A semantic segmentation method with category boundary for Land Use and Land Cover (LULC) mapping of Very-High Resolution (VHR) remote sensing image

Xu, Zeyu; Su, Cheng; Zhang, Xiaocan

doi:10.1080/01431161.2020.1871100

Cited by 22 publications

(9 citation statements)

References 36 publications

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“…ISPR Vaihingen and Potsdam are the widely used benchmark datasets, followed by GID, Landcover.ai, DeepGlobe and WHDLD. The ISPRS Vaihingen comprises 33 aerial image patches in IRRGB format along with their associated digital surface model (DEM) data, each with a size of around 2500 × 2500 pixels at 9 cm spatial resolution [95,96]. Similarly, the publicly accessible ISPRS Potsdam dataset encompasses Potsdam city, Germany.…”

Section: Benchmark Datasetsmentioning

confidence: 99%

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Ajibola,

Cabral

2024

Preprint

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Recent advancements in deep learning have spurred the development of numerous novel semantic segmentation models for land cover mapping, showcasing exceptional performance in delineating precise boundaries and producing highly accurate land cover maps. However, to date, no systematic literature review has comprehensively examined semantic segmentation models in the context of land cover mapping. This paper addresses this gap by synthesizing recent advancements in semantic segmentation models for land cover mapping from 2017 to 2023, drawing insights on trends, data sources, model structures, and performance metrics based on a review of 106 extracted articles. Our analysis identifies top journals in the field, including MDPI Remote Sensing, IEEE Journal of Selected Topics in Earth Science, and IEEE Transactions on Geoscience and Remote Sensing, IEEE Geoscience and Remote Sensing Letters, ISPRS Journal Of Photogrammetry And Remote Sensing as the leading journals. We find that research predominantly focuses on land cover, urban areas, precision agriculture, environment, coastal areas, and forests. Geographically, 35.29% of the study areas are located in China, followed by USA (11.76%), France (5.88 %), Spain (4%) and others. Sentinel-2, Sentinel-1, and Landsat satellites emerge as the most commonly used data sources. Benchmark datasets such as ISPRS Vaihingen &amp; Potsdam, LandCover.ai, DeepGlobe, and GID datasets are frequently employed. Model architectures predominantly utilize encoder-decoder, and hybrid convolutional neural network-based structures because of their impressive performances, with limited adoption of transformer-based architectures due to its computational complexity issue, and slow convergence speed. Lastly, this paper highlights existing key research gaps in the field to guide future research directions.

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Section: Benchmark Datasetsmentioning

confidence: 99%

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Ajibola,

Cabral

2024

Preprint

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“…Provided that any value of α is greater than β, the imbalanced image segmentation datasets can be handled more effectively. On the other hand, the γ parameter that determines the behavior of the FTL function in Equation ( 2) varies in the range [1,3]. Although the effect of γ relatively changes depending on TI value, in their experiments, Abraham and Khan [48] stated that the best performance was observed with γ = 4/3.…”

Section: Training Approachesmentioning

confidence: 99%

“…On the other hand, semantic segmentation in Land Use and Land Cover (LULC) change detection takes a considerable part in the RS literature. Venugopal [2] explained an automatic semantic segmentationbased change detection that produces a final change between the given two input images, while [3] described a semantic segmentation method with category boundary for LULC mapping. Touzani and Granderson [4], which can be considered in the scope of GIS applications, made improvements to existing accuracy of automatic building footprint extractions from RS images using a deep learning model.…”

Section: Introductionmentioning

confidence: 99%

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

Akçay

Kinaci

Avşar

et al. 2021

IJGI

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In geospatial applications such as urban planning and land use management, automatic detection and classification of earth objects are essential and primary subjects. When the significant semantic segmentation algorithms are considered, DeepLabV3+ stands out as a state-of-the-art CNN. Although the DeepLabV3+ model is capable of extracting multi-scale contextual information, there is still a need for multi-stream architectural approaches and different training approaches of the model that can leverage multi-modal geographic datasets. In this study, a new end-to-end dual-stream architecture that considers geospatial imagery was developed based on the DeepLabV3+ architecture. As a result, the spectral datasets other than RGB provided increments in semantic segmentation accuracies when they were used as additional channels to height information. Furthermore, both the given data augmentation and Tversky loss function which is sensitive to imbalanced data accomplished better overall accuracies. Also, it has been shown that the new dual-stream architecture using Potsdam and Vaihingen datasets produced 88.87% and 87.39% overall semantic segmentation accuracies, respectively. Eventually, it was seen that enhancement of the traditional significant semantic segmentation networks has a great potential to provide higher model performances, whereas the contribution of geospatial data as the second stream to RGB to segmentation was explicitly shown.

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“…We take the CLCM with domain adaptation as a pixel-level and multi-category segmentation task, whose experimental results are generally evaluated via the generated confusion matrix. Referring to it, TP, FP, TN, and FN denote the numbers of true positives, false positives, true negatives, and false negatives [53,54]. Having access to these indexes, the following four metrics, i.e., per-class accuracy, overall accuracy, balanced F (F1) score, and intersection-over-union (IoU), are given to prove the validity effectiveness of our proposed CsCANet.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network

et al. 2021

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Coastal land cover mapping (CLCM) across image domains presents a fundamental and challenging segmentation task. Although adversaries-based domain adaptation methods have been proposed to address this issue, they always implement distribution alignment via a global discriminator while ignoring the data structure. Additionally, the low inter-class variances and intricate spatial details of coastal objects may entail poor presentation. Therefore, this paper proposes a category-space constrained adversarial method to execute category-level adaptive CLCM. Focusing on the underlying category information, we introduce a category-level adversarial framework to align semantic features. We summarize two diverse strategies to extract category-wise domain labels for source and target domains, where the latter is driven by self-supervised learning. Meanwhile, we generalize the lightweight adaptation module to multiple levels across a robust baseline, aiming to fine-tune the features at different spatial scales. Furthermore, the self-supervised learning approach is also leveraged as an improvement strategy to optimize the result within segmented training. We examine our method on two converse adaptation tasks and compare them with other state-of-the-art models. The overall visualization results and evaluation metrics demonstrate that the proposed method achieves excellent performance in the domain adaptation CLCM with high-resolution remotely sensed images.

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A semantic segmentation method with category boundary for Land Use and Land Cover (LULC) mapping of Very-High Resolution (VHR) remote sensing image

Cited by 22 publications

References 36 publications

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

A Review and Meta-analysis of Semantic Segmentation Models in Land Use/ Land Cover Mapping

Semantic Segmentation of High-Resolution Airborne Images with Dual-Stream DeepLabV3+

Unsupervised Domain Adaption for High-Resolution Coastal Land Cover Mapping with Category-Space Constrained Adversarial Network

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