation Error = \frac{MP}{TP} \qquad (9)$$

where:
* FSP = False Segmentation Parcels (i.e., number of predicted parcels that do not overlap with any ground truth parcel).

* MP = Missed Parcels (i.e., number of ground truth parcels that do not overlap with any predicted parcel).

* TP = True Positives (i.e., number of predicted parcels that overlap with at least one ground truth parcel).

Structure metrics, are typically measured on a scale from 0 to 1, with higher values indicating high errors. A score of 0.6 to 1.0 represents high errors, while 0.3 to 0.5 indicates moderate errors. Low errors fall within the range of 0.2 to 0.0.

Omission and commission errors are two basic types of classification errors that are encountered in the analysis of remote sensing images, including tasks such as land cover classification and object detection. An omission error occurs when a pixel or object belonging to one class is misclassified as another class or as background, also known as a false negative.<fnref n="2" /> Essentially, it's a miss where the model fails to recognise something that should have been recognised. In agricultural parcel delineation, an omission error would occur if part of a parcel was classified as background or another land cover type, resulting in an overestimation of the area of the parcel.

$$Omission\ Error\ (OE) = \frac{FN}{(TP + FN)} \qquad (10)$$

A commission error, also known as a false positive, occurs when a pixel or object that does not belong to a particular class is misclassified as belonging to that class. It's a “false flag” where the model identifies something that shouldn't be there.<fnref n="3" /> In parcel delineation, a commission error would occur when some background or other land cover is misclassified as belonging to a parcel, resulting in an overestimation of parcel area.

$$Commission\ Error\ (CE) = \frac{FP}{(FP + TN)} \qquad (11)$$

### 3.2.4. Quality metrics

Panoptic segmentation provides a comprehensive approach to understanding a scene by combining semantic segmentation (classification of each pixel) and instance segmentation (detection and segmentation of individual objects). This integrated approach offers several advantages for agricultural parcel delineation, which can be measured using specific evaluation metrics: Segmentation Quality (SQ), Recognition Quality (RQ) and Panoptic Quality (PQ).

* SQ measures how well the predicted segmentation masks match the ground truth masks at the pixel level. It essentially assesses the accuracy of boundary delineation and region segmentation.

* RQ measures how well the model identifies and classifies individual object instances within each semantic category. In the context of parcel delineation, this relates to the detection and classification accuracy of individual parcels.

* PQ is the primary metric for panoptic segmentation, providing a combined measure of both SQ and RQ. It reflects the overall performance of the model in both segmenting regions and recognizing individual object instances.

* Benefits for Parcel Delineation: A high PQ indicates that the model is successful in accurately delineating parcel boundaries (high SQ) and correctly identifying and classifying individual parcels (high RQ), leading to a comprehensive and reliable understanding of the agricultural scene.

$$Segmentation\ Quality(SQ) = \frac{IoU}{TP} \qquad (12)$$

$$Recognition\ Quality(RQ) = \frac{TP}{TP + 0.5FP + 0.5FN} \qquad (13)$$

$$Panoptic\ Quality(PQ) = \frac{IoU}{TP + 0.5FP + 0.5FN} \qquad (14)$$

Quality metric, is typically measured on a scale from 0 to 1, with higher values indicating better performance. A score of 0.9 to 1.0 represents excellent performance, while 0.7 to 0.85 indicates good performance. A moderate performance falls within the range of 0.5 to 0.65, and scores below 0.5 are considered low.

## 4. Dataset

### 4.1. Introduction to dataset catalogs

Throughout our review, we present a comprehensive summary of the datasets commonly used in current state-of-the-art research. These datasets act as a basis for our methods, influencing the accuracy, precision and applicability of the outcomes. In this section, we will present a detailed table that not only catalogs the datasets employed in various studies but also highlights essential characteristics such as their sources, spatial, spectral, temporal resolution, and crucial bands for parcel delineation. Furthermore, important bands used for parcel delineation may vary depending on the specific application and the type of land cover being analyzed.

Table 2 summarizes the different data sources and their features. Aerial and UAV images are listed here, although they fall outside the scope of our study. However, it is worth mentioning their properties compared to satellite images:

* Revisit time: Flexible, depending on flight planning and weather conditions. Important bands: Varies depending on sensor used.

* RGB: Provides visual information for identifying boundaries and land cover.

* Multispectral and hyperspectral sensors: Offer additional spectral bands for detailed analysis of vegetation characteristics, soil properties and other features.

### 4.2. Experiment dataset

The dataset used in our study comprises Sentinel-2 satellite images captured over France during the year 2017, as previously utilized in [9]. Each image in the dataset is standardized to a resolution of 224 x 224 pixels, corresponding to a ground area of 2.24 km x 2.24 km. The binary masks annotating each satellite image are derived from the French Graphic Parcel Register (GPR), a comprehensive geographic information system designed for agricultural parcel identification and management. The GPR provides authoritative data on parcel delineation, serving as ground truth for our study. This cadastral database not only contains the precise geometric boundaries of agricultural parcels but also includes additional parcel characteristics such as land use, crop type, and field size. The integration of high-resolution Sentinel-2 imagery with GPR-derived masks ensures accurate spatial correspondence between the remote sensing data and the actual field boundaries, making it particularly suitable for agricultural parcel segmentation tasks. These masks are represented as grayscale images, also with a resolution of 224 x 224 pixels in 5.

The Fig. 5 consists of three main sections:

* Input (a): This section displays a sample RGB Sentinel-2 satellite image, representing the raw input data used for the parcel delineation task. The image clearly shows the diverse agricultural landscape with various field patterns and land cover types.

* Boundary Output (b): displays the model's output in the form of delineated boundaries between agricultural parcels. These boundaries are overlaid on the original image, highlighting the model's ability

<footnote n="2">² OE.</footnote>

<footnote n="3">³ CE.</footnote>
<table><caption>**Table 2**
Dataset catalogue characteristics.</caption><thead><tr><th>Dataset</th><th>Source</th><th>Spatial Resolution</th><th>Spectral Resolution</th><th>Revisit Time</th><th>Important Bands</th><th>Advantages</th><th>Disadvantages</th></tr></thead><tbody><tr><td>Sentinel</td><td>European Space Agency (ESA)</td><td>10 m, 20 m, 60 m</td><td>S1 → 1 band,<br>S2 → 13 bands,<br>S3 → 21 bands</td><td>S1 → 12 days,<br>S2 → 5 days,<br>S3 → 1 day</td><td>R, G, B,<br>Near-Infrared,<br>Shortwave-Infrared</td><td>High spatial and temporal resolution, free and open data</td><td>Limited spectral resolution for some sensors (S1)</td></tr><tr><td>Landsat-8</td><td>NASA Satellite Mission</td><td>30 m</td><td>11 bands</td><td>16 days</td><td>R, G, B,<br>Near-Infrared,<br>Shortwave-Infrared</td><td>Long historical archive, moderate spatial resolution, widely used</td><td>Lower spatial resolution compared to some commercial satellites</td></tr><tr><td>WorldView</td><td>Maxar Technologies</td><td>WV1 → 1 m,<br>WV2 → 1.8 m,<br>WV3 → (0.31, 1.24, 3.7, 30) m,<br>WV4 → (0.31, 1.24) m</td><td>WV1 → 1 band,<br>WV2 → 8 bands,<br>WV3 → 29 bands,<br>WV4 → 5 bands</td><td>WV1 → 1.7 days,<br>WV2 → 1.1 days,<br>WV3 → 1 day,<br>WV4 → 1 day</td><td>Panchromatic, R, G,<br>B, Near-Infrared</td><td>Very high spatial resolution, good spectral resolution, multiple sensors available</td><td>Costly, smaller coverage area compared to Sentinel or Landsat</td></tr><tr><td>Gaofen</td><td>China National Space</td><td>GF1 (Pan:0.8 m, MS:3.2 m),<br>GF2 → 16 m,<br>GF3 → 1-500 m</td><td>GF1 → 5 band,<br>GF2 → 5 bands,<br>GF3 → 1 band<br>(C-band<br>Synthetic Aperture Radar (C-SAR)),<br>GF4 → 5 bands</td><td>GF1 → 4 days,<br>GF2 → 5 days,<br>GF3 → 1 day,<br>GF4 → 1 day</td><td>Panchromatic, R, G,<br>B, Near-Infrared</td><td>High spatial resolution (especially GF2), includes SAR data</td><td>Limited availability outside of China, relatively new program (less historical data)</td></tr><tr><td>Pléiades</td><td>European Space Agency (ESA)</td><td>0.5 m</td><td>5 bands</td><td>1 day</td><td>Panchromatic, R, G,<br>B, Near-Infrared</td><td>Very high spatial resolution, good revisit time</td><td>Costly, smaller coverage area</td></tr><tr><td>Aerial Imagery</td><td>National Agricultural Imagery Program (NAIP), Google Earth, DigitalGlobe, EagleView</td><td>Depends on the application and the requirements</td><td>-</td><td>-</td><td>R, G, B,<br>Near-Infrared</td><td>Extremely high spatial resolution, can be acquired on-demand</td><td>Costly, data availability varies by location, processing can be complex</td></tr><tr><td>UAV (Unmanned Aerial Vehicle)</td><td>Drone Images, OpenAerialMap, SenseFly, Academic Institutions</td><td>Depends on the application and the requirements</td><td>-</td><td>-</td><td>-</td><td>Ultra-high spatial resolution, flexible and customise data acquisition</td><td>Limited coverage area per flight, data processing can be complex, regulations vary</td></tr></tbody></table>
<figure><img src="image_8.png" /><figcaption>Fig. 5. DataSet used in experiments. (a) The image, (b) the boundary mask, (c) the filled mask-areas of farm parcels.</figcaption></figure>

to distinguish individual parcels. The boundaries are visually repre-
sented by assigning a specific grayscale value (e.g., white) to pixels
belonging to the boundary lines, while other pixels are assigned a
different value (e.g., black).

*   **Segmentation Mask Output (c):** presents the output as segmentation masks of the agricultural parcels. Each unique parcel is assigned a distinct grayscale value, creating a segmentation mask where each pixel belongs to a specific parcel or the background. This provides a clear visualization of the model's segmentation performance, demonstrating its ability to accurately identify and segment individual parcels.

Sentinel-2 stands out as the preferred choice among satellite imaging datasets for several compelling reasons. Notably, Sentinel-2 offers a significantly larger coverage area per image, enabling the delineation of a greater number of farm parcels within a single frame. Moreover, the accessibility of Sentinel-2 data is a key advantage, as it is freely available to users. Additionally, the satellite boasts a relatively short global revisit time, making it an ideal selection for capturing the most up-to-date cadastral information through satellite imaging. These attributes collectively render the Sentinel-2 dataset a highly suitable and advantageous option for our purposes.

4.2.1. Parcel complexity

Fig. 6 presents a new concept to classify our parcels. With hand-
crafted features based on regularity and simplicity of plots, and on defi-
niteness and linearity of boundaries, we classified our tested parcels into
three groups: complex parcels, less complex parcels and simple parcels.
These three parcel categories are defined as follows:

*   Simple Parcels: a limited number of simple parcels with linear and geometric shapes and clear boundaries characterise the study area.

*   Less Complex parcels: emergence of non-agricultural fields (forests, urbanization), mix of small and medium-sized parcels, emergence of curved parcels.

*   Complex Parcels: heterogeneous mesh, curves form boundaries and less organized parcels.

Moreover, classifying parcels based on complexity can be valuable
for various reasons:

*   Model Evaluation: Analyzing the performance of delineation models across different complexity levels can reveal strengths and weaknesses, guiding model improvement efforts.

*   Assessment of Task Difficulty: Parcel complexity can be used as a proxy for the difficulty of the delineation task, helping to set expectations and compare results across different regions or datasets.

*   Application-Specific Considerations: Certain applications might require different levels of accuracy or detail depending on the parcel complexity for example panoptic segmentation and parcel delineation tasks.

5. Experiences and analysis

5.1. *Metric-based results*

Table 3 presents a comprehensive performance analysis of nine deep learning methods for parcel delineation, using both quantitative and qualitative assessments. This table provides numerical scores for each method across global metrics (accuracy, F1-score, mIoU) which measures overall segmentation accuracy; boundary metrics (BDE), assessing boundary delineation accuracy; and structural metrics (over-segmentation and under-segmentation) evaluating the model's ability to capture the structural integrity of parcels. Table 4 summarizes the overall performance of each method based on their respective achieved scores, categorizing them descriptively from very poor to very good providing an interpretive reading of their relative performance.

Our results show varying levels of performance across the meth-
ods. While CNN exhibited poor performance, with a low mIoU of 0.27
and high over-segmentation errors, methods such as ResUnet-a, Frac-
TalResUNet, and BASNet consistently achieved very good performances,
reaching high mIoU values and moderate errors in boundary displace-
ment and over/under-segmentation errors. LoRA-SAM method, despite
exhibiting very good global metrics, showed a higher tendency for
under-segmentation, suggesting potential challenges in accurately cap-
turing complete parcel boundaries. These findings highlight the impor-
tance of selecting appropriate deep learning methods for parcel delin-
eation based on the specific application and desired balance between
overall accuracy, boundary precision, and structural integrity.

5.2. *Complexity-based results*

Figs. 7, 8, and 9 show the visual performance of four models UNet, FracTALResUNet, BASNet, and LoRA-SAM on two scenes (I1 and I2) with different level of complexity of agricultural parcel delineation. As shown in these figures, the UNet model seems to struggle with accurately capturing the fine details of the parcels. The boundaries are less precise, and some parcels are not fully delineated. Generally, U-shape models like UNet, using FCN like a backbone, can be less effective in segmenting large objects or regions due to the fixed-size receptive field of convolutional layers. This can lead to inconsistencies in predictions across large parcels. Furthermore, having limited contextual information, i.e., while skip connections help integrate information from different scales, UNet might not fully capture long-range dependencies and contextual relationships between pixels, potentially leading to miss-classifications specially in complex scenes 7. For FracTALResUNet, appears to perform better than UNet, capturing more detail and producing smoother boundaries, due to two reasons.

Firstly, integrating ResNet, through skip connections which allow gradients to flow more easily across the network, enables the model to learn more complex features and represent them more accurately. Secondly, the “FracTAL” component in FracTALResUNet refers to a fractal
<figure><img src="image_9.png" /><figcaption>Fig. 6. Parcels Complexity.</figcaption></figure>

Table 3

For global (resp. boundary and structure) metrics, higher (resp. lower) the value is, better the method is.

<table><thead><tr><th rowspan="2">Methods</th><th colspan="3">Global Metrics</th><th>Boundary Metrics</th><th colspan="2">Structure Metrics</th></tr><tr><th>Accuracy</th><th>F1-Score</th><th>mIoU</th><th>BDE</th><th>Over Seg</th><th>Under Seg</th></tr></thead><tbody><tr><td>CNN [2]</td><td>0.50</td><td>0.35</td><td>0.27</td><td>0.67</td><td>0.65</td><td>0.63</td></tr><tr><td>FCN [17]</td><td>0.79</td><td>0.42</td><td>0.47</td><td>0.55</td><td>0.93</td><td>0.98</td></tr><tr><td>UNet [3]</td><td>0.93</td><td>0.70</td><td>0.73</td><td>0.21</td><td>0.38</td><td>0.33</td></tr><tr><td>RR-UNet [10]</td><td>0.87</td><td>0.73</td><td>0.7</td><td>0.29</td><td>0.45</td><td>0.32</td></tr><tr><td>ResUnet-a [56]</td><td>0.96</td><td>0.72</td><td>0.76</td><td>0.25</td><td>0.41</td><td>0.30</td></tr><tr><td>FracTalResUNet [12]</td><td>0.95</td><td>0.70</td><td>0.81</td><td>0.20</td><td>0.42</td><td>0.34</td></tr><tr><td>DSCUNet [13]</td><td>0.60</td><td>0.40</td><td>0.65</td><td>0.33</td><td>0.59</td><td>0.20</td></tr><tr><td>BASNet [23]</td><td>0.90</td><td>0.90</td><td>0.86</td><td><strong>0.15</strong></td><td>0.39</td><td>0.31</td></tr><tr><td>LoRA-SAM [48]</td><td>0.90</td><td>0.70</td><td>0.81</td><td><strong>0.15</strong></td><td>0.74</td><td>0.81</td></tr></tbody></table>

Table 4

Performance comparison of nine deep learning methods for parcel delineation, evaluated using global metrics (Accuracy, F1-Score, mIoU), boundary metrics (BDE), and structure metrics (Over-segmentation, Under-segmentation). Results are categorized based on overall performance.

<table><thead><tr><th>Method</th><th>Global Metrics</th><th>Boundary Metrics</th><th>Structure Metrics</th><th>Overall Performance</th></tr></thead><tbody><tr><td>CNN</td><td>Poor</td><td>Poor</td><td>High errors</td><td>Very poor</td></tr><tr><td>FCN</td><td>Moderate</td><td>Moderate</td><td>High errors</td><td>Below average</td></tr><tr><td>UNet</td><td>Good</td><td>Good</td><td>Moderate errors</td><td>Good</td></tr><tr><td>RR-UNet</td><td>Good</td><td>Good</td><td>Moderate errors</td><td>Good</td></tr><tr><td>ResUnet-a</td><td>Very good</td><td>Good</td><td>Moderate errors</td><td>Very good</td></tr><tr><td>FracTalResUNet</td><td>Very good</td><td>Good</td><td>Moderate errors</td><td>Very good</td></tr><tr><td>DSCUNet</td><td>Moderate</td><td>Moderate</td><td>Moderate errors</td><td>Average</td></tr><tr><td>BASNet</td><td>Very good</td><td>Good</td><td>Moderate errors</td><td>Very good</td></tr><tr><td>LoRA-SAM</td><td>Very good</td><td>Good</td><td>High errors</td><td>Above average</td></tr></tbody></table>
<figure><img src="image_10.png" /><figcaption>Fig. 7. UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions for Complex Scenes I1 and I2.</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 8. UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions for parcels with Less Complexity Scenes I1 and I2.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Fig. 9. UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions for Simple parcels Complexity Scenes I1 and I2.</figcaption></figure>

upsampling technique. Fractal upsampling uses a recursive process to generate high-resolution details from low-resolution input. This helps to improve the quality of the upsampling process, which is crucial for image segmentation tasks. Moving now to the last models BASNet and LoRA-SAM. BASNet shows further improvement, more accurately delineating the parcels with greater precision. LoRA-SAM seems to produce the most accurate results, with the most precise boundaries and the closest resemblance to the ground truth. BASNet model is designed for accurate image segmentation with a focus on generating sharp and well-defined boundaries. In addition, through the Predict-Refine Architecture, this model is able to capture both global context and fine-grained details. Also including, SAM is a powerful model that excels in image segmentation. It's designed to be highly adaptable and can be fine-tuned to perform segmentation tasks on various types of images. Moreover, LoRA-SAM model as mentioned in its name, uses the LoRA method. LoRA is a technique used to fine-tune large language models (LLMs) [49]

efficiently with a smaller number of trainable parameters. The combination of LoRA with SAM likely allows for a more efficient and effective adaptation of SAM for a specific task like agricultural parcel delineation. The LoRA component helps to fine-tune the model's parameters for the task, while SAM's inherent strengths in segmentation provide a strong foundation for accurate boundary detection.

Occasionally, scenes that deviate from our initial classification based on three levels of plot complexity were encountered within our dataset, leading us to identify these scenes as either “struggle scenes” or “mixture scenes”, both of which highlight common difficulties in agricultural parcel delineation. “Struggle scenes”, in Fig. 10 are characterized by parcels with intricate shapes and boundaries, often exhibiting varying levels of color intensity, making it challenging to distinguish between adjacent areas. In contrast, a “mixture scene”, as in Fig. 11, refers to an image where parcels exhibit a combination of characteristics that make them more difficult to delineate, such as mixed land use, where
<figure><img src="image_13.png" /><figcaption>Fig. 10. UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions for Struggle Scenes I1 and I2.</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>Fig. 11. UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions for Mixture Scenes I1 and I2.</figcaption></figure>

a single parcel might contain both cultivated and non-cultivated land or a mix of different crops, leading to variations in texture, color, and shape within the parcel. Additionally, varying color intensity within a single parcel, often due to different crops, shadows, or soil variations, creates ambiguity for the models when attempting to identify boundaries.

Fig. 10 presents the performance of the same four deep learning models (UNet, FracTALResUNet, BASNet, and LoRA-SAM) on two challenging scenes. Scene I2 includes a prominent straight line at the bottom, creating a false boundary that further complicates the task. As observed in the figure, UNet and FracTALResUNet struggle to accurately delineate parcel boundaries, particularly in the bottom of Scene I1 and the centre of Scene I2. Furthermore, these models, along with BASNet, exhibit artifacts in their predictions, likely due to an imperfect upsampling process that introduces extraneous information. In contrast, while showing minor errors, LoRA-SAM demonstrates the most accurate results overall. Despite some challenges in areas with very close boundaries, this model captures generally the shape and position of the parcels more effectively than its counterparts.

Fig. 11 shows the performance of the same four models on two “mixture scenes” (I1 and I2) for agricultural parcel delineation.

By comparing the model predictions to the ground truth, we can see how each model handles these mixed scenes. The UNet model struggles to accurately delineate the parcels, especially in areas where there is mixed land use or varying color intensity within a single parcel. The model tends to merge boundaries or create false boundaries. FracTALResUNet appears to perform slightly better than UNet, capturing more detail in some areas, but it still struggles with accuracy, particularly where there are distinct color changes within a single parcel. BASNet performs relatively well on these scenes, producing more accurate boundaries than UNet and FracTALResUNet. It shows improvement in handling areas with mixed land use and varying color intensities. LoRA-SAM continues to exhibit the most accurate results, although it is not

flawless, it successfully captures the complex boundaries (in the middle and the bottom end the I2 prediction) and the variations in color intensity within the parcels (in the right side of I1 prediction), demonstrating its adaptability to challenging situations.

Figs. 12-17e present a visual analysis of the under- and over-segmentation errors exhibited by four deep learning models – UNet, FracTALResUNet, BASNet, and LoRA-SAM – in delineating agricultural parcels. The figures depict a series of complex scenes, each containing a highlighted region of interest (ROI), and illustrate how the models perform under different conditions.

Figs. 12 and 13 present under-segmentation errors for two different scenes. They demonstrate that even in less complex scenes (Fig. 10), the models still struggle with under-segmentation. This suggests that under-segmentation errors are not solely attributed to scene complexity but can also be influenced by model limitations in accurately capturing boundaries. Fig. 12 highlights a crucial point: even in scenarios with relatively simple ROIs (regular shape, homogeneous color intensity), the models still exhibit over-segmentation. This suggests that, while scene complexity plays a crucial role, the models' inherent limitations in accurately identifying boundaries are a significant factor.

Figs. 14, 16e, and 17e illustrate over-segmentation errors in the same complex scene but with different ROIs, showcasing the models' struggles to correctly delineate boundaries in complex areas. Fig. 15e, on the other hand, depicts a different complex scene with an ROI that does not exhibit any irregular shapes or distinct color variations. Despite the relative simplicity of the ROI, the models still demonstrate under-segmentation errors.

While scene complexity plays a role in the models' performance, the presence of “fake” boundaries in the ground truth image, which are absent in the ground reference mask, could also contribute significantly to over-segmentation errors. The consistent presence of under-segmentation errors, even in simpler scenes, indicates that the models face inherent limitations in accurately identifying boundaries.
<figure><img src="image_15.png" /><figcaption>Fig. 12. Under Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>
<figure><img src="image_16.png" /><figcaption>Fig. 13. Under Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>
<figure><img src="image_17.png" /><figcaption>Fig. 14. Over Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>
<figure><img src="image_18.png" /><figcaption>Fig. 15. Over Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>
<figure><img src="image_19.png" /><figcaption>Fig. 16. Over Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>
<figure><img src="image_20.png" /><figcaption>Fig. 17. Over Segmentation Error in UNet, FracTALResUNet, BASNet, and LoRA-SAM predictions.</figcaption></figure>

## 6. Discussion

U-shaped models, particularly UNet and its variants like FracTAL-ResUNet, present a compelling approach to parcel delineation. Their encoder-decoder structure, coupled with skip connections, enables the capture of multi-scale features and detailed boundary information, as evidenced by their strong performance in Fig. 18 of our appendix section Appendix A. The fractalNet in Fig. 3 nature of variants like FracTAL-ResUNet further enhances their capacity to accurately delineate parcels of diverse sizes and shapes. However, these models may not fully capture long-range dependencies, potentially limiting their effectiveness in complex scenes where contextual information is critical. Additionally, their reliance on convolutional layers can pose challenges when handling large objects or regions, potentially leading to segmentation inconsistencies.

BASNet, specifically designed for accurate image segmentation with an emphasis on sharp boundaries, leverages a predict-refine architecture to capture both global and local image details. This approach proves effective for precise parcel delineation. However, its reliance on local information might hinder performance in highly complex agricultural

landscapes characterized by overlapping objects or ambiguous boundaries. Integrating mechanisms for more robust contextual reasoning could further enhance BASNet's accuracy in such challenging scenarios.

SAM stands out as a powerful and versatile image segmentation model capable of segmenting diverse image elements based on user-defined prompts. Its impressive zero-shot generalization capabilities, demonstrated by its accurate segmentation of unseen objects (as shown in prediction number 10 of Fig. 18 in section Appendix A), make it highly suitable for agricultural applications. Fine-tuned for parcel delineation using LoRA techniques, LoRA-SAM emerges as a top performer. This is evident in its visually impressive results Figs. 7, 8, and 9 and its achievement of the highest mIoU value from Table 3.

Under-segmentation, a common issue in parcel delineation, can lead to the loss of valuable information about individual parcels, such as their size, shape, and connectivity. This can introduce bias into subsequent analyses, particularly when evaluating productivity or resource use. Conversely, over-segmentation can increase computational complexity in post-processing and analysis, generating spurious boundaries that lead to false positives when identifying specific parcels or land features.
tures. Our study, as illustrated in Figs. 12 and 13, demonstrates that under-segmentation errors are consistently challenging, even in relatively simple scenes, highlighting the need for model improvements to accurately identify boundaries. While over-segmentation errors, as shown in Figs. 14-17e, are more prevalent in complex scenes, they are also influenced by factors such as irregular shapes, color intensities, and sometimes by inaccuracies in the ground truth image.

Our quantitative analysis, detailed in the preceding section, consistently highlights UNet, FracTALResUNet, SAM, and BASNet as high-performing models across multiple evaluation metrics, indicating their suitability for accurate parcel delineation. Visual assessment further substantiates these findings. U-Net effectively delineates smooth, continuous boundaries, even for irregularly shaped parcels. FracTALResUNet excels at capturing fine-grained boundary details, accurately identifying small parcels within larger fields. SAM effectively separates individual parcels, even those in close proximity or with subtle boundary transitions. BASNet's strength lies in its precise delineation of parcel edges. These visual strengths align with the inherent advantages of each approach: U-shaped architectures excel at capturing contextual information, segmentation-based methods excel at object separation, and boundary-focused methods prioritize precise edge detection. While visual assessment provides valuable insights, we acknowledge its inherent subjectivity. Nevertheless, the presented visual evidence strongly supports our quantitative results, underscoring the significant potential of these deep learning models for accurate and efficient agricultural parcel delineation.

An improvement of the method should be the use of vector-based geospatial data for more accurate boundary delineation with a deeper link to existing data such as the Graphic Parcel Register or cadastral databases and to facilitate transfer to real-world applications that require vector-based workflows, particularly in operational land management and property mapping. Future work will explore raster-to-vector conversion methods, such as machine learning-based edge detection, and integrate hybrid (raster and vector) datasets to improve benchmarking and applicability.

## 7. Conclusion & perspectives

This research underscores the vital role of deep learning in achieving accurate and efficient agricultural parcel delineation, especially in complex landscapes. By systematically evaluating nine state-of-the-art deep learning methods across varying levels of parcel complexity and employing a comprehensive set of 14 evaluation metrics, our study provides valuable insights for both researchers and practitioners. Our findings confirm the strengths of U-shaped architectures like UNet and FracTALResUNet, highlighting their effectiveness in capturing contextual information and multi-scale features critical for delineating diverse parcel shapes and sizes. Additionally, specialized segmentation models like SAM and boundary detection networks like BASNet demonstrate considerable promise, excelling in object separation and precise boundary delineation, respectively.

While each method exhibits unique strengths and limitations, the visual evidence strongly supports our quantitative results, highlighting the significant progress made in deep learning for this task. To further refine these methods and address their limitations, future research will focus on enhancing contextual reasoning in boundary-focused methods and improving the computational efficiency of resource-intensive models. Exploring hybrid approaches that combine the strengths of different architectures could lead to even more robust and accurate parcel delineation techniques.

A crucial aspect for advancing the field of agricultural parcel delineation is investigating the impact of data quality and characteristics on model performance, particularly focusing on spatial and temporal resolution of input imagery.

Future studies should explore how varying spatial and temporal resolutions of remote sensing data affect the accuracy and efficiency of deep learning models. Higher spatial resolution could potentially improve the delineation of smaller parcels and intricate land features, while enhanced temporal resolution could enable better analysis of land-use changes and seasonal patterns.

To build upon our current findings, we propose a follow-up study that will analyze the performance of the top-performing models (UNet, FracTALResUNet, BASNet, and LoRA-SAM) using a broader set of evaluation metrics, including BDE, Under and Over Segmentation Errors metric for assessing parcel structure morphology. This extended analysis will incorporate diverse datasets with varying spatial and temporal resolutions to quantify their impact on parcel delineation accuracy. While our results are promising, grounded in reliable real-world data from the French GPR, it is important to acknowledge that their performance may vary when applied to other datasets. Although the GPR offers highly accurate ground truth for parcel boundaries, testing these methods on datasets from other regions would be beneficial to assess potential variations in performance, particularly due to regional characteristics or differences in data quality. This could provide a broader perspective on the models' applicability, ensuring their adaptability across diverse agricultural landscapes.

Building on the insights gained from our research, it is essential to acknowledge the lack of global ground truth data for parcels. In Africa, Asia, and Latin America, agricultural parcels are often smaller and more complex due to subsistence farming practices, land tenure systems, and diverse ecological conditions. These regional variations significantly affect the effectiveness of parcel delineation methods, emphasizing the need for robust, adaptable approaches. The significance of our study becomes particularly evident in light of this complexity. The French dataset used in this research, characterized by its high parcel complexity, reflects the typical European agricultural landscape shaped by historical inheritance laws and traditional farming practices. This contrasts markedly with regions like North America or Australia, where industrial farming has led to larger, more uniform parcels, in general (e.g. [57]). Consequently, our findings provide valuable insights into model performance under challenging conditions while highlighting the necessity for adaptive approaches in different agricultural contexts.

## CRediT authorship contribution statement

**Amine Hadir:** Writing – original draft, Methodology, Formal analysis, Conceptualization. **Mohamed Adjou:** Writing – review & editing, Supervision, Conceptualization. **Olga Assainova:** Writing – review & editing, Supervision, Conceptualization. **Gaëtan Palka:** Writing – review & editing, Supervision, Conceptualization. **Marwa Elbouz:** Writing – review & editing, Supervision, Project administration, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgements

This work was Co-funded by the Brittany Region (ARED Grant) and LABISEN (Research Grant).
# Appendix A. Supplementary materials

In this section, we present the evaluation of the models’ visual performances presented in Fig. 18. Each segmentation model has its own strengths and weaknesses, making it essential to consider their limitations when choosing the most suitable model for agricultural parcel delineation. Note that all the models tested used the same hyperparameters, such as ADAM optimizer with an initial value of $10^{-3}$, the loss function is Binary Cross Entropy (BCE), the number of epochs is 100.

Here’s a breakdown of potential strengths of these models:

## UNet and their derivatives - U-shape Models:

1. **Encoder-Decoder Structure:** The encoder-decoder architecture effectively extracts features at multiple scales, capturing both global context and local details. This makes U-shape models well-suited for segmenting objects with diverse sizes and shapes, such as agricultural parcels as shown in Fig. 18 (from the 4th line of the first part to 8th line in the second part in Fig. 18).

2. **Skip Connections:** Skip connections between the encoder and decoder allow for the direct transfer of fine-grained spatial information, improving boundary delineation and preserving intricate details, especially as shown in the visual performances figure (FracTalResUNet predictions of the (a) and (c) images).

3. **Adaptability:** U-shape models is a versatile model that can be easily adapted to various segmentation tasks and datasets with minimal modifications.

4. **Multi-scale Feature Learning:** For example, the fractal structure of the network enables the extraction of features at multiple scales, leading to a comprehensive understanding of the image content and potentially improving segmentation accuracy for objects with diverse sizes.

* **BASNet model:** designed for accurate image segmentation with a focus on generating sharp and well-defined boundaries. In addition, through the Predict-Refine Architecture, this model is capable to capture both global context and fine-grained details. Furthermore, the BASNet model train with a hybrid loss function by Combining BCE (Binary Cross Entropy), SSIM (Structural Similarity), and IoU losses provides a comprehensive supervision signal that encourages accurate segmentation, structural preservation, and attention to boundaries.

* **SAM** is a promptable image segmentation framework that comprises three core components: an image encoder, a prompt embedding module, and a lightweight mask decoder.

1. **Image Encoder:** SAM employs a ViT backbone as shown in (c) Fig. 2, typically ViT-H/16, pre-trained using a masked autoencoder (MAE) [58]. The encoder splits the input image into non-overlapping 16 × 16 patches, projects them into tokens, and processes them through 32 transformer layers with windowed self-attention for computational efficiency. These layers capture global contextual relationships, producing high-dimensional embeddings.

2. The prompt embedding module maps diverse prompts—including points, bounding boxes, text, or masks—into a unified latent space. Spatial prompts, such as points or boxes, are represented by fusing positional encodings with learned embeddings of their coordinates. Text prompts, while less emphasized in SAM’s default implementation, are encoded via a CLIP-like text encoder [59] in extended variants. These embeddings are integrated with the output of the image encoder, conditioning the segmentation task on the provided prompts.

3. **Mask Decoder:** The mask decoder is a transformer-based module that dynamically combines image and prompt embeddings to generate segmentation masks. It uses a two-way cross-attention mechanism: one branch attends to image features, while the other processes prompt embeddings. This interaction resolves ambiguities, such as multiple candidate objects, by producing up to three valid masks per prompt. The decoder outputs low-resolution masks (256×256), which are upsampled to the input image size using a lightweight convolutional head.

SAM achieves zero-shot generalization through training on the SA-1B dataset, which includes 11 million diverse images and 1.1 billion masks as mentioned in 3.1. SAM can segment novel objects or domains without task-specific fine-tuning using zero-shot generalization capabilities as shown in the prediction number 10 (c) images presented in the Fig. 18. For example, when prompted with a bounding box or point in remote sensing imagery, SAM leverages its encoder’s global context and decoder’s prompt-conditioned refinement to generate plausible masks, even for unseen object categories like agricultural parcels. However, its performance depends on the alignment between the prompt semantics and the image content. In our case we disabled prompts to force the model to rely only on the image encoder for feature extraction, encouraging it to learn domain-relevant patterns directly from the data, also get a fully automatic segmentation rather than interpreting user-provided hints.

As noted above, the advantage of these models is that they can segment images with a high degree of accuracy, as shown in Table 3, but they also have limitations.

## UNet and their derivatives - U-shape Models:

1. **Limited Contextual Information:** While skip connections help integrate information from different scales, U-Net might not fully capture long-range dependencies and contextual relationships between pixels, potentially leading to miss-classifications in complex scenes.

2. **Inability to Capture Complex Boundaries:** Simple CNNs are not well-suited for handling complex boundaries and fine details, potentially leading to over-segmentation or under-segmentation errors.

3. **Challenges with Large Objects:** U-shape models using FCN like a backbone can be less effective in segmenting large objects or regions due to the fixed-size receptive field of convolutional layers. This can lead to inconsistencies in predictions across large parcels.

## BASNet:

1. **Handling Complex Scenes:** While BASNet excels at capturing fine details and boundaries, it might still struggle in extremely complex scenes with multiple overlapping objects, intricate textures, or ambiguous boundaries.

2. **Limited Contextual Reasoning:** The model primarily relies on local and patch-level information for refinement. Incorporating more sophisticated mechanisms for long-range dependencies and contextual reasoning could further improve accuracy in challenging scenarios.

## While SAM and its LoRA-adapted variant demonstrate promising segmentation capabilities, several critical limitations persist.

1. First, SAM’s zero-shot performance is highly sensitive to prompt quality: inaccurate or ambiguous prompts (e.g., misplaced points or underspecified boxes) often lead to fragmented or over-segmented masks, as shown in Fig. 18(c) (middle-right parcel). This dependency undermines reliability in automated workflows where manual prompting is impractical.

2. Second, in agricultural remote sensing, parcels with homogeneous textures or spectral signatures (e.g., similar NDVI values across adjacent fields) frequently confuse SAM’s zero-shot mode, causing missed boundaries (Fig. 18(a)) or erroneous mergers (Fig. 18(c)). These errors arise from SAM’s training on RGB-centric natural images, which lack the spectral and geometric features critical for distinguishing agro-parcels. LoRA-SAM mitigates some of these issues through task-specific fine-tuning, yet challenges remain. As seen in Fig. 18(a) (central region), even
<figure><img src="image_21.png" /><figcaption>**Fig. 18.** Visual performances from our tested methods. (a), (b), (c) are tested images respectively. (1-) Ground truth boundaries. (2) → (11) CNN, FCN, U-Net, RR-UNet, ResUNet-a, FracTALResUnet, DSCUNet, BASNet, zero-shot mode of SAM, and fine-tuning mode, are respectively the results presented.</figcaption></figure>
LoRA-SAM fails to delineate parcels in complex scenes where color, texture, and shadow patterns overlap. This suggests that low-rank adaptations alone cannot fully resolve SAM's inherent biases toward natural image semantics.

3. Third, SAM's mask decoder prioritizes object-level segmentation over pixel-perfect boundaries, resulting in jagged or imprecise edges a critical drawback for cadastral mapping or precision agriculture. Finally, both SAM and LoRA-SAM exhibit limited robustness to scale variations; small parcels in high-resolution imagery are often undersegmented, while large, irregularly shaped fields may be split into disjoint regions.

These limitations highlight key research gaps: (1) the need for hybrid architectures that integrate SAM's promptable design with domain-specific feature extractors (e.g., spectral indices), and (2) improved training strategies to address ambiguous or low-contrast boundaries in agricultural landscapes.

## Data availability

The dataset used in this study is publicly available at (link to dataset). The dataset is freely accessible for research purposes, and no specific permissions are required for its use.

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