Categories Of Features Research Articles

The Role of Categorical and Metrical Stimulus Features in Action Selection

The Role of Categorical and Metrical Stimulus Features in Action Selection

Semantic Correlation Transfer for Heterogeneous Domain Adaptation.

Heterogeneous domain adaptation (HDA) is expected to achieve effective knowledge transfer from a label-rich source domain to a heterogeneous target domain with scarce labeled data. Most prior HDA methods strive to align the cross-domain feature distributions by learning domain invariant representations without considering the intrinsic semantic correlations among categories, which inevitably results in the suboptimal adaptation performance across domains. Therefore, to address this issue, we propose a novel semantic correlation transfer (SCT) method for HDA, which not only matches the marginal and conditional distributions between domains to mitigate the large domain discrepancy, but also transfers the category correlation knowledge underlying the source domain to target by maximizing the pairwise class similarity across source and target. Technically, the domainwise and classwise centroids (prototypes) are first computed and aligned according to the feature embeddings. Then, based on the derived classwise prototypes, we leverage the cosine similarity of each two classes in both domains to transfer the supervised source semantic correlation knowledge among different categories to target effectively. As a result, the feature transferability and category discriminability can be simultaneously improved during the adaptation process. Comprehensive experiments and ablation studies on standard HDA tasks, such as text-to-image, image-to-image, and text-to-text, have demonstrated the superiority of our proposed SCT against several state-of-the-art HDA methods.

Types of use of peripheral forms of the creative case in the aspect of adverbialization (the substantive word form "in a whisper")

The stepwise nature of adverbial transposition of nouns without prepositions is investigated. The object of study is the forms of the creative case of nouns without prepositions, representing the first stage of transposition into adverbs, the subject of consideration is combinatorics and the proportion of substantive and adverbial features in the structure of word forms of the "whisper" type, functioning in the zone of the periphery of the noun class. The relevance of the research is due to the need to develop such a field of scientific research as the transpositional grammar of the Russian language, as well as to identify patterns of functioning of linguistic units in transitional zones between parts of speech and interparticle semantic and syntactic categories. The purpose of the article is a comprehensive analysis of substantive word forms that demonstrate different degrees of manifestation of signs interacting with the functional adverbialization of nouns and adverbs on two stages of the noun zone. Within the framework of the proposed approach to syncretic formations, methods of oppositional analysis (with an adverbial scale), elements of component, distributive analysis, and linguistic experiment are used. The novelty of the study consists in the description of a formalized methodology for establishing the stages of adverbialization of substantive word forms, demonstrating a different degree of departure from nouns and approximation to adverbs. It is established that the substantive word form "whisper", for example, is characterized by a multi-stage transposition into an adverbial class. In the zone of the periphery of nouns, she makes the first, actually syntactic step towards adverbs. It is associated with its use first in the syncretic function of complement and circumstance, and then only in the function of circumstance. The presence of dependent words in the form of adjectival and subsubstantial distributors, as well as the function of object complement, restrain the further advancement of the word form "in a whisper" towards the class of adverbs. The loss of object meaning in the word form under consideration is associated with a shift at the level of verbal subordination: weak control as a way of connecting the word form "in a whisper" with the main word is replaced by case adjunction. Object-circumstantial relations are transformed into circumstantial ones. But the main categorical features of nouns in the zone of the periphery of this class of words are preserved. The theoretical significance of the work lies in the fact that it contains an understanding of transitional phenomena in the field of parts of speech in a systemic linguistic aspect. The practical significance of the research is determined by the possibility of applying its results in teaching linguistic disciplines – Russian, general linguistics, theoretical grammar.

Ransomware Threats in Industrial Internet of Things Networks: A Detection Approach

The Industrial Internet of Things (IIoT) is a challenging environment for ransomware threats, and it requires robust detection mechanisms to protect critical infrastructures. This study explores the complex landscape of ransomware attacks in IIoT and suggests proactive detection strategies. To develop an advanced detection model, this research uses the CATBoost algorithm that can handle categorical features by leveraging a comprehensive dataset that captures various attributes of ransomware incidents. The study also enhances the interpretability of the model by incorporating SHAP (SHapley Additive exPlanations) which explains how individual features affect ransomware identification in IIoT environments. Empirical evaluation demonstrates that the model can accurately classify ransomware instances with high precision and recall rates. Moreover, SHAP explanation reveals important features that influence the decisions made by the model thereby improving its interpretability and trustworthiness. The experimental results indicate that customized detection approaches are important and highlight the effectiveness of CATBoost algorithm in strengthening IIoT systems against ransomware attacks.

Dynamic comprehensive quality assessment of post-harvest grape in different transportation chains using SAHP–CatBoost machine learning

Abstract Objectives Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value. Due to high environmental sensitivity, the post-harvest quality of fresh fruit will undergo complex changes in the supply chain, with various dynamic quality-related features. It is difficult to efficiently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics (numerical and categorical). Therefore, we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process (SAHP) integrated with the CatBoost model. Materials and Methods By adaptive weight optimization, the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value, as an output sample of CatBoost machine learning. Then, using heterogeneous monitoring data as input, the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status. Results Three quality index monitoring data sets for ‘Jufeng’ grape in different transportation chains (normal temperature, cold insulation, and cold chain) were individually constructed as the research samples. Furthermore, compared to other machine learning methods, the SAHP–CatBoost had more accurate results in comprehensive quality feature extraction. In actual transportation chains, the mean absolute error, mean absolute percentage error, and root mean squared error of dynamic comprehensive assessment were limited to 0.0044, 1.012%, and 0.0078, respectively. Conclusions The proposed method is efficient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator. It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefits.

Emotion Classification on Social Media Comments Using Categorical Feature Extraction Along With the Bidirectional Encoder-based Recurrent Neural Network Classification

All across the world, social media is one of the most widely used platforms for information exchange. Comments on relevant information might be made in response to a video or any other piece of information. A remark may include an emotion that may be recognized by an automated recognition system. On Facebook, Twitter, and YouTube comments, we performed studies to determine their emotional categorization. A set of comments is gathered and manually classified using six fundamental emotion labels (happy, sad, angry, surprised, disgust, and fear) and one neutral label, with each emotion label representing a different emotion category. A prominent approach in natural language processing (NLP), deep learning has been used in a wide range of categorization applications. This procedure begins by preprocessing the input data with normalization, followed by categorizing characteristics in feature extraction utilizing the Linguistic and word count analysis (LIWC). Finally, for the categorization stage, the classify features might be supplied. Finally, for categorizing emotions, the Bidirectional Encoder based recurrent neural network classification approach is used. The studies have been carried out with the use of typical social media data that has been acquired from the kaggle data repository. The findings show that the suggested model outperforms all other existing mechanisms in terms of overall performance.

Suç Kategorisi Tespiti için Yığınlama Topluluk Öğrenimi Modeli Kullanan Çatı Tasarımı

Crime refers to an action legally defined as harmful to society, and it is important to understand the type of crime to prevent these actions. However, crime can occur at any time and place, making it difficult to predict. Data generated based on previously committed crimes contributes to overcoming this difficulty. This study proposes a novel model for classifying criminal activities using a Doc2Vec that can cause a numerical representation of texts regardless of length and a stacking ensemble model that includes 8 different machine-learning models. Unlike the literature, the model processes the features as text and converts them into vectors rather than categorically. In this way, it enables using features that cannot be used in the literature. The proposed model is tested using a distributed online competition database, Francisco Crime Classification, which contains crimes committed over 12 years. An accuracy value of 99.28% was obtained for the 15 crime categories with the highest crime records, while precision, recall, and f-score values were 99.18%, 99.38%, and 99.20%, respectively. With cross-validation (k=10), 99.80% performance was achieved with a std. value of 0.001. These performance values are higher than those of all the studies in the literature using categorical feature structures. The results show that converting criminal activity reports, which contain text-based features, into vectors that can be processed with natural language processing techniques such as Doc2vec instead of using them categorically in model training can directly contribute to the classification performance and provide a more efficient model with less preprocessing.

Ultrasound radiomics based XGBoost model to differential diagnosis thyroid nodules and unnecessary biopsy rate: Individual application of SHapley additive exPlanations.

Radiomics-based eXtreme gradient boosting (XGBoost) model was developed to differentiate benign thyroid nodules from malignant thyroid nodules and to prevent unnecessary thyroid biopsies, including positive and negative effects. The study evaluated a data set of ultrasound images of thyroid nodules in patients retrospectively, who initially received ultrasound-guided fine-needle aspiration biopsy (FNAB) for diagnostic purposes. According to ACR TI-RADS, a total of five ultrasound feature categories and the maximum size of the nodule were determined by four radiologists. A radiomics score was developed by the LASSO algorithm from the ultrasound-based radiomics features. An interpretative method based on Shapley additive explanation (SHAP) was developed. XGBoost was compared with ACR TI-RADS for its diagnostic performance and FNAB rate and was compared with six other machine learning models to evaluate the model performance. Finally, 191 thyroid nodules were examined from 177 patients. The radiomics score were calculated using 8 features, which were selected among 789 candidate features generated from the ultrasound images. The model yielded an AUC of 93% in the training cohort and 92% in the test cohort. It outperformed traditional machine learning models in assessing the nature of thyroid nodules. Compared with ACR TI-RADS, the FNAB rate decreased from 34% to 30% in training and from 35% to 41% in test. The radiomics-based XGBoost model proposed could distinguish benign and malignant thyroid nodules, thereby reduced significantly the number of unnecessary FNAB. It was effective in making preoperative decisions and managing selected patients using the SHAP visual interpretation tools.

The Status of Indigenous Peoples in International Law

Introduction. The International Decade of Indigenous Languages (2022–2032) was proclaimed in 2022 by the UN system. Thus, the problem of indigenous peoples acquires increased relevance in international law. At the same time, the definition of the concept of “indigenous peoples” has not yet been developed in UN law. Identification of the characteristic features of this category has become the task of the study. Methods. General scientific and special methods for legal science, primarily formal legal analysis, underlie the methodology used. Theoretical conclusions are supported by examples of international judicial practice. Results. Specific features of indigenous peoples as a special subject of international human rights law are shown in the article. The author proposes to create a special international legal regime for the protection of those indigenous peoples who are endangered. Discussion and Conclusion. Based on the results of the study, a number of conclusions were drawn. The author considers the most significant: three features can be considered generally recognized. The only special feature (compared to the features of the generic category – the people in international law) is the original, historical connection with the territory of residence. The geographical area in which this people or their ancestor lived during the period of its conquest or colonization, or during the establishment of existing state borders, is proposed to be understood as the territory of the indigenous people. The notion of indigenous peoples as exclusively post-colonial or as not emerging from the stage of tribal development, is subject to criticism as discriminatory. The special qualities of indigenous peoples, primarily civilizational (ethnic) identity, must be protected by the norms of international law.

TrajTransGCN: Enhancing trajectory prediction by fusing transformer and graph neural networks

This paper proposes a novel model named TrajTransGCN for taxi trajectory prediction, which leverages the power of both graph convolutional networks (GCNs) and Transformer. TrajTransGCN first passes the input through the GCN layer and then combines the GCN outputs with one-hot encoded categorical features as input to the transformer layer. This paper evaluates. TrajTransGCN uses real-world taxi trajectory datasets in Porto and compares it against several baselines. The experimental results show that TrajTransGCN outperforms all the other models in terms of both RMSE and MAPE. Specifically, the model achieves an RMSE of 0.0247 and a MAPE of 0.09%, which are significantly lower than those of the other models. The results demonstrate the effectiveness of the proposed model in predicting taxi trajectories, indicating the potential of leveraging both GCN and transformer layers in trajectory prediction tasks. In addition, this paper includes ablation experiments to demonstrate the effectiveness of using one-hot encodings of classification labels in complex real-time scenarios. In addition, a parameter study is carried out to examine how the TrajTransGCN's performance is impacted by the learning rate, the quantity of Transformer layers, and the size of the hidden dimension of the Transformer layer.

Культурные и лингвокультурные коды: теоретический обзор

The review paper aims to summarize scientific theoretical knowledge on the problem of codes presented in its humanitarian aspect. The paper provides various points of view on the essence, composition, structure and functioning of codes in general, culture and linguoculture codes in particular. The interaction of verbal and non-verbal codes is discussed. Special attention is paid to such a variety of linguoculture codes as figurative codes. The scientific novelty lies in the fact that the authors of this review paper attempt to provide insight into the concept of culture and linguoculture codes, to demonstrate scholars’ diverse opinions, coinciding views and their disagreements on this issue, which gives a detailed idea of the current state of the issue. As a result of studying various points of view of Russian linguists, semiologists and scholars in culture studies on the essence, composition, structure and functioning of codes, the review demonstrates the high importance attached to the notion of code within the entire spectrum of the humanities and the great effectiveness of the theory of codes, which acts as a general scientific epistemology that makes it possible to consider society, culture and language in a single system of categories. In addition, by extracting and bringing together the disparate features presented in the works on the subject under consideration, the review paper presented a list of essential (categorical) features collectively forming the notion of ‘culture code’.

Introducing packet-level analysis in programmable data planes to advance Network Intrusion Detection

Programmable data planes offer precise control over the low-level processing steps applied to network packets, serving as a valuable tool for analysing malicious flows in the field of intrusion detection. Albeit with limitations on physical resources and capabilities, they allow for the efficient extraction of detailed traffic information, which can then be utilised by Machine Learning (ML) algorithms responsible for identifying security threats. In addressing resource constraints, existing solutions in the literature rely on compressing network data through the collection of statistical traffic features in the data plane. While this compression saves memory resources in switches and minimises the burden on the control channel between the data and the control plane, it also results in a loss of information available to the Network Intrusion Detection System (NIDS), limiting access to packet payload, categorical features, and the semantic understanding of network communications, such as the behaviour of packets within traffic flows. This paper proposes P4DDLe, a framework that exploits the flexibility of P4-based programmable data planes for packet-level feature extraction and pre-processing. P4DDLe leverages the programmable data plane to extract raw packet features from the network traffic, categorical features included, and to organise them in a way that the semantics of traffic flows are preserved. To minimise memory and control channel overheads, P4DDLe selectively processes and filters packet-level data, so that only the features required by the NIDS are collected. The experimental evaluation with recent Distributed Denial of Service (DDoS) attack data demonstrates that the proposed approach is very efficient in collecting compact and high-quality representations of network flows, ensuring precise detection of DDoS attacks.

How Do Shoppers Respond to Noisy Signals on Price Changes? Evidence from a Field Experiment in Online Supermarket Shopping*

What is the effect on the demand for both discounted and nondiscounted products, when promotional material informs shoppers that some product categories feature discounts? We address this question by conducting a field experiment on a website for online grocery shopping. We find that shoppers who had purchased in a certain food category prior to the experiment responded to noisy promotional information by purchasing items in the discounted category that they had already purchased in the past, regardless of whether the item purchased was currently discounted. Our results suggest that coarse information on discounts increases both consumer spending and seller revenue.

Late pre-Hispanic fog oasis settlements and long-term human occupation on the Peruvian central coast from satellite imagery

Fog oases (lomas) present pockets of verdant vegetation within the arid coastal desert of Andean South America and archaeological excavation within some of the oases has revealed a long history of human exploitation of these landscapes. Yet lomas settlements are under-represented in archaeological datasets due to their tendency to be located in remote inter-valley areas. Here, the authors employ satellite imagery survey to map the locations of anthropogenic surface features along the central Peruvian coast. They observe two categories of archaeological features, large corrals and clustered structures, and document a concentration of settlement features within lomas landscapes that suggests a pre-Hispanic preference for both short- and long-term occupation of these verdant oases.

Семантико-синтаксичний потенціал синкретичних підрядно-сурядних сполучників

Semantic and syntactic potential of syncretic subjunctive-sequential conjunctions (on the material of V. Shevchuk’s novels) The article is devoted to the analysis of the semantic and syntactic potential of syncretic conjunctions of subordinate-coordinate in the idiostyle of Valery Shevchuk. The specifics of the syncretism of connecting means were studied on the material of the novels «House on the Mountain», «On the Humble Field», «Eye of the Abyss», «The Beginning of Horror», «The Ghost of the Dead House», «Three Leaves Outside the Window». L. V. Shytyk’s classification of syncretic connectives, developed on the basis of K. G. Horodenska’s typology of conоjuctions, became important for our research. The functional potential of highlighted conjunctions of subordination-sequential as markers of semantic-syntactic relations in syncretic complex sentences was investigated. Are characterized the salient features of such semantic categories of the analyzed conjunctions as: explanatory-distinctive, accented-distinctive, explanatory-exploratory, temporal-comparative, conditional-comparative, permissive-opposite. Types of conjunctions are correlated with two types of syncretic complex sentences: subordinate in form and coordinate in meaning (explanatory and comparative subtypes) and subordinate-sequential in form and meaning (permissive-opposite subtype). Analogous semantic-syntactic relations for parceled constructions have been clarified. In the syntactic version of a simple complex sentence, the analyzed conjunctions are interpreted as markers of the separation of the secondary sentence elements and the homogeneity of the connection between the elements of the sentence. Grammatical and semantic-syntactic features of the author’s style within the boundaries of the researched topic are determined. Examples from the writer’s works confirmed the active functioning of each of the subgroups of subordination-sequential conjunctions and the peculiarities of their semantic and syntactic manifestation. Among the explanatory-distinctive conjunctions, the most actively used is – зокрема, among the accented-distinctive ones – особливо, within the explanatory-finding ones – а саме, temporal-comparative, conditional-comparative – тоді як, коли …то, permissive-opposite – хай …але, хоч …але. Quantitative indicators of the frequency of use of each сonnective mean were determined. The subgroup of accented-distinctive conjunctions is the least represented in the novels of V. Shevchuk, the group of permissive-opposite ones is the most numerous. Keywords: subordinate-coordinate conjunctions, explanatory-distinctive, accented-distinctive, explanatory-exploratory, temporal-comparative, conditional-comparative, permissive-opposite, novels of Valery Shevchuk.

Performance Evaluation and Comparative Analysis of Machine Learning Models on the UNSW-NB15 Dataset: A Contemporary Approach to Cyber Threat Detection

This research work utilizes the University of New South Wales Network Based 2015 (UNSW-NB15) dataset to investigate the dynamic nature of cyber threats, departing from the obsolete Knowledge Discovery and Data Mining competition 1999 (KDD Cup99) dataset. The data preparation pipeline consists of essential procedures aimed at ensuring the integrity and appropriateness of the data for analysis. The method begins by removing null values, thereafter, applying one-hot encoding to categorical features, min-max scaling for data normalization, and label encoding for efficient management of binary labels. The process of feature selection is conducted utilizing the Pearson coefficient correlation. An exhaustive evaluation is conducted on six machine learning models for the purpose of binary classification. The evaluation takes into account key performance measures like accuracy, precision, recall, and F1 score. The Random Forest model demonstrated exceptional performance, with a remarkable accuracy of 99% and a robust F1 score of 98%. Additionally, it exhibited a well-balanced precision and recall at 98%. The Support Vector Machine, Gradient Boosting, Logistic Regression, Decision Tree, and K-Nearest Neighbors models exhibit notable performance, achieving accuracy and F1 scores around at the 98% level. During our investigation into multi-class classification research, we thoroughly examined numerous machine learning models, all of which exhibited robust performance, with accuracy rates ranging from 97% to 98%. The aforementioned results highlight the efficacy of these models in accurately classifying data, regularly achieving high levels of precision, recall, and F1 scores for positive case predictions. This study offers a current viewpoint on the identification of cyber threats and emphasizes the appropriateness of several machine learning models in this rapidly changing field.

Time-frequency supervised contrastive learning via pseudo-labeling: An unsupervised domain adaptation network for rolling bearing fault diagnosis under time-varying speeds

The varying speed can cause the significant data distribution shift of bearings, making it difficult for deep learning-based bearing fault diagnosis models to ensure good generalization. Domain adaptation methods have been developed to address the domain shifts, while they struggle with the class-invariant features extraction under variable speed. Accordingly, a time–frequency supervised contrastive learning framework (TF-SupCon) is proposed for unsupervised cross-speed fault diagnosis of bearing. TF-SupCon adopts a pre-training-downstream task framework that aims to extract speed immune class-invariant features. During the pre-training phase, the physical consistency of the time-domain information and the frequency-domain information of bearing signal, a general feature applicable to different speed conditions, is learned in a targeted manner through supervised contrastive learning. Additionally, a K-Nearest Neighbor (KNN) algorithm based on cosine distance is designed to assign pseudo-labels to unlabeled target domain data, enabling cross-domain supervised contrastive pre-training. In the downstream task, unsupervised cross-domain fault diagnosis is performed using a KNN classifier and the speed immune time–frequency features by the trained encoders. It is worth noting that the same metric is maintained throughout both the pre-training phase and the downstream task, ensuring organic connection between the two stages. The superiority of TF-SupCon was demonstrated through a variety of fault diagnosis experiments conducted on both public and self-collected datasets. Lastly, the distances between time-domain and frequency-domain features of different categories were studied to verify the physical consistency between the features.

Machine learning model for predicting immediate postoperative desaturation using spirometry signal data

Postoperative desaturation is a common post-surgery pulmonary complication. The real-time prediction of postoperative desaturation can become a preventive measure, and real-time changes in spirometry data can provide valuable information on respiratory mechanics. However, there is a lack of related research, specifically on using spirometry signals as inputs to machine learning (ML) models. We developed an ML model and postoperative desaturation prediction index (DPI) by analyzing intraoperative spirometry signals in patients undergoing laparoscopic surgery. We analyzed spirometry data from patients who underwent laparoscopic, robot-assisted gynecologic, or urologic surgery, identifying postoperative desaturation as a peripheral arterial oxygen saturation level below 95%, despite facial oxygen mask usage. We fitted the ML model on two separate datasets collected during different periods. (Datasets A and B). Dataset A (Normal 133, Desaturation 74) was used for the entire experimental process, including ML model fitting, statistical analysis, and DPI determination. Dataset B (Normal 20, Desaturation 4) was only used for verify the ML model and DPI. Four feature categories—signal property, inter-/intra-position correlation, peak value/interval variability, and demographics—were incorporated into the ML models via filter and wrapper feature selection methods. In experiments, the ML model achieved an adequate predictive capacity for postoperative desaturation, and the performance of the DPI was unbiased.

A Comprehensive Study on Nanoparticle Drug Delivery to the Brain: Application of Machine Learning Techniques.

The delivery of drugs to specific target tissues and cells in the brain poses a significant challenge in brain therapeutics, primarily due to limited understanding of how nanoparticle (NP) properties influence drug biodistribution and off-target organ accumulation. This study addresses the limitations of previous research by using various predictive models based on collection of large data sets of 403 data points incorporating both numerical and categorical features. Machine learning techniques and comprehensive literature data analysis were used to develop models for predicting NP delivery to the brain. Furthermore, the physicochemical properties of loaded drugs and NPs were analyzed through a systematic analysis of pharmacodynamic parameters such as plasma area under the curve. The analysis employed various linear models, with a particular emphasis on linear mixed-effect models (LMEMs) that demonstrated exceptional accuracy. The model was validated via the preparation and administration of two distinct NP formulations via the intranasal and intravenous routes. Among the various modeling approaches, LMEMs exhibited superior performance in capturing underlying patterns. Factors such as the release rate and molecular weight had a negative impact on brain targeting. The model also suggests a slightly positive impact on brain targeting when the drug is a P-glycoprotein substrate.

Multi-scale multi-temporal landscape pattern analysis using high spatial urban scene classification

ABSTRACTUrban landscape pattern analysis is conducive to understanding the land cover distribution and changes and promoting urban spatial layout rationality and sustainable development. Previous research has often used low and medium spatial resolution data to classify urban land cover at the pixel level and then analyse landscape pattern indices. However, the underlying semantic information provided by pixel classification needs to consider the spatial correlation of surface features, and it is challenging to obtain high-level semantic information on urban functional areas and analyse the relationship between human activities and land use. Leveraging the abundant spatial characteristics in high-resolution remote sensing images, high spatial urban scene classification can infer the feature categories and spatial relationships between features to realize higher-level image interpretation and obtain regional urban scene details. And it contributes to urban planning and decision-making, better meeting the different demands. Therefore, this study aims at a novel methodological framework for landscape pattern analysis of cross-temporal multi-scale urban scene classification. The results imply that: (1) The high-resolution images can make up for the lack of information on urban functional zones in low and medium resolution images; (2) Landscape pattern analysis based on scene classification can analyse urban spatial relationships more rationally than traditional research; and (3) With the continuous advancement of urbanization, the urban functional zoning becomes more apparent, and the dominant industries have changed significantly from industry to commerce and services. This study expands the analytical application range of landscape ecology and remote sensing technology and provides new inspirations for urban planning, environmental monitoring, ecological protection.