Classification Bias Research Articles

Artificial intelligence in judicial adjudication: Semantic biasness classification and identification in legal judgement (SBCILJ)

History reveals that human societies have suffered in terms of social justice due to cognitive bias. Semantic bias tends to amplify cognitive bias. Therefore, the presence of cognitive biases in extensive historical data can potentially result in unethical and allegedly inhumane predictions since AI systems are trained on this data. The innovation of artificial intelligence and its rapid integration across disciplines has prompted questions regarding the subjectivity of the technology. Current research focuses the semantic bias in legal judgment to increase the legitimacy of training data. By the application of general-purpose Artificial Intelligence (AI) algorithms, we classify and detect the semantics bias that is present in the Chinese Artificial Intelligence and Law (CAIL) dataset. Our findings demonstrate that AI models acquire superior prediction power in the CAIL dataset, which is comprised of hundreds of cases, compared to a structured professional risk assessment tool. To assist legal practitioners during this process, innovative approaches that are based on AI may be implemented inside the legal arena. To accomplish this objective, we suggested a classification model for semantic bias that is related to the classification and identification of semantic biases in legal judgment. Our proposed model legal field uses the example of categorization along with the identification of the CAIL dataset. This will be accomplished by identifying the semantics biases in judicial decisions. We used different types of classifiers such as the Support Vector Machine (SVM), Naïve-Bayes (NB), Multi-Layer Perceptron (MLP), and the K-Nearest Neighbour (KNN) to come across the preferred results. SVM got 96.90 %, NB has 88.80 %, MLP has 86.75 % and KNN achieved 85.66 % accuracy whereas SVM achieved greater accuracy as compared to other models. Additionally, we demonstrate that we were able to get a relatively high classification performance when predicting outcomes based just on the semantic bias categorization in judicial judgments that determine the outcome of the case.

Floating object detection using double-labelled domain generalization

Detection of floating objects in complicated aquatic environments has a wide range of applications, but it confronts significant hurdles due to the imbalance in detection accuracy and efficiency, and low domain generalization performance. To address these issues, this study proposes a novel floating object detection method based on double-labelled domain generalization. First, the Single Shot Multibox Detector (SSD) is improved by replacing the backbone network with a lightweight feature extraction network, and dynamic feature pyramid network is introduced to balance accuracy and efficiency. Then, this study initializes the improved SSD network based on the double-labelled data of the source domain, and filters the feature extractor bias, classification bias and location bias using pseudo-labelling and feature projection based on the un-labelled source domain data to minimize the bias to improve the domain generalization performance. The proposed method is trained on a self-constructed floating object dataset and is compared with state-of-the-art methods based on multiple scenarios. The results show that the proposed method achieves better performance in double-labelled domain generalization and conventional domain generalization tasks compared to other methods, achieving 70.33%, 22.38 f/s and 85.29% and 17.81 f/s in accuracy and speed respectively, satisfying the need for multi-scale floating object detection in complex environments and also alleviating the data labelling problem. This work effectively solves the problem of slow detection of floating objects due to the complex model structure and low generalization ability, and provides support for the rapid detection of floating objects in complex scenarios and the promotion of technology applications.

TFRS: A task-level feature rectification and separation method for few-shot video action recognition

Few-shot video action recognition (FS-VAR) is a challenging task that requires models to have significant expressive power in order to identify previously unseen classes using only a few labeled examples. However, due to the limited number of support samples, the model’s performance is highly sensitive to the distribution of the sampled data. The representativeness of the support data is insufficient to cover the entire class, and the support features may contain shared information that confuses the classifier, leading to biased classification. In response to this difficulty, we present a task-level feature rectification and separation (TFRS) method that effectively resolves the sample bias issue. Our main idea is to leverage prior information from base classes to rectify the support samples while removing the commonality of task-level features. This enhances the distinguishability and separability of features in space. Furthermore, TFRS offers a straightforward yet versatile solution that can be seamlessly integrated into various established FS-VAR frameworks. Our design yields significant performance enhancements across various existing works by implementing TFRS, resulting in competitive outcomes on datasets such as UCF101, Kinetics, SSv2, and HMDB51.

Multi-source precipitation estimation using machine learning: Clarification and benchmarking

Multi-source precipitation estimation techniques, including interpolation, bias correction, and merging, are essential for acquiring high-precision precipitation datasets. Machine learning (ML) methods have achieved inspiring success in this field, but current studies utilizing ML often blur the boundaries of interpolation, bias correction, and merging because of the utilization of uniform input–output frameworks. The ambiguity in classification (interpolation, bias correction and merging) results in inconsistent and inappropriate comparative benchmarking among studies. A widespread example is the tendency to select easy-to-beat benchmarks, which can magnify the relative performance of ML methods. We propose to reclarify ML-based precipitation estimation by conducting a series of experiments with designed feature combinations to unveil the essence of ML-based methods. Our findings suggest that with different feature combinations, there are no essential distinctions between models for merging, bias correction and interpolation. Ground measurements, as the reference in supervised learning, play a more vital role than different features in precipitation estimation. This means that the ML-based multi-source merging or bias correction should be treated as gauge data-based interpolation with various auxiliary predictors. Moreover, the improvements of ML estimates are more significant compared to low-standard benchmarks like individual precipitation datasets and simple merging methods, but less preeminent compared to high-standard benchmarks such as a sophisticated interpolation method (e.g., Geographically Weighted Regression). Therefore, we call for positioning such ML applications as interpolation and adhering to high-standard benchmarks whenever possible, which can illustrate the true potential and promote further advancements of ML in geospatial estimation.

Neural collapse inspired semi-supervised learning with fixed classifier

Pseudo-labeling-based approaches are gaining prominence in Semi-Supervised Learning (SSL). Recent studies have identified that the key bottleneck in this methodology is addressing insufficient, incorrect, and imbalanced pseudo-labels. In this paper, we argue that the intrinsic problem behind this bottleneck is classifier bias, i.e., the classifier's prototypes suffer from poor uniformity. Further, inspired by neural collapse that reveals an optimal structure under supervised training scenarios, we address this classifier bias by utilizing an offline simplex Equiangular Tight Frame (ETF) classifier with maximally and equally separated prototypes. During the training phase, we maintain the prototypes of the classifier as fixed and concentrate on refining the feature encoder. Specifically, we integrate a straightforward clustering-based pseudo-labeling strategy with information maximization for feature learning. In practice, the fixed ETF classifier prevents the model from falling into a detrimental cycle, where a biased classifier exacerbates misaligned features, further perpetuating this bias. Furthermore, the clustering-based pseudo-labeling strategy reduces the dependency on complex threshold-adjusting mechanisms and effectively navigates the quantity-quality trade-off that plagues existing SSL methods. Leveraging these methodologies, we develop a simple yet powerful approach, termed ETF-SSL. Extensive experiments across Image, Text, and Audio datasets demonstrate that ETF-SSL can achieve competitive or superior performance compared to existing approaches. This success highlights the benefits of using a fixed ETF classifier in SSL and points to promising directions for future research in this area. The code is available at: https://github.com/yichenwang231/ETFSSL.

Dataset Augmentation for Counteracting Bias in Toxic Comment Classification


 Toxic comments are a prevalent issue on online social media and networking platforms. These comments contain offensive, malicious, hate speech, or other harmful content that negatively impacts audiences and communities. Effectively detecting and categorizing toxic comments is essential for maintaining order in the online environ ment, protecting user safety, and enhancing user experience. This is despite the fact that researchers and companies have developed various models to recognize toxicity in online chats and comments, achieving some success. However, many of the currently used models incorrectly classify non-toxic comments that contain certain identity terms as potentially toxic. This misclassification hinders the ability to accurately identify categorized comments. In this paper, the detection and classification of toxic comments were implemented using Term Frequency-Inverse Document Frequency (TF-IDF) and machine learning techniques. Additionally, two dataset-specific optimizations were proposed to mitigate the impact of bias on text classification by expanding the number of datasets. Comparative analysis of bias evaluation metrics demonstrates that this approach can effectively mitigate bias while maintaining the accuracy of the original model as much as possible.

Kmeans-SMOTE Integration for Handling Imbalance Data in Classifying Financial Distress Companies using SVM and Naïve Bayes

Imbalanced data presents significant challenges in machine learning, leading to biased classification outcomes that favor the majority class. This issue is especially pronounced in the classification of financial distress, where data imbalance is common due to the scarcity of such instances in real-world datasets. This study aims to mitigate data imbalance in financial distress companies using the Kmeans-SMOTE method by combining Kmeans clustering and the synthetic minority oversampling technique (SMOTE). Various classification approaches, including Nave Bayes and support vector machine (SVM), are implemented on a Kaggle financial distress data set to evaluate the effectiveness of Kmeans-SMOTE. Experimental results show that SVM outperforms Nave Bayes with impressive accuracy (99.1%), f1-score (99.1%), area under precision recall (AUPRC) (99.1%), and geometric mean (Gmean) (98.1%). On the basis of these results, Kmeans-SMOTE can balance the data effectively, leading to a quite significant improvement in performance.

Rule-based deep learning method for prognosis of neonatal hypoxic-ischemic encephalopathy by using susceptibility weighted image analysis.

Susceptibility weighted imaging (SWI) of neonatal hypoxic-ischemic brain injury can provide assistance in the prognosis of neonatal hypoxic-ischemic encephalopathy (HIE). We propose a convolutional neural network model to classify SWI images with HIE. Due to the lack of a large dataset, transfer learning method with fine-tuning a pre-trained ResNet 50 is introduced. We randomly select 11 datasets from patients with normal neurology outcomes (n = 31) and patients with abnormal neurology outcomes (n = 11) at 24 months of age to avoid bias in classification due to any imbalance in the data. We develop a rule-based system to improve the classification performance, with an accuracy of 0.93 ± 0.09. We also compute heatmaps produced by the Grad-CAM technique to analyze which areas of SWI images contributed more to the classification patients with abnormal neurology outcome. Such regions that are important in the classification accuracy can interpret the relationship between the brain regions affected by hypoxic-ischemic and neurodevelopmental outcomes of infants with HIE at the age of 2 years.

Hematopoietic stem cell heterogeneity in non-human primates revealed by five-lineage output bias analysis.

Understanding hematopoietic stem cell (HSC) heterogeneity is crucial for treating malignant blood disorders. Compared with mice, we have limited knowledge of the heterogeneity of human HSCs. Fortunately, non-human primates (NHPs) have become the best animal models for studying human HSCs. Here, we employed a public dataset derived from NHP autologous bone marrow transplantation, and focused on a total of 820 HSC clones with reconstitution capacity of all available five lineages (granulocyte, monocyte, B cell, T cell, and natural killer cell) at two time points (11/12 and/or 42/43 months). Intriguingly, unsupervised clustering on these clones revealed six HSC subtypes, including a lymphoid/myeloid balanced (LM-balanced) subtype and five single-lineage-biased subtypes. We also observed that the subtypes of these HSC clones might change over time, and a given subtype could transition into any one of the other five subtypes, albeit with a certain degree of selectivity. Particularly, each of the six subtypes was more likely to turn into lymphoid-biased rather than myeloid-biased ones. Additionally, our five-lineage classification method exhibited strong correlation with traditional lymphoid/myeloid bias classification method. Specifically, our granulocyte- and monocyte-biased subtypes were predominantly attributed to α-HSCs, while LM-balanced, B cell-biased, and T cell-biased subtypes were primarily associated with β-HSCs. The γ-HSCs were composed of a small subset of B cell-biased and T cell-biased subtypes. In summary, our five-lineage classification identifies more finely tuned HSC subtypes based on lineage output bias. These findings enrich our understanding of HSC heterogeneity in NHPs and provide important insights for human research.

The clinical utility of Nanopore 16S rRNA gene sequencing for direct bacterial identification in normally sterile body fluids.

The prolonged incubation period of traditional culture methods leads to a delay in diagnosing invasive infections. Nanopore 16S rRNA gene sequencing (Nanopore 16S) offers a potential rapid diagnostic approach for directly identifying bacteria in infected body fluids. To evaluate the clinical utility of Nanopore 16S, we conducted a study involving the collection and sequencing of 128 monomicrobial samples, 65 polymicrobial samples, and 20 culture-negative body fluids. To minimize classification bias, taxonomic classification was performed using 3 analysis pipelines: Epi2me, Emu, and NanoCLUST. The result was compared to the culture references. The limit of detection of Nanopore 16S was also determined using simulated bacteremic blood samples. Among the three classifiers, Emu demonstrated the highest concordance with the culture results. It correctly identified the taxon of 125 (97.7%) of the 128 monomicrobial samples, compared to 109 (85.2%) for Epi2me and 102 (79.7%) for NanoCLUST. For the 230 cultured species in the 65 polymicrobial samples, Emu correctly identified 188 (81.7%) cultured species, compared to 174 (75.7%) for Epi2me and 125 (54.3%) for NanoCLUST. Through ROC analysis on the monomicrobial samples, we determined a threshold of relative abundance at 0.058 for distinguishing potential pathogens from background in Nanopore 16S. Applying this threshold resulted in the identification of 107 (83.6%), 117 (91.4%), and 114 (91.2%) correctly detected samples for Epi2me, Emu, and NanoCLUST, respectively, in the monomicrobial samples. Nanopore 16S coupled with Epi2me could provide preliminary results within 6 h. However, the ROC analysis of polymicrobial samples exhibited a random-like performance, making it difficult to establish a threshold. The overall limit of detection for Nanopore 16S was found to be about 90 CFU/ml.

Applying Minority Range to Gini Index to Handle Imbalanced Dataset in Decision Tree Classifiers

The Class imbalance Problem is a common problem in Machine Learning where the number of instances in one class is significantly lower than the other, this can lead to biased classification models where the majority class dominates and the minority class is mis- misclassified. Decision Tree Classifiers are commonly used for classification tasks due to their simplicity and in interpretability. However, the class imbalance Problem can negatively impact the performance of decision tree Classifiers [1]. In this paper, we discuss a new approach to training a decision tree classifier that is an improvement over a pre-existing approach. We also provide experiments to prove that the proposed method is an improvement over the preexisting metrics

Metric-Independent Mitigation of Unpredefined Bias in Machine Classification

In this study, we evaluate the “propose-review” framework for the mitigation of bias in machine classification. The framework considers Bob, who aims to protect sensitive dimensions from discrimination, and Alice, who sends proposals to Bob for using his data to construct a target classifier. The goal is to minimize discrimination in Bob’s protected dimension while preserving the desired separating capability of Alice’s classifier. The method does not assume predefined bias terms, does not anchor on specific fairness metrics, and is independent of Alice’s classifier choice. We consider that data attributes have different concentrations of the latent bias axes; assessing attributes’ concentrations in the ruled bias hyperspace helps identify bias-prone attributes and inform bias-mitigating data transforms. To this end, we assess attributes’ contribution to the separating capability of Bob’s conceptual classifier. We then compute the pairwise distances between attributes, and by applying multidimensional scaling to the distance matrix, we infer the axes of bias and establish a bias-attribute mapping. Bias mitigation is achieved by greedily applying appropriate data transforms to bias-prone attributes. The method works desirably across 21 classifiers and 7 datasets, bringing about substantial bias reduction under different choices of the protected dimension and the fairness metric. Compared to adversarial debiasing, the method better exploits the fairness-utility trade-off in machine classification.

A Two-Stage Classification for Dealing with Unseen Clusters in the Testing Data

Classification is an important statistical tool that has increased its importance since the emergence of the data science revolution. However, a training data set that does not capture all underlying population subgroups (or clusters) will result in biased estimates or misclassification. In this paper, we introduce a statistical and computational solution to a possible bias in classification when implemented on estimated population clusters. An unseen-cluster problem denotes the case in which the training data does not contain all underlying clusters in the population. Such a scenario may occur due to various reasons, such as sampling errors, selection bias, or emerging and disappearing population clusters. Once an unseen-cluster problem occurs, a testing observation will be misclassified because a classification rule based on the sample cannot capture a cluster not observed in the training data (sample). To overcome such issues, we suggest a two-stage classification method to ameliorate the unseen-cluster problem in classification. We suggest a test to identify the unseen-cluster problem and demonstrate the performance of the two-stage tailored classifier using simulations and a public data example.

Continual Learning With Knowledge Distillation: A Survey.

The foremost challenge in continual learning is to mitigate catastrophic forgetting, allowing a model to retain knowledge of previous tasks while learning new tasks. Knowledge distillation (KD), a form of regularization, has gained significant attention for its ability to maintain a model's performance on previous tasks by mimicking the outputs of earlier models during the learning of new tasks, thus reducing forgetting. This article offers a comprehensive survey of continual learning methods employing KD within the realm of image classification. We provide a detailed analysis of how KD is utilized in continual learning methods, categorizing its application into three distinct paradigms. Besides, we classify these methods based on the type of knowledge source used and thoroughly examine how KD consolidates memory in continual learning from the perspective of loss functions. In addition, we have conducted extensive experiments on CIFAR-100, TinyImageNet, and ImageNet-100 across ten KD-integrated continual learning methods to analyze the role of KD in continual learning, and we have further discussed its effectiveness in other continual learning tasks. Our extensive experimental evidence demonstrates that KD plays a crucial role in mitigating forgetting in continual learning and substantiates that, when used with data replay, classification bias adversely affects the effectiveness of KD, whereas employing a separated softmax loss can significantly enhance its efficacy.

Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms

Improving Commonsense Bias Classification by Mitigating the Influence of Demographic Terms

A fusiform network of indoor scene classification with the stylized semantic description for service-robot applications

Scene perception technology helps robots identify the target areas that people refer to so that it contributes to human–robot interaction, semantic navigation and other related tasks. Currently, pure semantic-feature-based methods are insufficient to fully describe diverse indoor scenes, resulting in a high confusion rate and performance inconsistency on each class. To overcome these problems, the style information is compounded with the common semantic feature to form a more elaborate description of a scene, and the corresponding network is proposed. First, the convolutional network is adopted to extract the base feature. Then, the high-level feature maps are taken out and reasonably divided into overlapped units to reserve a more appropriate neighbour correlation. Next, two branches are proposed to acquire the style and the semantic information respectively. In the style branch, the gram matrix is applied to each unit. In the semantic branch, the units of high-level feature maps are directly used. In both branches, batch normalization and vector embedding techniques are applied to the flattened elements of the unit sets to unify the feature strength and introduce compression representation. Then, the two compressed representations are combined as a compound expression to describe a scene. A multi-head self-attention structure is introduced to correlate and reinforce the multi-divided information and further form a dominant and refined stylized semantic description. Finally, scene classification is implemented by a multi-layer fully connected network. The experiment adopts the paradigm of once learning and cross-environment inference, which is closer to practical applications. The proposed method performs the best compared with several popular methods in the field of robotics, and especially, it has the smallest classification bias. In addition, the necessity of the main components of our method is evaluated, and the semantic explanation is also presented.

Classification of dendritic spiking neurons’ shape through clustering and machine learning techniques

A synapse is a specialized region between two adjacent neurons that allows information to be transmitted from one cell to another. The formation of these connections and transfer of signals via electrical impulses is a critical characteristic of neuronal cells. The synapse is formed by the axonal bouton on the axon side and the dendritic spine, a specialized outgrowth of the dendritic membrane, on the dendrite side. Dendritic spines exhibit diverse shapes and sizes, differing significantly across brain regions, cell categories, and animal species. Changes in dendritic spine morphology occur in neurodevelopmental and neurodegenerative ailments while also responding to external stimuli. Although deemed to facilitate synaptic plasticity, further investigation is essential for establishing the correlation between spine construction and its function. To address the issue in modern neurobiology of characterizing synapse morphology on 3D neuron images, the development of effective analytical methods is necessary. Our team has produced an open-source software solution for precise dendritic spine segmentation using 3D dendrite images. This software calculates the 10 most widely used 3D-adapted morphological features [1, 2] and enables classification and clustering of dendritic spine data sets to determine their shape. In addition to numerical features for describing the shape of a dendritic spine, researchers proposed using a histogram of chord lengths, known as the chord length distribution histogram (CLDH). This involves generating a set of random chords within the dendritic spine’s volume, connecting its outer boundaries and forming a histogram. By setting n=30 000, the probabilistic fluctuations of the histogram become insignificant. The derived metrics were then used for clustering and classifying the dataset. Classification based on predetermined morphological groups is a frequently used approach for analyzing the morphology of dendritic spines. This method involves categorizing spines into established groups such as thin, mushroom, and stubby. Experimenters generally perform classification in a semi-automated manner, leading to considerable error. We have created a spine categorization tool using a machine learning algorithm. The tool classifies spines based on a consensus reached by eight experts who manually labeled the training dataset. The accuracy of this method surpasses 77% when using classical morphological features and is comparable to expert labeling. The implementation of this approach reduces classification bias and complexity. Recent studies, including those using live microscopy in vitro and in vivo, indicate that dendritic spine shapes exhibit a continuum rather than distinct categories [3]. Therefore, it is essential to establish a dependable methodology for evaluating and examining the morphology of dendritic spines. We have created a clustering tool that establishes both the number of groups and their content based on data, rather than the experimenter’s discretion. This tool leverages the k-means and DBSCAN algorithms for its representation. Three clustering methods are presented to determine the number of clusters: the silhouette method, the elbow method, and a novel method, developed by the authors, based on the max class divergence criteria. The authors assume that cluster quality improves as clusters differ significantly in the number of mushroom/thin/stubby spine classes, as marked by experts. The benefit of this approach is that it considers the particular data type for clustering. Without this knowledge, assessing the quality of clustering is challenging. The use of the CLDH metric for clustering yielded a consistent and stable number of clusters (n=5) across all three methods described. These clusters contain dendritic spines that share similar shapes and have been validated by experts. In contrast, using classical metrics resulted in a variable cluster count ranging from n=4 to n=14. These findings suggest that the CLDH metric, with its complexity, provides enough information about synapse shape to enable precise clustering.

Is ChatGPT a friend or foe in the war on misinformation?

The release of ChatGPT at the end of 2022 met with fears and optimism. One particularly important avenue of research that is emerging revolves around ChatGPT's ability to provide accurate and unbiased information on a variety of topics. Given the interest that Google and Microsoft have shown in similar technologies, it is likely that Large Language Models such as ChatGPT could become new gateways to information, and if this is the case, what kind of information this technology provides needs to be investigated. The current study examines the usefulness of ChatGPT as a source of information in a South African context by first investigating ChatGPT's responses to ten South African conspiracy theories in terms of truthfulness, before employing bias classification as well as sentiment analysis to evaluate whether ChatGPT exhibits bias when presenting eight South African political topics. We found that, overall, ChatGPT did not spread conspiracy theories. However, the tool generated falsehoods around one conspiracy theory and generally presented a left bias, albeit not to the extreme. Sentiment analysis showed that ChatGPT's responses were mostly neutral and, when more emotive, were more often positive than negative. The implications of the findings for academics and students are discussed, as are a number of recommendations for future research.

Hybrid resampling and weighted majority voting for multi-class anomaly detection on imbalanced malware and network traffic data

In a large skewed dataset, the data imbalance is severe and the classifier's accuracy is biased towards the majority class. Insufficient data makes it challenging for the classifier to learn the feature of the minority classes. Moreover, some existing techniques of binary classification cannot directly apply to multi-classification. Plain oversampling may generate redundant and irrelevant data for the minority classes while undersampling may eliminate too many features for the majority class. This paper proposes a combination of constrained undersampling, oversampling, noise cleaning, and a weighted majority voting classifier (WMVC) to detect multi-classification obfuscated malware via memory and network traffic anomalies. As a constraint on undersampling and oversampling, the proposed framework divides the total sample number by the class number to obtain the average class size. According to the constraint, one or more majority classes are down-sampled using Random Undersampling, while all the minority classes are up-sampled using Adaptive Synthetic Sampling Approach, which is followed by Tomek Link to remove the noisy data. Then a weighted majority voting classifier aggregated tree-based ensemble algorithms is designed and compared to the XGBoost and Convolutional Neural Network (CNN) classifiers and six state-of-the-art ensemble algorithms. The comparison results show that the performance of the classifiers on balanced data outperforms those on imbalanced data, and the WMVC outperforms XGBoost, CNN, and the six other ensemble algorithms. Our approach can alleviate the classifier's bias towards the majority class while improving its performance for the difficult minority class.

Optical and radar Earth observation data for upscaling methane emissions linked to permafrost degradation in sub-Arctic peatlands in northern Sweden

Abstract. Permafrost thaw in Arctic regions is increasing methane (CH4) emissions into the atmosphere, but quantification of such emissions is difficult given the large and remote areas impacted. Hence, Earth observation (EO) data are critical for assessing permafrost thaw, associated ecosystem change and increased CH4 emissions. Often extrapolation from field measurements using EO is the approach employed. However, there are key challenges to consider. Landscape CH4 emissions result from a complex local-scale mixture of micro-topographies and vegetation types that support widely differing CH4 emissions, and it is difficult to detect the initial stages of permafrost degradation before vegetation transitions have occurred. This study considers the use of a combination of ultra-high-resolution unoccupied aerial vehicle (UAV) data and Sentinel-1 and Sentinel-2 data to extrapolate field measurements of CH4 emissions from a set of vegetation types which capture the local variation in vegetation on degrading palsa wetlands. We show that the ultra-high-resolution UAV data can map spatial variation in vegetation relevant to variation in CH4 emissions and extrapolate these across the wider landscape. We further show how this can be integrated with Sentinel-1 and Sentinel-2 data. By way of a soft classification and simple correction of misclassification bias of a hard classification, the output vegetation mapping and subsequent extrapolation of CH4 emissions closely matched the results generated using the UAV data. Interferometric synthetic-aperture radar (InSAR) assessment of subsidence together with the vegetation classification suggested that high subsidence rates of palsa wetland can be used to quantify areas at risk of increased CH4 emissions. The transition of a 50 ha area currently experiencing subsidence to fen vegetation is estimated to increase emissions from 116 kg CH4 per season to emissions as high as 6500 to 13 000 kg CH4 per season. The key outcome from this study is that a combination of high- and low-resolution EO data of different types provides the ability to estimate CH4 emissions from large geographies covered by a fine mixture of vegetation types which are vulnerable to transitioning to CH4 emitters in the near future. This points to an opportunity to measure and monitor CH4 emissions from the Arctic over space and time with confidence.