# Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts

Adrien Le Guillou, Simona Niculescu

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Adrien Le Guillou, Simona Niculescu. Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts. International Journal of Applied Earth Observation and Geoinformation, 2026, 146, pp.105069. 10.1016/j.jag.2025.105069. hal-05451372

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# Modular and adaptive implementation of Semantic Segmentation Models for Satellite Images and Open Source tools suitable for complex geographical contexts

Adrien Le Guillou<sup>a, b, ∗</sup>, Simona Niculescu<sup>a, b</sup>

ARTICLE INFO

Dataset link: https://github.com/aleguillou1/SemanticSeg4EO

Keywords:

Semantic segmentation

Remote sensing

LULC

Deep learning

Flexible approach

Ecosystems dynamics

Coastal wetlands

A B S T R A C T

Semantic segmentation, the process of assigning a semantic label to each pixel in an image, is a critical computer vision task for extracting detailed information from remote sensing data. However, its application to complex geographical contexts, such as coastal wetlands, is often constrained by the need for highly specialized implementations, class imbalance, and limited accessibility for non-specialists. This paper introduces a novel, modular, and adaptive open-source framework for semantic segmentation tailored to satellite imagery. Designed for maximum flexibility, the framework supports both binary and multi-class segmentation tasks and incorporates specific training strategies to handle severe class imbalances inherent in ecological detection, such as salt marsh mapping. The implementation provides a fully configurable pipeline that bridges the gap between Geographic Information Systems (GIS) and Deep Learning (DL). It integrates QGIS for intuitive spatial preprocessing and grid generation with a Python-based training and prediction workflow, thereby democratizing access to advanced segmentation techniques. The framework is architecture-agnostic, allowing the seamless deployment and benchmarking of various state-of-the-art encoder-decoder models, which are effective at combining multi-scale contextual information with high spatial resolution. A key contribution is the integration of a multifaceted training methodology that includes hybrid loss functions with dynamic class weighting and spectral-consistent data augmentation to ensure robust model generalization from limited and imbalanced datasets. We demonstrate the framework's efficacy and scalability through two distinct case studies: a multi-class land cover classification on the Crozon Peninsula using Pléiades and a binary salt marsh detection in the Mont-Saint-Michel Bay Sentinel-2 imagery. The results show that accurate segmentation can be achieved with modest computational resources, promoting more sustainable and ethical AI applications in environmental monitoring. This work provides a critical tool for researchers and practitioners aiming to apply advanced DL segmentation to domain specific remote sensing challenges beyond conventional benchmarks.

# 1. Introduction

Semantic segmentation with DL networks, popularized by the U-Net
architecture by Ronneberger et al. (2015), marked a turning point in
image analysis by enabling pixel-to-pixel classification while preserving
fine spatial structures. Initially developed for biomedical segmentation,
this approach was later adapted to remote sensing, where it shows
promising potential for automated land cover mapping (Maggiori et al.,
2017a,b). Encoder-decoder architectures, combined with skip connec-
tions, efficiently process the high spatial resolution of satellite imagery
while integrating multi-scale contextual information.

In the field of remote sensing, semantic segmentation has revolu-
tionized land use and land cover (LULC) mapping. Since 2019–2020,

a great deal of research has focused on increasingly complex U-net
methods to improve the mapping and automatic detection of land cover
objects (Zhang et al., 2018; Solórzano et al., 2021; Zhang et al., 2023;
Giang et al., 2020; Yang et al., 2024). Its key advantage lies in its ability
to generate detailed thematic maps from satellite or aerial imagery,
accurately distinguishing different classes such as urban areas (Zhang
et al., 2018), forests, croplands, water bodies (Yao et al., 2019), or salt
marshes (Li et al., 2021). Unlike traditional classification methods, it
preserves precise geographic object boundaries while leveraging spatial
context through convolutional neural networks. Recent approaches also
exploit time series data to improve discrimination between classes with
similar spectral signatures (Pelletier et al., 2019)

<footnote><sup>a</sup> Laboratory LETG-Brest, Université de Brest, Brest, 29200, France</footnote>

<footnote><sup>b</sup> Institut Universitaire de France (IUF), Paris, 75000, France</footnote>

<footnote>* Corresponding author.</footnote>

<footnote>E-mail addresses: adrien.leguillou@univ-brest.fr (A. Le Guillou), Simona.niculescu@univ-brest.fr (S. Niculescu).</footnote>
However, practical implementation poses several challenges. Result quality heavily depends on comprehensive and accurately labeled training datasets, requiring tedious manual annotation—a critical hurdle for non-expert users. Additionally, DL models demand significant computational resources and expertise in parameter tuning (hyperparameter selection, loss function optimization), limiting their adoption by operational practitioners. Finally, performance can vary substantially depending on landscape diversity and spectral band availability, underscoring the need for localized adaptations.

This research is driven by the observation that implementing a semantic segmentation approach, particularly for tasks involving custom labels that differ structurally from those typically used in standard convolutional neural networks (CNNs), remains a complex endeavour. While U-Net has become a foundational architecture for image segmentation due to its encoder-decoder design and its ability to capture both contextual and spatial information, its practical adaptation to user-specific datasets is often underexplored in the literature. Most existing studies and tutorials rely on pre-existing benchmark datasets such as SpaceNet (Van Etten et al., 2018; Shermeyer et al., 2020), DeepGlobe (Demir et al., 2018) Land Cover, or EuroSat Helber et al. (2018, 2019). These datasets are indeed valuable, well-curated, and have contributed significantly to advancements in remote sensing and land cover classification. However, they are inherently limited by their predefined class structures and standardized preprocessing pipelines. As a result, applying U-Net models to novel, user-generated datasets especially those with unconventional or heterogeneous label definitions—can be challenging (Zhou et al., 2014). The present approach seeks to bridge this gap by providing a flexible and applied methodology for adapting semantic segmentation models to diverse, non-standard classification tasks. This contribution is particularly relevant for practitioners and researchers aiming to deploy DL models on specialized datasets that extend beyond the scope of conventional benchmarks. Recent breakthroughs in computer vision have revolutionized LULC and ecosystems mapping, with semantic segmentation emerging as a particularly powerful approach (Onim et al., 2020; Xu et al., 2021; Neupane et al., 2021). Although traditional machine learning (ML) methods such as Random Forest (RF) and Support Vector Machine (SVM) were initially widely adopted for pixel-based classification (Ghanbari et al., 2021; Wang et al., 2004), they face inherent limitations in capturing spatial context and complex hierarchical patterns. Even conventional CNNs, despite their ability to learn discriminative features, often struggle with precise localization in dense prediction tasks. Semantic segmentation architectures, particularly U-Net and its variants, address these challenges by combining deep feature extraction with preservation of spatial resolution (Ronneberger et al., 2015; Dong et al., 2017). The key innovation lies in their encoder-decoder structure with skip connections, which maintains fine-grained details while leveraging high-level semantic understanding, a critical advantage over patch-based CNN approaches that lose spatial precision. This paradigm shift enables not just classification but dense, pixel-accurate delineation of land cover boundaries, overcoming the salt-and-pepper noise typical of Random Forest predictions (Yu et al., 2022) while providing more sophisticated feature learning than SVMs can achieve. The result is a robust framework capable of handling the spectral variability and spatial complexity inherent in remote sensing imagery, setting new standards for accuracy in environmental monitoring applications.

LULC mapping plays a crucial role in environmental monitoring (Mucher et al., 2000), urban planning (Bibby and Shepherd, 1999), and natural resource management (Lesslie et al., 2006). Accurate and up-to-date maps are essential for tracking landscape changes (Velli et al., 2019), assessing ecosystem health, and supporting policy decisions. However, generating precise LULC classifications remains challenging due to the complexity of landscapes (Sohl and Claggett, 2013), spectral similarities between different land cover types (Moharram and Sundaram, 2023), and the need for scalable, flexible methodologies. RS and ML have revolutionized this field, yet existing approaches

often struggle with computational efficiency (Ma et al., 2015), generalization across diverse regions, and ease of implementation. Current LULC mapping methods range from traditional ML techniques, such as RF (Breiman, 2001) and SVM (Vapnik, 1999), to DL models like U-Net and convolutional neural networks (CNNs) (LeCun et al., 2015). While these methods have improved classification accuracy (Shafay et al., 2018; Sothe et al., 2020; Singh et al., 2022), they come with notable limitations. Classical machine learning approaches require extensive feature engineering and struggle with complex spatial patterns (Khanal et al., 2020; Sagan et al., 2020). Deep learning models, though powerful, are often computationally intensive, require large labeled datasets (Papernot et al., 2016), and lack modularity making them difficult to adapt for different applications. Additionally, many existing frameworks are not user-friendly for non-experts, limiting their accessibility in real-world scenarios. DL can be complex to understand (Charniak (2021). Several studies have shown that the Crozon Peninsula constitutes a geographically complex environment where conventional change-detection and land-use classification methods face important limitations. Xie and Niculescu (2021) demonstrated that, although machine learning approaches such as SVM and RF, and even pixel-based PCC methods, can capture multivariate changes, they still suffer from misclassification issues particularly between vegetation and croplands and from difficulties linked to heterogeneous spatial resolutions and small, highly fragmented parcels. Their results underline the need for more robust and spatially explicit models.

In this context, the work of Le Guillou et al. (2023) on coastal wetlands similarly highlighted persistent challenges related to class confusion and spatial heterogeneity. Their findings point to semantic segmentation and flexible deep learning architectures as promising solutions, capable of better delineating complex land-cover units, reducing mixed pixel errors, and adapting to the geomorphological intricacy of coastal landscapes. Together, these studies show that such DL based segmentation approaches offer a relevant pathway to overcoming the observed limitations and improving land-cover mapping in complex territories. (Le Guillou et al., 2024)

This study presents an end-to-end workflow for LULC mapping. The primary objective is to develop a standardized yet flexible approach capable of segmenting diverse land cover classes specific to coastal wetlands (e.g., salt marshes) or more common land use classes (roads and urban areas) using a single model framework. By integrating QGIS for grid generation and spatial preprocessing with Python-based model training and prediction, we bridge the gap between GIS tools and DL, making the method accessible to non-specialists. A key innovation lies in optimizing the U-Net for minimal resource consumption, challenging the perception that DL necessarily requires energy intensive infrastructure. Through architectural choices (e.g., controlled network depth, efficient batch processing) and workflow design, we demonstrate that accurate segmentation can be achieved with modest hardware, a critical step toward more ethical and sustainable AI in environmental applications. The approach aims to democratize AI powered LULC analysis while addressing growing concerns about the environmental costs of ML.

To address these challenges, we propose a new approach that prioritizes modularity, simplicity, and efficiency. Our method combines the strengths of DL that allows for easy adaptation to different landscapes and sensor inputs. Our demonstration is based on the application of our new method to complex coastal wetlands with specific land use classes (specific ecosystems) and classes that characterize the urbanization of these areas. Unlike rigid, monolithic models, our approach enables customizable feature extraction and lightweight processing, making it suitable for both high-resolution satellite imagery and drone-based surveys. By simplifying implementation without sacrificing accuracy, we aim to bridge the gap between advanced research tools and practical, deployable solutions for land cover mapping. All this is offered using free, open-source tools: Python in command line format and
<table><caption>**Table 1**

Technical comparison between the Pleiades and Sentinel-2 satellite systems.</caption><thead><tr><th>Characteristic</th><th>Pleiades</th><th>Sentinel-2</th></tr></thead><tbody><tr><td>Operator</td><td>CNES/Airbus Defence and Space</td><td>European Space Agency (ESA)</td></tr><tr><td>Constellation</td><td>2 satellites (Pleiades-1A, Pleiades-1B)</td><td>2 satellites (Sentinel-2A, Sentinel-2B)</td></tr><tr><td>Launch Dates</td><td>2011, 2012</td><td>2015, 2017</td></tr><tr><td>Spatial Resolution</td><td>0.5 m (panchromatic), 2 m (multispectral)</td><td>10 m, 20 m, 60 m (band-dependent)</td></tr><tr><td>Spectral Configuration</td><td>5 bands (1 PAN + 4 MS: B, G, R, NIR)</td><td>13 multispectral bands (VIS, NIR, Red Edge, SWIR)</td></tr><tr><td>Swath Width</td><td>20 km</td><td>290 km</td></tr><tr><td>Revisit Frequency</td><td>1–2 days (combined constellation, off-nadir)</td><td>5 days at the equator (with both satellites)</td></tr><tr><td>Primary Use Cases</td><td>Very high-resolution mapping, urban analysis, defense, infrastructure monitoring</td><td>Agriculture, land cover monitoring, vegetation and water studies, environmental assessment</td></tr></tbody></table>

Qgis. In addition, a GitHub tutorial is available to help any user make predictions using their own data.<fnref n="1" />

## 2. Materials and methods

### 2.1. Dataset

In this study, two satellite systems Pleiades and Sentinel-2 were employed for data acquisition, offering complementary spatial and spectral characteristics relevant for DL based remote sensing tasks. The Pleiades constellation is operated jointly by CNES and Airbus, while Sentinel-2 is a mission under the European Space Agency (ESA), highlighting different institutional frameworks and design priorities behind the two systems (Table 1).

Pleiades satellites 1 A and 1B were launched in 2011 and 2012, respectively, whereas Sentinel-2 comprises satellites S2 A and S2B, launched in 2015 and 2017. The disparity in launch dates is reflected in their respective technological generations and mission goals. Pleiades provides very high-resolution imagery with a spatial resolution of 0.5 m in the panchromatic band and 2 m in multispectral bands. In contrast, Sentinel-2 offers coarser spatial resolution, varying between 10, 20, and 60 m depending on the spectral band, but compensates with significantly broader coverage and more spectral information.

The two satellite datasets used in this study were selected as analysis-ready products, meaning that most of the radiometric and geometric corrections required for scientific exploitation were already applied by the data providers. However, both products are derived from different acquisition principles and processing chains, and therefore rely on distinct preprocessing workflows. The following subsections describe the native processing applied to the Pleiades and Sentinel-2 data prior to distribution, and explain why these levels are suitable for deep learning-based image segmentation.

The Pleiades constellation acquires imagery at a native spatial resolution of 70 cm in the panchromatic band and 2.8 m in the multispectral bands (blue, green, red, NIR). Before distribution, the images undergo a resampling step to standard consumer resolutions of 50 cm (PAN) and 2 m (MS), followed by a pansharpening operation that fuses the high-resolution panchromatic band with the lower-resolution multispectral bands. This fusion increases the spatial resolution of the multispectral image while preserving its spectral fidelity, resulting in a high-resolution multispectral product suitable for object-level analysis. The dataset used in this study corresponds to an orthorectified product, in which geometric distortions induced by terrain elevation, sensor viewing geometry, and satellite attitude have been corrected using a digital elevation model (DEM). Orthorectification converts the

sensor level “PRIMARY” product into a georeferenced ortho-image with sub-pixel planimetric accuracy, allowing direct integration in a GIS environment without additional geometric refinement. Because the ortho-image already includes radiometric calibration, resampling, and co-registration, no further geometric corrections were required before tiling and standardization for model training.

The Sentinel-2 dataset corresponds to a Level-3 A monthly synthesis generated from multiple Level-2A surface reflectance images processed with the MAJA atmospheric correction chain. Level-2A products already incorporate cloud and shadow masking, aerosol correction, and top-of-atmosphere to surface reflectance conversion. However, individual scenes still contain cloud gaps and temporal discontinuities (Table 2). The Level-3 A synthesis is produced using the WASP (Weighted Average Synthesis Processor), which aggregates all cloud free observations over a 45 day temporal window and computes a per pixel weighted average for each spectral band. The weights account for proximity to clouds and shadows, aerosol optical thickness, and temporal distance to the synthesis date. Compared to single date “best pixel” compositing methods, the WASP synthesis reduces spectral noise and limits artifacts caused by abrupt changes in viewing angle or acquisition date. The resulting product is therefore radiometrically consistent, spatially complete, and almost entirely cloud free properties that make it well suited for large-scale deep learning workflows, although it does not retain precise pixel-level acquisition timing.

The combination of orthorectified Pleiades imagery and temporally composited Sentinel-2 L3A products ensures spatial accuracy, radiometric stability, and minimal data gaps, thereby reducing the preprocessing burden typically required before training a segmentation network. These products can be directly tiled, normalized, and ingested into the model without the need for cloud masking, DEM based correction, or atmospheric recalibration, which increases reproducibility and avoids introducing sensor specific preprocessing bias into the learning process.<fnref n="2" />

From a spectral perspective, Pleiades offers five bands, one panchromatic and four multispectral whereas Sentinel-2 delivers a richer spectral profile with thirteen multispectral bands, enabling finer discrimination of surface features across different wavelengths. In terms of spatial coverage, the swath width of Pleiades is approximately 20 kilometers, suitable for localized high-detail mapping, while Sentinel-2 covers a much larger swath of 290 kilometers, facilitating large-scale monitoring applications.

The revisit time also differs, with Pleiades achieving a revisit frequency of one to two days when both satellites are operational. Sentinel-2 provides a revisit time of approximately five days at the

<footnote n="1">¹ https://github.com/aleguillou1/SemanticSeg4EO</footnote>

<footnote n="2">² More info about L3 A format here : https://www.cesbio.cnrs.fr/multitemp/theias-sentinel-2-l3a-monthly-cloud-free-syntheses/</footnote>
<table><caption>**Table 2**
Summary of native preprocessing applied to the satellite datasets used in this study.</caption><thead><tr><td>Sensor</td><td>Processing Level</td><td>Main Operations Performed by Provider</td><td>Resulting Product Properties</td></tr></thead><tbody><tr><td>Pleiades</td><td>Orthorectified multispectral product</td><td>Resampling to 50 cm (PAN) and 2 m (MS); pansharpening; radiometric calibration; orthorectification using DEM</td><td>High-resolution georeferenced image suitable for GIS and object-level analysis</td></tr><tr><td>Sentinel-2</td><td>Level-3A monthly synthesis</td><td>Atmospheric correction (MAJA); cloud/shadow masking; multi-date aggregation (WASP weighted average); directional normalization</td><td>Cloud-free multispectral composite with consistent radiometry and full spatial coverage</td></tr></tbody></table>

equator with the combined capacity of S2 A and S2B, enabling consistent temporal monitoring on a global scale. These revisit characteristics are particularly relevant for time-sensitive applications such as vegetation monitoring or urban change detection.<fnref n="3" />

## 2.2. Methodology

### 2.2.1. Image pre-processing

This approach proposes the implementation of various satellite data (Pleiades and Sentinel-2) for the predictions to be made. For preprocessing, only two data sets are required. We begin with two core inputs: a satellite image in GeoTIFF format (either RGB or multispectral) (here we use Pléiades and Sentinel-2 images), and a corresponding label file generated by rasterizing vector salt marsh label (for example). The label file must share the exact same geographic extent, resolution, and projection as the satellite image, with pixel values representing class IDs (e.g., 1 = forest, 2 = urban). To mitigate the effects of imperfect or incomplete ground truth (GT) annotations and ensure methodological reproducibility, we implemented a spatially explicit, grid-based patch extraction strategy. Regular grid creation was performed using QGIS's "Create Grid" tool, with cell dimensions matching the desired patch size (e.g., 224 × 224 pixels) and adapted to the sensor's spatial resolution (Fig. 1). To address potential residual misalignment at the grid edges and preclude the extraction of incomplete data, non-overlapping edge cells were systematically removed. An auto-increment field ("ID") was added using QGIS's "Add Autoincrement Field" tool, assigning sequential numeric identifiers to each cell. These IDs were then utilized by a Python script that iterates over the grid, calculates the spatial coordinates (bounding box) of each geometry, and crops the imagery and corresponding labels to the defined extents. This approach allows for selective curation of training samples permitting oversampling of underrepresented classes such as salt marshes and limits the propagation of annotation noise into the learning process.

For example, in the patch extraction for our semantic segmentation models benchmarks, we generate a square grid where each cell becomes a 224 × 224 pixel patch. The input and output dimensions of 224 × 224 pixels were selected as an optimal trade-off to handle multi-scale feature extraction across varying spatial resolutions (from 50 cm to 20 m). While higher resolutions (e.g., 512 × 512) could theoretically preserve finer details, they would disproportionately represent large Landsat patches in the training dataset, marginalizing smaller-scale habitat features critical for ecological mapping. Conversely, lower resolutions would fail to capture fine-grained patterns. The 224 × 224 size ensures balanced representation across scales, enabling the model to generalize effectively for both high-resolution (e.g., aerial imagery) and moderate-resolution (e.g., Landsat) inputs without biasing the training toward dominant spatial extents. This choice aligns with computational efficiency and memory constraints while maintaining discriminative power for habitat classification tasks.

The grid size is calculated based on the image's ground resolution. For example:

* With 50 cm resolution imagery: 224 pixels = 112 m (224 × 0.5 m)

* With 10 m resolution imagery: 224 pixels = 2.24 km (224 × 10 m)

In case of a 0.5 m resolution image, like for Pleiades (Fig. 2), the grid is created with 112 m × 112 m cells to perfectly match the desired input size. Due to potential edge misalignment between the grid and image, we manually remove non-overlapping grid cells at the right/bottom borders (visible in Figs. 3–5). The grid is then numbered sequentially using QGIS's "Add Autoincrement Field" tool. Before training, pixel values are standardized:

* Proper alignment between image patches and labels

* Correct class representation in masks

* Appropriate value ranges after normalization

The QGIS grid system provides a robust framework for optimizing training data preparation in land cover segmentation tasks. Its primary advantage lies in enabling direct visual control and spatial management of input patches prior to model implementation, ensuring both geometric and thematic quality. For rare or spatially constrained land cover classes such as salt marshes, vernal pools, or urban infrastructure—the grid allows selective patch curation to address class imbalance. By systematically excluding grid cells devoid of target features while retaining representative examples, researchers can artificially enhance the prevalence of minority classes in the training dataset. This approach proves particularly valuable when working with medium-resolution imagery (e.g., Sentinel-2 at 10m/pixel), where target features may occupy only fractions of individual patches. The grid's spatial explicitness also facilitates manual verification of label alignment and spectral consistency across the study area, reducing error propagation during model training. Furthermore, this method maintains geographic integrity while offering flexibility to adjust sampling strategies based on specific class distributions, making it superior to random or sliding-window patch extraction for ecologically sensitive applications requiring precise spatial representation. This end-to-end process ensures the U-Net receives perfectly aligned, standardized input patches while maintaining the geographic integrity of the original data. The modular design accommodates various satellite sensors and resolutions through adjustable parameters in both the QGIS grid creation and Python extraction steps (Figs. 2 and 3).

All extracted patches are subjected to rigorous preprocessing, including pixel-wise standardization and verification of label integrity to ensure consistent class representation and numerical ranges. By enforcing precise spatial alignment between imagery and annotations, the workflow minimizes residual misregistration and inconsistencies inherent to the GT data. Collectively, these procedures constitute a robust framework for mitigating label noise, thereby enhancing the model's ability to learn discriminative representations and generalize effectively despite imperfect or incomplete annotations.

<footnote n="3">³ More information for Pleiades here https://space-solutions.airbus.com/imagery/our-optical-and-radar-satellite-imagery/pleiades and more information for Sentinel-2 here https://www.esa.int/Applications/Observing_the_Earth/Copernicus/Sentinel-2</footnote>
<figure><img src="image_3.png" /><figcaption>Fig. 1. A Grid-Based sampling strategy facilitating systematic dataset construction and Patch Selection (here, focus on salt marsh) from Sentinel-2 L3A imagery and Qgis.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>Fig. 2. Patch and labels extraction from satellite Pleiades imagery.</figcaption></figure>

### 2.2.2. Efficient semantic segmentation architectures for remote sensing

Convolutional neural networks designed for dense prediction tasks typically rely on a combination of hierarchical feature extraction and spatial detail preservation to generate pixel-wise semantic maps. Among these architectures, the U-Net model (Ronneberger et al., 2015) has emerged as a foundational framework, largely due to its ability to integrate multi-scale contextual information with precise localization. U-Net follows a symmetric encoder–decoder structure: the encoder compresses the input into increasingly abstract representations through

repeated convolutions, each expressed as

$$ \mathrm{Conv}(x) = W * x + b, \qquad (1) $$

followed by a non-linear activation such as the Rectified Linear Unit (ReLU),

$$ \mathrm{ReLU}(x) = \max(0, x) = \begin{cases} x & \text{if } x \ge 0, \\ 0 & \text{otherwise,} \end{cases} \qquad (2) $$
<figure><img src="image_5.png" /><figcaption>Fig. 3. Workflow for the implementation of different semantic segmentation models.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>Fig. 4. Comparison between U-Net and U-Net++.</figcaption></figure>

which enables the network to model complex decision boundaries. Downsampling operations, typically 2 × 2 max-pooling, progressively reduce spatial resolution while expanding the depth of feature maps, allowing the encoder to capture global structural patterns. At the network's bottleneck, the representation reaches maximal semantic abstraction before being progressively reconstructed by the decoder (Fig. 4). The expansive path reverses the process through upsampling layers that restore spatial resolution and refine object boundaries. A key innovation in U-Net lies in its skip connections, which concatenate encoder features with decoder activations at matching resolutions. This mechanism compensates for information lost during downsampling by directly injecting high-resolution structural cues into the reconstruction process, thereby improving the delineation of fine edges and small objects. The final segmentation map is produced by a 1 × 1 convolution followed by a softmax activation, which assigns each pixel to the most likely semantic class:

$$ \hat{y}(p) = \mathrm{Softmax}(z(p)). \quad (3) $$

While this architecture has demonstrated strong performance across biomedical and remote sensing applications, its simplicity also reveals intrinsic limitations when dealing with complex natural or urban scenes. In particular, the fixed encoder-decoder hierarchy restricts the model's capacity to aggregate multi-scale contextual information, and the direct fusion of encoder and decoder features can lead to a semantic mismatch between low-level and high-level representations. These constraints motivate the development of more advanced variants such as U-Net++, PAN, or DeepLabv3+ that refine feature aggregation

mechanisms, enhance multi-scale processing, or modify skip connection strategies to better address the challenges of semantic segmentation in satellite imagery.

Semantic segmentation of satellite imagery presents a unique computational challenge, requiring architectures that can simultaneously model global contextual information for accurate class identification and fine-grained spatial details for precise boundary delineation. The classical U-Net architecture has served as a foundational model in this domain. Its streamlined symmetric structure, particularly when paired with pre-trained backbones (e.g., ResNet), offers significant advantages in terms of computational efficiency and inference speed. This allows the model to achieve competitive convergence even on limited annotated datasets, making it a robust baseline for resource constrained scenarios. However, despite these practical benefits, the architecture exhibits intrinsic limitations in complex geographical contexts. In particular, the direct fusion of encoder and decoder features often suffers from a semantic gap, as encoder features at a given level $l$ may not align well with decoder features at the same level which have undergone additional non-linear transformations. This misalignment can impede effective information propagation. Moreover, standard U-Net's fixed pyramid structure and reliance on basic convolutional operations constrain its ability to efficiently capture multi-scale contextual information, which is crucial for satellite images containing objects of vastly different sizes (Fig. 5). To overcome these limitations, several advanced variants have been developed. U-Net++ (Zhou et al., 2020) introduces a redesigned skip pathway that bridges the semantic gap through dense, nested convolutional blocks. Instead of direct connections, U-Net++ generates a set of feature maps {$x_{i,j}$}, where i indexes the
<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison between Pyramid Attention Network (PAN) and DeepLabv3+.</figcaption></figure>

downsampling layer and j indexes the upsampling layer, and each node in the decoder aggregates features from multiple encoder levels according to: where C(·) represents a convolutional block, U(·) denotes an upsampling operation, and [·] indicates concatenation. This dense connectivity promotes multi-scale feature fusion and improves gradient flow, enhancing segmentation accuracy for objects of varying sizes and accelerating convergence.

The Pyramid Attention Network (PAN) (Li et al., 2018) further enhances feature representation using a dual-path strategy. PAN combines a Feature Pyramid Module to capture multi-scale context with a Global Attention Upsample (GAU) module to refine features. The GAU selectively weights skip connexion features before fusion, producing an output O defined as:

$$O = \text{Conv}(\mathcal{V}(F_{\text{high}}) + \mathcal{V}(F_{\text{high}}) \odot \sigma(\text{Conv}(F_{\text{low}}))) \quad (4)$$

where $F_{\text{high}}$ and $F_{\text{low}}$ are high-level and low-level feature maps, ⊙ denotes element-wise multiplication, and $\sigma$ is the sigmoid function. This selective propagation allows PAN to balance accuracy and computational efficiency by emphasizing the most informative spatial details.

DeepLabv3+ (Chen et al., 2018b) addresses multi-scale context directly in the encoder via atrous (dilated) convolutions, defined for a 1D signal as:

$$y[i] = \sum_{k=1}^{K} x[i + r \cdot k]w[k] \quad (5)$$

where r is the dilation rate, enabling a larger receptive field without increasing parameters or losing resolution. DeepLabv3+ leverages Atrous Spatial Pyramid Pooling (ASPP) to extract multi-scale context and combines a lightweight decoder with skip connections to refine the segmentation mask by merging high-level and spatially detailed low-level features.

A key factor in the efficiency and performance of these modern architectures is their compatibility with pre-trained backbones such as ResNet-34 trained on ImageNet. By leveraging generalized feature representations, these models can significantly reduce training time and data requirements while achieving superior performance on specialized tasks such as multi-spectral satellite image analysis. By integrating advanced skip connection schemes, attention mechanisms, multi-scale processing, and transfer learning, U-Net++, PAN, and DeepLabv3+ provide a favorable trade-off between segmentation accuracy and computational efficiency, making them the current benchmarks for complex geographical analysis (see Table 3).

$$x_{i,j} = \begin{cases} C(x_{i-1,j}) & j = 0 \\ C([x_{i,k}]_{k=0}^{j-1}, \mathcal{V}(x_{i+1,j-1})) & j > 0 \end{cases} \quad (6)$$

Beyond their structural differences, these architectures exhibit distinct operational trade-offs regarding data efficiency and robustness to class imbalance. The standard U-Net, characterized by a lighter parameter space, generally proves more robust on smaller datasets where overparameterized models risk overfitting, yet it may struggle to maintain

semantic consistency in highly diverse scenes. Conversely, DeepLabv3+ typically yields superior performance on large-scale benchmarks due to its extensive receptive field, but often requires vast amounts of annotated data to converge effectively without biasing toward majority classes. U-Net++ and PAN occupy a middle ground: the dense connections of U-Net++ facilitate gradient flow, potentially stabilizing training on imbalanced data, while PAN's attention mechanisms help focus on small, informative features often belonging to minority classes. Consequently, the primary objective of this study is to empirically evaluate these behaviors. By standardizing the encoder backbone to ResNet34 across all architectures, we isolate the impact of decoder designs to determine which configuration offers the best resilience for semantic segmentation tasks characterized by highly imbalanced satellite datasets.

### 2.2.3. A unified and parametric training framework

To enhance the performance of each segmentation architecture without substantially increasing training time, several regularization and optimization strategies were employed, while maintaining full flexibility through parameterizable code. A dropout layer with rate $p = 0.5$ was applied in the decoder blocks to mitigate overfitting and improve model generalization. The effect of dropout on an activation x can be formally expressed as:

$$\bar{x}_i = \begin{cases} 0 & \text{with probability } p \\ \frac{x_i}{1-p} & \text{with probability } 1-p \end{cases} \quad (7)$$

An early stopping callback was implemented to monitor validation metrics during training, ensuring that only the best performing model iterations were retained. The callback tracked both the validation loss $L_{\text{val}}$ and the mean Intersection over Union (mIoU). Training was stopped if no improvement greater than a minimum threshold $\min_{\text{delta}} = 1 \times 10^{-4}$ occurred over a patience period of 15 epochs, and the best model was saved automatically.

The optimization strategy was further refined by employing the AdamW optimizer with a small weight decay $\lambda = 10^{-4}$, and a learning rate scheduler that dynamically reduced the learning rate $\eta$ if the validation loss plateaued:

$$\eta_{t+1} = \begin{cases} \eta_t \cdot \text{factor} & \text{if } L_{\text{val}} \text{ has not decreased for patience epochs} \\ \eta_t & \text{otherwise} \end{cases} \quad (8)$$

where factor = 0.5 and the minimum learning rate was set to $\eta_{\min} = 1 \times 10^{-6}$.

To ensure reproducibility and broad applicability across diverse remote sensing scenarios, we developed a highly modular training framework designed to be input-agnostic. Unlike rigid implementations fixed to RGB inputs, our pipeline allows for the dynamic instantiation of model architectures regardless of the input spectral depth ($C_{in}$) or the target semantic classes ($N_{classes}$). This flexibility is critical for this study, allowing seamless transitions between standard 3-channel imagery (or 4-channel imagery in this case) and 10-channel Sentinel-2
<table><caption>**Table 3**

Comparative summary of U-Net, U-Net++, PAN, and DeepLabv3+ for satellite image segmentation.</caption><thead><tr><th>Architecture</th><th>Encoder/Backbone</th><th>Skip Connections</th><th>Multi-scale Handling</th><th>Attention</th><th>Key Advantages</th></tr></thead><tbody><tr><td>U-Net (Ronneberger et al., 2015)</td><td>Custom CNN/Resnet</td><td>Direct, symmetric</td><td>Limited (fixed pyramid)</td><td>None</td><td>Simple, effective, widely used; preserves spatial details</td></tr><tr><td>U-Net++ (Zhou et al., 2020)</td><td>ResNet or custom</td><td>Dense, nested connections</td><td>Multi-level fusion via nested blocks</td><td>None</td><td>Bridges semantic gap; better multi-scale feature fusion; faster convergence</td></tr><tr><td>PAN (Li et al., 2018)</td><td>ResNet or custom</td><td>Selective, weighted skip connections</td><td>Feature Pyramid Module captures multiple scales</td><td>GAU (Global Attention Upsample)</td><td>Balances accuracy and efficiency; highlights informative features; effective for objects of varying sizes</td></tr><tr><td>DeepLabv3+ (Chen et al., 2018b)</td><td>ResNet/Xception</td><td>Skip connections from encoder to decoder</td><td>Atrous Spatial Pyramid Pooling (ASPP)</td><td>Implicit via ASPP and decoder fusion</td><td>Strong multi-scale context capture; lightweight decoder; fast convergence with pre-trained backbones</td></tr></tbody></table>

multispectral data without structural redesign. The training strategy is bifurcated to address the specific challenges of binary (salt marsh detection) versus multi-class segmentation. The binary detection of specific ecological features, such as salt marshes, often suffers from severe class imbalance, where the target class represents a small fraction of the total pixels. To mitigate overfitting on the majority class (background), we implemented a multi-faceted regularization strategy:

1. **Hybrid Loss Function:** We employ a composite loss function combining Binary Cross-Entropy (BCE) and Dice Loss:

$$ \mathcal{L}_{\text{total}} = \mathcal{L}_{\text{BCE}}(y, \hat{y}) + \lambda(1 - \text{Dice}(y, \hat{y})) \quad (9) $$

Critically, the BCE component incorporates a positive class weight ($w_{pos}$), dynamically calculated from the training set statistics to penalize errors on the minority class more heavily.

2. **Aggressive Multi-Channel Augmentation:** Given the limited size of labeled datasets, we utilize an aggressive data augmentation pipeline specifically adapted for multispectral data. Beyond standard geometric transformations (flips, rotations), we introduce spectral-consistent noise injection and channel grouping augmentations. This ensures that synthetic variations respect the physical correlation between spectral bands (e.g., perturbing Red-Edge bands coherently) rather than treating them as independent RGB channels.

Training utilizes the AdamW optimizer with weight decay to prevent convergence to sharp minima. We employ a ReduceLROnPlateau scheduler that progressively decays the learning rate (e.g., by a factor of 0.5) when validation metrics stagnate. This allows the model to make large gradient updates in early epochs and refine decision boundaries with high precision in later stages.

For binary class segmentation, the framework shifts to a standard Cross-Entropy and Dice combination, utilizing Softmax activations. A key feature of our approach is the robust inference mechanism for large satellite tiles. To prevent boundary artifacts common in patch-based processing, we implement a sliding window prediction strategy with a significant overlap (typically 25%–50%) and Gaussian weighted reconstruction. This ensures that predictions at the center of patches contribute more to the final map than edges, yielding seamless segmentation maps regardless of the satellite scene's dimensions. All hyperparameters including dropout rate, patience, minimum improvement threshold, learning rate, weight decay, and scheduler parameters are fully configurable in the code, allowing researchers to adapt the training process to different datasets or computational constraints. This combination of dropout, adaptive learning rate scheduling, and callbacks effectively stabilizes training, accelerates convergence, and guarantees that the final models achieve their optimal metrics without incurring additional computational cost. In practice, this framework ensures that each architecture U-Net, U-Net++, PAN, and DeepLabv3+ benefits from improved generalization, optimized performance, and a flexible,

reproducible training pipeline suitable for multi-spectral satellite image segmentation tasks.

To rigorously evaluate and compare model architectures despite the constraints of a limited and imbalanced dataset, we extended our evaluation protocol beyond a static train-validation split by implementing a K-Fold Cross-Validation strategy. Given that a single split can introduce selection bias—where a model might perform well simply due to a favorable random partition this approach provides a statistically robust assessment of generalization capability. The dataset is randomly partitioned into K distinct subsets or “folds” (with K = 5 in our standard configuration). The training process is iterative: for each fold $k \in \{1, ..., K\}$, the model is trained on K − 1 folds and validated on the remaining kth fold. This methodology ensures that every image tile contributes to the validation metrics exactly once. We aggregate the results to report not only the mean performance ($\mu$) but also the standard deviation ($\sigma$) and the 95% Confidence Intervals (CI) for key metrics such as Intersection over Union (IoU) and F1-score:

$$ \text{CI} = \mu \pm t_{\alpha/2, n-1} \left( \frac{\sigma}{\sqrt{n}} \right) \quad (10) $$

where n is the number of folds. This statistical rigor allows us to quantify the stability of the architecture; a high mean IoU with a low standard deviation indicates a model that generalizes reliably across diverse spatial distributions of salt marshes.

Finally, to maximize the utility of our limited labeled data for operational inference, once the cross-validation confirms an architecture's stability, the final production model is retrained on the entire dataset, leveraging the full spectrum of available training samples without holding back data for validation.

### 2.2.4. Image post-processing: Patch-based prediction and image reconstruction

To enable scalable inference on large-scale satellite imagery of varying resolutions (e.g., Sentinel-2 tiles, Pleiades, or SPOT scenes), we developed a memory-efficient sliding window pipeline. This workflow ensures end-to-end control of the processing chain, handling input images of arbitrary spatial dimensions and spectral depth $C_{in}$ without the need for physical tiling prior to inference. The input raster is processed through a sliding window mechanism that extracts overlapping patches of fixed size (typically 224 × 224 pixels). Unlike simple tiling, our approach incorporates a configurable stride and overlap parameter (e.g., 64 to 128 pixels). This overlap is critical for the reconstruction phase: to eliminate the “checkerboard” artifacts common in patch-based segmentation, the system employs an advanced aggregation strategy. For binary tasks, a Gaussian-weighted fusion is applied, where predictions in the center of a patch contribute more to the final map than those at the edges. For multi-class problems, a robust majority voting scheme is implemented in overlapping regions to ensure semantic consistency. Computationally, the pipeline leverages efficient windowed reading via Rasterio, allowing the processing of
<caption>**Table 4**</caption>

Training time and epoch comparison. For Binary Segmentation, values represent the **average per fold** across the 5-fold cross-validation. (Note: Training times are expressed in decimal minutes).

<table><thead><tr><th>Model</th><th>Segmentation Type</th><th>Avg. # Epochs</th><th>Est. Training Time (min)</th></tr></thead><tbody><tr><td rowspan="2">PAN</td><td>Multi-class</td><td>61</td><td>30.00</td></tr><tr><td>Binary</td><td>48.4</td><td>11.41</td></tr><tr><td rowspan="2">U-Net</td><td>Multi-class</td><td>58</td><td>36.88</td></tr><tr><td>Binary</td><td>53.4</td><td>12.58</td></tr><tr><td rowspan="2">U-Net++</td><td>Multi-class</td><td>55</td><td>30.65</td></tr><tr><td>Binary</td><td>59.8</td><td>14.78</td></tr><tr><td rowspan="2">DeepLabV3+</td><td>Multi-class</td><td>55</td><td>25.09</td></tr><tr><td>Binary</td><td>57.8</td><td>14.14</td></tr></tbody></table>

**Dataset specifications:**

* Binary segmentation: 5-Fold CV (~270 Train/68 Val per fold)

* Multi-class segmentation: Single Split (1320 Train/352 Val)

* Common settings: Early Stopping (Patience=15), AdamW, ReduceLROnPlateau

multi-gigabyte scenes even on GPUs with limited VRAM. While the default pipeline produces a seamless merged raster, an optional debugging mode allows for the serialization of individual patches as georeferenced TIFFs, facilitating granular quality control in GIS software. Finally, the output encoding is dynamically optimized (Uint8 for standard classes, Uint16 for complex taxonomies) to balance storage efficiency with classification depth, preserving the original georeferencing and coordinate system of the source imagery.

## 3. Results

All experiments were conducted using a workstation equipped with an NVIDIA RTX 3080 GPU, 64 GB of RAM, and an Intel Core i7-12700F processor. Because computational efficiency, training time, and the comparability of benchmark results were central concerns in this study, all models were trained using a ResNet-34 encoder. To further ensure efficient convergence and stable optimization, we relied on pre-existing encoder architectures with weights pre-trained on ImageNet.

The training dynamics presented in Table 4 reveal distinct convergence behaviors dictated by architectural complexity and task specificity. In the binary segmentation task, U-Net++ exhibits the most prolonged training phase (59.8 epochs), closely followed by DeepLabV3+ (57.8 epochs). This extended duration likely reflects the contribution of nested skip pathways and ASPP modules, which facilitate continuous feature refinement and allow the models to maintain learning gradients longer before triggering early stopping. U-Net occupies a middle ground (53.4 epochs), suggesting a stable optimization trajectory typical of its balanced encoder-decoder design. Conversely, PAN records the lowest average epoch count for the binary task (48.4 epochs) but, paradoxically, requires the highest number of epochs for multi-class segmentation (61 epochs). This inversion suggests that while compact architectures like PAN may converge rapidly and potentially prematurely on simpler binary tasks due to limited capacity, they struggle to resolve the more complex feature space of multi-class segmentation, thereby prolonging the optimization process. Overall, heavier architectures (U-Net++, DeepLabV3+) appear to sustain learning more consistently across folds in the binary setting, whereas lighter models exhibit higher sensitivity to task complexity.

### 3.1. Statistical evaluation of the different semantic segmentation model result

Table 5 presents a comparative evaluation of four widely used semantic segmentation architectures U-Net, U-Net++, Pyramid Attention Network (PAN), and DeepLabV3+ applied to two distinct tasks: a binary segmentation problem for salt-marsh detection and a multi-class land-use segmentation on the Crozon Peninsula. These case studies differ

<caption>**Table 5**</caption>

Comprehensive performance comparison. For Binary Segmentation, results represent the Mean ± Standard Deviation across 5 folds. Best results are highlighted in bold.

<table><thead><tr><th>Metric</th><th>U-Net</th><th>U-Net++</th><th>PAN</th><th>DeepLabV3+</th></tr></thead><tbody><tr><td>Parameters (M)</td><td>24.4</td><td>26.1</td><td><strong>21.5</strong></td><td>22.4</td></tr><tr><td colspan="5"><strong>Binary Segmentation (Salt Marsh - 5-Fold Cross-Validation)</strong></td></tr><tr><td>Mean mIoU</td><td>0.574 ± 0.04</td><td><strong>0.592 ± 0.06</strong></td><td>0.527 ± 0.10</td><td>0.574 ± 0.06</td></tr><tr><td>Mean F1-Score</td><td>0.729 ± 0.03</td><td><strong>0.742 ± 0.05</strong></td><td>0.685 ± 0.08</td><td>0.727 ± 0.05</td></tr><tr><td colspan="5"><strong>Multi-Class Segmentation (Land Use - Crozon Peninsula)</strong></td></tr><tr><td>Validation mIoU</td><td>0.696</td><td>0.702</td><td><strong>0.709</strong></td><td>0.708</td></tr><tr><td>Validation F1</td><td>0.811</td><td>0.815</td><td><strong>0.820</strong></td><td>0.816</td></tr><tr><td>Test Recall</td><td><strong>0.867</strong></td><td>0.863</td><td>0.866</td><td>0.839</td></tr><tr><td>Test F1</td><td>0.759</td><td>0.799</td><td><strong>0.802</strong></td><td>0.749</td></tr></tbody></table>

substantially in dataset size, class balance, and complexity, factors that strongly influence the observed performance patterns. The 5-Fold Cross-Validation results for the binary salt marsh detection task provide a rigorous assessment of model robustness. U-Net++ achieved the highest performance, with a Mean F1-score of 0.742 ± 0.05 and Mean mIoU of 0.592 ± 0.06, outperforming the standard U-Net (0.729 ± 0.03) (Fig. 6). This suggests that the nested skip connections in U-Net++ effectively bridge the semantic gap, which is critical when segmenting complex, irregular ecological features like salt marshes. However, the standard U-Net demonstrated the highest stability, exhibiting the lowest standard deviation (±0.03 for F1) compared to other architectures. This indicates that while U-Net++ has a higher peak performance, U-Net is less sensitive to data variations across different folds. Conversely, PAN showed the lowest performance (0.685±0.08) and highest variance (±0.10 mIoU), confirming that attention mechanisms may struggle to converge stably on smaller, highly imbalanced datasets without more extensive pre-training or data volume. DeepLabV3+ offered performance comparable to U-Net but with slightly higher variance (Zhou et al., 2020). By contrast, PAN demonstrated the lowest performance on this task, while DeepLabV3+ failed to surpass the simpler U-Net architectures despite its advanced design. PAN, though parameter efficient, relies heavily on attention-based feature fusion mechanisms that typically require more diverse training samples to reach stable performance levels (Li et al., 2018). DeepLabV3+, which integrates atrous spatial pyramid pooling (ASPP), generally excels on large-scale datasets with abundant examples of complex classes, but its advantages diminish when data are limited and the target class is poorly represented a tendency also observed in previous ecological mapping studies (Zhou et al., 2023). Overall, the results highlight the dominant impact of dataset size and class imbalance in the binary task, where architectures of moderate complexity, such as U-Net and U-Net++, appear better suited to handling sparse minority classes (Kattenborn et al., 2021; Zhu et al., 2017a).

In the multi-class land-use segmentation task, trained on a considerably larger and more balanced dataset, all architectures achieve higher performance. PAN obtains the best validation results, with a mIoU of 0.709 and an F1-score of 0.820, illustrating the effectiveness of its attention mechanisms and multi-scale feature aggregation when the dataset provides sufficient variability. DeepLabV3+ performs similarly, albeit slightly below PAN, which is consistent with previous benchmarks comparing these architectures for land-cover mapping. The test phase results further support this pattern: all models display high recall values (≥ 0.84), with U-Net achieving the highest recall at 0.867. However, PAN reaches the best overall performance, with a test F1-score of 0.802, closely followed by U-Net++ (0.799). These outcomes underline the advantage of architectures that integrate richer contextual modeling when dealing with the spatial heterogeneity typical of multi-class remote-sensing applications. The comparison across tasks reveals noteworthy divergences in model behavior. Differences in dataset size and class distribution emerge as critical determinants of performance.
<figure><img src="image_8.png" /><figcaption>**Fig. 6.** Performance stability assessment using 5-Fold Cross-Validation across four architectures. Each panel displays the validation curves (IoU, F1, Loss) over epochs, followed by per-fold performance (Bar charts) and statistical distribution (Boxplots) for: (A) U-Net++; (B) Pyramid Attention Network (PAN); (C) Deeplabv3+; (D) U-Net. The boxplots (bottom right of each panel) visually quantify the trade-off between metric variance and mean accuracy.</figcaption></figure>

PAN and DeepLabv3+ perform best when abundant, well-balanced training data are available, which aligns with prior findings in Earth observation research (Zhu et al., 2017b). Conversely, U-Net and U-Net++ demonstrate greater robustness in constrained data scenarios, where excessive architectural depth or complexity can lead to overfitting. The binary task also exhibits more fragile generalization capabilities, as indicated by the pronounced gap between validation and test F1-scores. This phenomenon is characteristic of ecological habitat mapping tasks, where minority classes such as salt marshes often exhibit limited spatial footprints and high variability (Durden et al., 2021; Bressan et al., 2022; Sierra et al., 2025). Finally, the comparison of parameter counts with model performance provides additional insights. PAN, the most parameter efficient model (21.5M parameters), achieves the best results in the multi-class scenario, illustrating a favorable balance between architectural efficiency and predictive capacity. In contrast, U-Net++, which has the highest parameter count (26.1M), achieves the best validation results on the binary task, supporting the idea that model capacity must be carefully matched to data availability and complexity. This observation is consistent with broader comparative studies in semantic segmentation and medical imaging, where optimal performance typically arises from aligning model expressiveness with the characteristics of the training dataset (Minaee et al., 2022; Hesamian et al., 2019).

## 3.2. Comparative spatial and qualitative analysis of semantic segmentation models

### 3.2.1. Land cover classification using Pléiades imagery in Crozon Peninsula (Brittany, France)

The multi-class segmentation capabilities of the framework were evaluated on the Crozon Peninsula (Brittany, France) using pansharpened Pleiades-1B imagery at 50 cm resolution. This coastal landscape presents a challenging environment characterized by complex vegetation patterns including mixed deciduous-coniferous forests and dense ericaceous shrublands interspersed with fragmented urban settlements. Ground truth data were derived from the OCS GE (Occupation du Sol

à Grande Échelle) database. Although this reference system provides detailed parcel-level annotations with high accuracy, we aggregated the original 23 land cover classes into 6 target categories to maintain semantic consistency for the segmentation task.

The qualitative analysis of the predictions demonstrates that the different models have a strong capacity to capture the underlying patterns for fine-grained semantic segmentation, even when trained with imperfect ground truth labels. An illustrative example concerns the distinction between forest and grassland/meadow classes, which is not always consistent in the ground truth data due to temporal discrepancies between the land cover database (OCS) and the Pléiades imagery. In some cases, areas labeled as forest in the reference data correspond to more open vegetated surfaces in the satellite image, or vice versa, reflecting land cover evolution over time. Despite these inconsistencies, the semantic segmentation models demonstrate a strong ability to delineate these two land cover types. This is particularly notable given that the transition between forest and grassland often forms a gradual continuum, involving intermediate vegetation structures such as shrublands or heathlands, which are inherently difficult to assign to a single class. The results therefore highlight the robustness of the models in handling both temporal mismatches in the reference data and ambiguous land cover gradients. In these instances, the segmentation models prioritized the current visual cues present in the Pleiades imagery over the erroneous labels, correctly distinguishing vegetation types (e.g., grassland vs. forest) despite the conflicting supervision signal. This behavior underscores the framework's resilience to label noise and its capacity to learn meaningful spatial-spectral representations rather than simply memorizing training annotations (Fig. 7).

### 3.2.2. Monitoring of salt marshes dynamics using Sentinel-2 images in Mont-Saint-Michel Bay

The second validation case examined spatiotemporal changes in salt-marsh (schorre) vegetation (Vernberg, 1993) within the Mont-Saint-Michel Bay tidal system using multi-temporal Sentinel-2 MSI data (2017–2024) in L3 A format to avoid clouds. Training data were generated through photo-interpretation from aerial photography and
<figure><img src="image_9.png" /><figcaption>Fig. 7. Qualitative comparison of predictions by various semantic segmentation models for multi-class classification.</figcaption></figure>

inventories made in situ. This bay represents the largest area of salt meadow in Europe (Fig. 9) (Tessier et al., 2003).

These carefully selected case studies encompassing both very-high-resolution (0.5 m) single date classification and medium-resolution (10 m) SITS demonstrate the framework's versatility across fundamentally different RS challenges, from fine-scale habitat mapping to broad scale coastal ecosystem monitoring. The Crozon implementation particularly highlighted the method's ability to discriminate spectrally similar but ecologically distinct vegetation types, while the Mont-Saint-Michel application proved its robustness against tidal fluctuations and suspended sediment variability characteristic of macrotidal environments. Using the proposed approach, Sentinel-2 tiles (30UXU, 30TXN, 30TYN) were used to train different semantic segmentation models proposed in this research. The training was based on a L3 A image in 2017. Following the training, two Sentinel-2 images were cut from the Mont-Saint-Michel Bay area of interest, so that salt marshes could be detected. The results showed that in 7 years the salt meadows had increased by 4.69% to 439 ha, with a regression of 274 ha. For a total of 3680 ha of salt meadow (see Fig. 8).

Recent literature on coastal ecosystem monitoring underscores the relevance of approaches that combine multiple types of sensors with advanced analytical methods for detecting and characterizing these complex environments. For instance, Le Guillou and Niculescu, 2025 analyzed the spatial changes in Breton coastal wetlands over three decades (1990–2020) and revealed contrasting dynamics: the salt marshes in Audierne Bay decreased by approximately 8%, whereas those in Mont-Saint-Michel Bay expanded by nearly 36%. Similarly, Yi et al., 2025 integrated Sentinel-1 and Sentinel-2 time series to produce accurate maps of salt-marsh habitats, demonstrating the robustness of their approach (overall accuracy ≈ 90%) and the temporal stability of vegetated surfaces over several years. In addition, combining very high-resolution drone imagery with Sentinel-2 data has proven effective for generating detailed maps of mixed forest stands. Collectively, these studies emphasize the importance of a methodological framework that leverages deep learning (DL) architectures and class-imbalance mitigation strategies to analyze, at both fine and large scales, the surface dynamics and spatial evolution of coastal ecosystems.

## 4. Discussion

The proposed methodology was applied and validated in multiple practical case studies that involved various geographic contexts

and types of satellite imagery. Specifically, the approach was implemented for LULC classification into five distinct categories in Crozon peninsula (Brittany, France) using high-resolution Pleiades imagery (50 cm resolution), as well as for detecting and monitoring salt marshes (schorre) dynamics in the Mont-Saint-Michel Bay (Brittany and Normandy, France) using Sentinel-2 data from 2017 to 2024 (Table 1). This deliberate selection of contrasting applications ranging from high-resolution LULC mapping to medium-resolution time series analysis of coastal ecosystems was designed to demonstrate the method's inherent flexibility and adaptability to different analytical requirements and operational contexts. The successful application across these varied use cases confirms the robustness of the approach when handling different spatial resolutions (from 50 cm to 10 m) and temporal scales, while maintaining consistent performance in extracting meaningful environmental information. This article presents a methodology for preparing custom datasets and making predictions using semantic segmentation models trained on 224 × 224 patches. However, the provided Python pipeline offers a flexible architecture that can handle input patches of different sizes. Depending on the satellite imagery and its spatial and spectral resolution, the patch size can be adjusted accordingly. For very high spatial resolution images (e.g., less than 50 cm), we recommend using larger patches (e.g., 524 × 524), as these images contain a dense amount of pixel information. For lower-resolution imagery (such as Landsat data), we suggest using smaller patches (e.g., 64 × 64), since each patch covers a larger ground area.

Patch sizes are adjustable to match image resolution and scene characteristics, enhancing flexibility across use cases. The model's architecture concludes with a 2D convolutional output layer containing as many filters as the number of target classes, followed by a softmax activation function. This configuration is especially effective for medium-resolution satellite imagery (e.g., Sentinel-2 at 10 m/pixel), where target objects may occupy only a portion of a pixel or grid cell. The spatially accurate grid facilitates manual verification of label alignment and spectral consistency across the study area, which in turn reduces error propagation during model training. Our experiments show that the model preserves the geographical coherence of features while enabling sampling strategies to be tailored to the spatial distribution of target classes. The end-to-end pipeline ensures that the model receives spatially standardized and correctly aligned input patches, while retaining the geospatial integrity of the original data.
Images Labels U-Net U-Net++ Pan Deeplabv3+

<figure><img src="image_10.png" /><figcaption>Fig. 8. Qualitative comparison of predictions by various semantic segmentation models for binary classification (salt marsh detection)</figcaption></figure>

<figure><img src="image_11.png" /><figcaption>Fig. 9. Mapping of Salt marshes dynamic in Mont-Saint-Michel Bay between 2017 and 2024 using U-Net.</figcaption></figure>

Thanks to its modular structure and adjustable parameters (set during the grid creation in QGIS and the data extraction steps in Python), the workflow is adaptable to a variety of satellite sensors and spatial resolutions. This contribution is particularly relevant for practitioners and researchers seeking to apply DL models to domain-specific datasets that fall outside the scope of conventional benchmarks. While the current implementation demonstrates robust performance and architectural coherence, several targeted refinements could be considered to address persistent challenges, particularly class imbalance and generalization across complex geographies. Crucially, as our benchmark indicates that increased architectural complexity does not systematically yield improved performance on imbalanced tasks, alternative pathways beyond model deepening merit exploration. One promising avenue involves refining the learning objective. Although a hybrid loss is already employed, dynamic class-weighting schemes or advanced loss functions such as Focal Loss which down weights well classified examples could provide more nuanced handling of severe foreground

background disparities (Lin et al., 2017). Complementarily, strategic data augmentation could be enhanced; rather than applying uniform transformations, targeted oversampling of minority-class samples or realistic synthesis via copy-paste augmentation may improve model sensitivity to underrepresented features. Enhanced spectro-spatial pre-processing represents another lever for improvement. Adaptive per-scene normalization, guided dimensionality reduction (e.g., via PCA or spectral indices), or automated band selection could mitigate spectral variability and highlight discriminative features (Ghiasi et al., 2021). Finally, model-agnostic post-processing techniques, such as Conditional Random Fields (CRFs) or morphological filtering, could enforce spatial consistency and reduce fragmented predictions without retraining the network (Chen et al., 2018a). These directions focus not on increasing model complexity but on addressing data-centric biases and improving decision boundaries, offering modular enhancements that align with the flexible philosophy of the proposed framework (Whang et al., 2023).
Beyond architectural refinements of the semantic segmentation
models itself, another important avenue for improvement lies in in-
creasing the adaptability of the approach to varying computational
environments. At present, the implementation is constrained to operate
on fixed-size 224 × 224 input patches, which inherently limits its scal-
ability and flexibility across different hardware profiles. A more robust
solution would involve designing and providing multiple versions of the
model architecture, each tailored to specific computational budgets and
resource availability. By parametrizing key architectural elements such
as depth, number of filters, and input resolution it would be possible to
generate lightweight models for real-time or edge inference, as well as
deeper, high-capacity variants for offline, high-performance computing
environments (Tan and Le, 2019). This modular design philosophy
would make the framework more accessible and usable in a wide range
of operational contexts, from low-power embedded devices to large-
scale cloud infrastructures (Cai et al., 2019). In practical terms, this
could translate into the publication of a suite of models with varying
levels of computational complexity, enabling users to select the most
appropriate version according to their performance constraints and
application needs (Howard et al., 2017).

Another significant axis for improvement involves the integration
of temporal dynamics into the segmentation process. The current input
is based on single input normalized, which limits the model's capacity
to capture temporal patterns that are often critical for distinguishing
between phenologically similar land cover types. A hybrid approach,
inspired by architectures such as M3-Fusion (Benedetti et al., 2018)
or Flair (Garioud et al., 2022, 2023), could address this limitation by
jointly leveraging high-temporal-resolution (HR) time series such as
those provided by Sentinel-2 and VHR imagery. In such a setup, a
recurrent neural network (RNN), typically a GRU or LSTM (Shiri et al.,
2023; Mohsen, 2023), can be used to encode the temporal sequence of
HR images, capturing the evolution of spectral signatures over time. Si-
multaneously, a convolutional neural network (CNN) can extract spatial
features from the VHR image. The joint embedding of these two sources
of information can then be fused in a shared representation space,
enabling the model to benefit from both spatial detail and temporal
context. This fusion strategy would significantly enhance the model's
ability to disambiguate complex classes, handle seasonal variability,
and produce more robust segmentation maps across time.

5. Conclusion

This study presents a flexible, reproducible, and architecture-
agnostic framework for semantic segmentation of satellite imagery,
designed to address both binary and multi-class land cover mapping
challenges in complex coastal environments. The proposed modular
implementation enables systematic benchmarking of modern encoder-
decoder architectures including U-Net, U-Net++, PAN, and DeepLabv3+
across diverse data regimes, from limited, imbalanced datasets to
more extensive, balanced collections. Our comparative analysis re-
veals that no single architecture universally dominates across tasks.
For the binary salt marsh detection under severe class imbalance,
the cross-validation analysis revealed that U-Net++ offered the best
trade-off between precision and recall, achieving the highest mean
F1-score (0.742). However, the standard U-Net demonstrated superior
stability across folds, reinforcing its value as a robust, low-variance
baseline for environmental monitoring when computational resources
or training data are constrained. In contrast, for the multi-class land
cover mapping with balanced classes, PAN and U-Net++ achieved
marginally higher validation and test F1-scores (up to 0.802), ben-
efiting from their advanced attention mechanisms and dense skip
pathways when sufficient representative data are available. These find-
ings underscore that the optimal model choice is context dependent,
influenced more by data characteristics such as class distribution,
sample size, and spectral complexity than by architectural sophisti-
cation alone. A key methodological contribution is the integration

of a hybrid training strategy specifically designed for remote sensing
challenges. By combining dynamic class weighting in a composite
loss function, spectral-consistent data augmentation, and a modular
pipeline that bridges QGIS-based spatial preprocessing with PyTorch
based model training, we provide an accessible yet powerful toolkit
for non-specialists. The framework demonstrates that accurate seg-
mentation of high-resolution Pleiades and multi-spectral Sentinel-2
imagery can be achieved with modest computational resources, thereby
promoting more sustainable and equitable AI applications in environ-
mental monitoring. Despite these advances, several limitations warrant
further investigation. The framework currently operates on fixed size
patches, which may fragment large contiguous features; future work
could explore end-to-end large-area inference or the integration of
spatial constraints via post-processing. Moreover, while the bench-
marked architectures process single-date imagery, many land cover
dynamics are inherently temporal. Extending the framework to support
spatio-temporal architectures such as those combining convolutional
backbones with recurrent or attention-based temporal modules would
 unlock new potential for monitoring seasonal and inter-annual changes
in sensitive ecosystems like coastal wetlands. This work provides a
validated, open-source foundation for applying state-of-the-art seg-
mentation models to domain-specific remote sensing problems. By
emphasizing modularity, reproducibility, and adaptive training strate-
gies over any single architecture, we offer a pragmatic and scalable
pathway for researchers and practitioners to map and monitor complex
geographical contexts with greater accuracy and accessibility.

CRediT authorship contribution statement

**Adrien Le Guillou:** Writing – original draft, Visualization, Validation, Software, Methodology, Investigation, Conceptualization. **Simona Niculescu:** Writing – review & editing, Validation, Supervision, Project administration, Funding acquisition.

Declaration of competing interest

The authors declare the following financial interests/personal rela-
tionships which may be considered as potential competing interests:
Adrien Le Guillou reports article publishing charges was provided by
University of Western Brittany. Adrien Le Guillou reports a relationship
with University of Western Brittany that includes: employment and
non-financial support. If there are other authors, they declare that they
have no known competing financial interests or personal relationships
that could have appeared to influence the work reported in this paper.

Acknowledgments

The research work was partially supported by a CNES/TOSCA
project 10486.

Data availability

All materials, including codes related to our findings, are provided
as follows. The code is available here : https://github.com/aleguillou1/
SemanticSeg4EO.

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