Land-Use Classification Using Convolutional Neural Networks

Stepchenko, Arthur

doi:10.3103/s0146411621040088

Cited by 6 publications

(6 citation statements)

References 10 publications

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“…Parameter sharing describes this scenario. As a result of this procedure, CNN systems use less processing power than NN systems [35]. Multiple layers in a network can improve accuracy over a single one.…”

Section: Methodsmentioning

confidence: 99%

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Dalal

Seth²,

Rădulescu

et al. 2023

Sustainability

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Unfortunately, accidents caused by bad weather have regularly made headlines throughout history. Some of the more catastrophic events to recently make news include a plane crash, ship collision, railway derailment, and several vehicle accidents. The public’s attention has been directed to the severe issue of safety and security under extreme weather conditions, and many studies have been conducted to highlight the susceptibility of transportation services to environmental factors. An automated method of determining the weather’s state has gained importance with the development of new technologies and the rise of a new industry: intelligent transportation. Humans are well-suited for determining the temperature from a single photograph. Nevertheless, this is a more challenging problem for a fully autonomous system. The objective of this research is developing a good weather classifier that uses only a single image as input. To resolve quality-of-life challenges, we propose a modified deep-learning method to classify the weather condition. The proposed model is based on the Yolov5 model, which has been hyperparameter tuned with the Learning-without-Forgetting (LwF) approach. We took 1499 images from the Roboflow data repository and divided them into training, validation, and testing sets (70%, 20%, and 10%, respectively). The proposed model has gained 99.19% accuracy. The results demonstrated that the proposed model gained a much higher accuracy level in comparison with existing approaches. In the future, this proposed model may be implemented in real-time.

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Section: Methodsmentioning

confidence: 99%

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Dalal

Seth²,

Rădulescu

et al. 2023

Sustainability

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“…The ecological protection and high-quality development of the UR of the Yellow River should follow natural laws and fully utilize regional natural resources to form a harmonious model of hydrology, soil, climate, ecology, and humanity. To achieve high-quality development while protecting ecology, the water of the Yellow River must be "clean"; the loess must be "green", and the water must shift from "harmful" to "beneficial" [10][11].…”

Section: Ecological Suitabilitymentioning

confidence: 99%

Exploration on Ant Colony Optimization for Optimizing Land Use Spatial Allocation

Si,

2024

Proceedings of the First International Conference on Science, Engineering and Technology Practices for Sustainable Development,

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The purpose of optimizing the allocation of land use (LU) space is to enable effective utilization of land resources in various regions, in order to maximize the economic and social benefits of limited land area. The upper reaches (UR) of the Yellow River are important agricultural production bases and ecological barriers in China, and optimizing land space is of great significance for promoting agricultural development and protecting the ecological environment. Therefore, the optimization method based on ant colony optimization (ACO), as a new type of optimization algorithm, has strong adaptability, robustness, and global search ability, which can effectively solve the problem of LU spatial optimization configuration. This article determined the definition and constraints of land issues, and set relevant parameters. The steps of the ACO were analyzed, and the results of the algorithm were analyzed and evaluated. The LU situation in the UR of the Yellow River was analyzed using survey methods and statistical analysis. The results showed that the patch density index before and after optimization in various regions was generally small, with the index values of forest land and construction land being 0.1142 and 0.2975, respectively.

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“…The pooling layers reduce the spatial dimension of the convolutional layers' output feature vector and extract the most useful high-level features with a moving kernel window [24,31,32]. There are multiple pooling layers, such as max pooling, average pooling, and mean pooling layers.…”

Section: Overview Of DL Cnnsmentioning

confidence: 99%

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach

Ali

Johnson

2022

Sensors

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Detailed Land-Use and Land-Cover (LULC) information is of pivotal importance in, e.g., urban/rural planning, disaster management, and climate change adaptation. Recently, Deep Learning (DL) has emerged as a paradigm shift for LULC classification. To date, little research has focused on using DL methods for LULC mapping in semi-arid regions, and none that we are aware of have compared the use of different Sentinel-2 image band combinations for mapping LULC in semi-arid landscapes with deep Convolutional Neural Network (CNN) models. Sentinel-2 multispectral image bands have varying spatial resolutions, and there is often high spectral similarity of different LULC features in semi-arid regions; therefore, selection of suitable Sentinel-2 bands could be an important factor for LULC mapping in these areas. Our study contributes to the remote sensing literature by testing different Sentinel-2 bands, as well as the transferability of well-optimized CNNs, for semi-arid LULC classification in semi-arid regions. We first trained a CNN model in one semi-arid study site (Gujranwala city, Gujranwala Saddar and Wazirabadtownships, Pakistan), and then applied the pre-trained model to map LULC in two additional semi-arid study sites (Lahore and Faisalabad city, Pakistan). Two different composite images were compared: (i) a four-band composite with 10 m spatial resolution image bands (Near-Infrared (NIR), green, blue, and red bands), and (ii) a ten-band composite made by adding two Short Wave Infrared (SWIR) bands and four vegetation red-edge bands to the four-band composite. Experimental results corroborate the validity of the proposed CNN architecture. Notably, the four-band CNN model has shown robustness in semi-arid regions, where spatially and spectrally confusing land-covers are present.

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Land-Use Classification Using Convolutional Neural Networks

Cited by 6 publications

References 10 publications

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Optimized Deep Learning with Learning without Forgetting (LwF) for Weather Classification for Sustainable Transportation and Traffic Safety

Exploration on Ant Colony Optimization for Optimizing Land Use Spatial Allocation

Land-Use and Land-Cover Classification in Semi-Arid Areas from Medium-Resolution Remote-Sensing Imagery: A Deep Learning Approach

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