A Hybrid Image Segmentation Method for Accurate Measurement of Urban Environments

Kim, Hyungjoon; Lee, Jae Ho; Lee, Suan

doi:10.3390/electronics12081845

Cited by 4 publications

(1 citation statement)

References 52 publications

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“…Initially, deep learning models designed for multistep-ahead forecasting were contrasted with HYTREM. These baseline models, some of which were based on attention mechanisms, demonstrated their effectiveness across a wide variety of domains [52,53]. To establish a comparable environment for day-ahead solar irradiance prediction, 11th time points were employed from these models.…”

Section: Performance Comparisonmentioning

confidence: 99%

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

Leem

et al. 2023

Electronics

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This study introduces HYTREM, a hybrid tree-based ensemble learning model conceived with the sustainable development of eco-friendly transportation and renewable energy in mind. Designed as a digital model, HYTREM primarily aims to enhance solar power generation systems’ efficiency via accurate solar irradiance forecasting. Its potential application extends to regions such as Jeju Island, which is committed to advancing renewable energy. The model’s development process involved collecting hourly solar irradiance and weather-related data from two distinct regions. After data preprocessing, input variables configuration, and dataset partitioning into training and testing sets, several tree-based ensemble learning models—including extreme gradient boosting, light gradient boosting machine, categorical boosting, and random forest (RF)—were employed to generate prediction values in HYTREM. To improve forecasting accuracy, separate RF models were constructed for each hour. Experimental results validated the superior performance of HYTREM over state-of-the-art models, demonstrating the lowest mean absolute error, root mean square error (RMSE), and normalized RMSE values across both regions. Due to its transparency and efficiency, this approach suits energy providers with limited computational resources. Ultimately, HYTREM is a stepping stone towards developing advanced digital twin systems, highlighting the importance of precise forecasting in managing renewable energy.

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Section: Performance Comparisonmentioning

confidence: 99%

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

Leem

et al. 2023

Electronics

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An Active Contour Model Based on Local Entropy for Image Segmentation with High Noise

Li,

Wang,

Weng

et al. 2024

2024 39th Youth Academic Annual Conference of Chinese Association of Automation (YAC)

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Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

Arulananth,

Kuppusamy,

Ayyasamy

et al. 2024

PLoS ONE

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Semantic segmentation of cityscapes via deep learning is an essential and game-changing research topic that offers a more nuanced comprehension of urban landscapes. Deep learning techniques tackle urban complexity and diversity, which unlocks a broad range of applications. These include urban planning, transportation management, autonomous driving, and smart city efforts. Through rich context and insights, semantic segmentation helps decision-makers and stakeholders make educated decisions for sustainable and effective urban development. This study investigates an in-depth exploration of cityscape image segmentation using the U-Net deep learning model. The proposed U-Net architecture comprises an encoder and decoder structure. The encoder uses convolutional layers and down sampling to extract hierarchical information from input images. Each down sample step reduces spatial dimensions, and increases feature depth, aiding context acquisition. Batch normalization and dropout layers stabilize models and prevent overfitting during encoding. The decoder reconstructs higher-resolution feature maps using "UpSampling2D" layers. Through extensive experimentation and evaluation of the Cityscapes dataset, this study demonstrates the effectiveness of the U-Net model in achieving state-of-the-art results in image segmentation. The results clearly shown that, the proposed model has high accuracy, mean IOU and mean DICE compared to existing models.

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A Hybrid Image Segmentation Method for Accurate Measurement of Urban Environments

Cited by 4 publications

References 52 publications

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

A Hybrid Ensemble Model for Solar Irradiance Forecasting: Advancing Digital Models for Smart Island Realization

An Active Contour Model Based on Local Entropy for Image Segmentation with High Noise

Semantic segmentation of urban environments: Leveraging U-Net deep learning model for cityscape image analysis

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