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Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
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In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
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Updated: Jul 27, 2025

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Classification and Recognition of Building Appearance Based on Optimized Gradient-Boosted Decision Tree Algorithm.

Mengting Hu1, Lingxiang Guo2, Jing Liu1

  • 1School of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen 361024, China.

Sensors (Basel, Switzerland)
|June 10, 2023
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Summary

This study introduces an optimized gradient-boosted decision tree algorithm for accurate building classification in complex urban environments. The machine learning model achieves over 94% accuracy for R, S, and U-class buildings.

Keywords:
building classificationdecision tree algorithmk-fold cross-validation methodmodel cluster

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Area of Science:

  • Urban planning and architectural design
  • Machine learning applications in remote sensing
  • Geospatial data analysis

Background:

  • Urban spaces face challenges with complex land use identification.
  • Efficient building type classification is crucial for urban architectural planning.
  • Existing methods may lack the precision needed for diverse urban landscapes.

Purpose of the Study:

  • To develop and validate an enhanced machine learning algorithm for precise building type identification.
  • To improve the efficiency and scientific accuracy of building classification in urban planning.
  • To create a adaptable model capable of classifying buildings across various urban scales.

Main Methods:

  • Utilized an optimized gradient-boosted decision tree algorithm for building classification.
  • Employed supervised classification learning with a business-type weighted database.
  • Developed an innovative form database and applied k-fold cross-validation for model optimization and overfitting prevention.

Main Results:

  • The developed algorithm demonstrated high accuracy in building recognition.
  • Achieved over 94% accuracy in identifying R, S, and U-class buildings.
  • Successfully clustered models corresponding to different city sizes, allowing for scalable application.

Conclusions:

  • The optimized gradient-boosted decision tree algorithm offers a highly accurate solution for building classification.
  • This approach enhances scientific identification of building types in complex urban settings.
  • The model's adaptability to various city sizes makes it a valuable tool for urban planning.