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Related Concept Videos

Upsampling01:22

Upsampling

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Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
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Downsampling01:20

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When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Topographic surveying is critical for documenting the Earth's surface, focusing on capturing elevations, slopes, and natural and man-made features. It is essential in construction planning, water resource management, and land-use analysis. The primary outcome of such surveys is a topographic map, which uses contour lines to visually represent the shape and slope of the terrain, providing valuable insights into the landscape's characteristics.Contour lines are fundamental to understanding the...
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Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Depth Perception and Spatial Vision01:15

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Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
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Related Experiment Video

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GreenViT: A Vision Transformer with Single-Path Progressive Upsampling for Urban Green-Space Segmentation and

Ziqiang Xu1, Young Choi2, Changyong Yi3

  • 1Department of Robot and Smart System Engineering, Kyungpook National University, 80, Daehak-ro, Buk-gu, Daegu 41566, Republic of Korea.

Journal of Imaging
|February 26, 2026
PubMed
Summary

GreenViT precisely quantifies urban green space using a Vision Transformer (ViT). This framework balances accuracy and efficiency for reliable green-area metrics supporting urban planning and environmental management.

Keywords:
deep learningimage processingremote sensing imageurban green space monitoringvision transformer

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Urban Planning

Background:

  • Urban green-space monitoring faces accuracy-efficiency trade-offs.
  • Existing methods lack integrated, auditable area estimation.
  • Dense cityscapes present challenges for precise green-space quantification.

Purpose of the Study:

  • Introduce GreenViT, a Vision Transformer (ViT)-based framework for precise urban green-space segmentation and quantification.
  • Address limitations in accuracy, efficiency, and auditable area estimation for urban green spaces.
  • Develop a reliable tool for decision-oriented long-term monitoring and management assessments.

Main Methods:

  • Utilized a ViT-L/14 backbone with a progressive upsampling decoder (Green Head).
  • Employed a sliding window sampling scheme on high-resolution satellite imagery.
  • Conducted experiments on a manually annotated dataset covering five land-cover classes.

Main Results:

  • Achieved high performance metrics: 0.9200 mIoU, 0.9580 Dice, and 0.9570 PA.
  • Demonstrated a low relative area error of 1.10% with a calibrated estimator.
  • Showcased suitability for thin or boundary-rich green-space classes.

Conclusions:

  • GreenViT offers a strong balance between accuracy and efficiency in urban green-space monitoring.
  • The framework provides reliable green-area metrics for urban heat mitigation and pollution control.
  • GreenViT supports various applications including planning evaluations, urban renewal, and ecological verification.