Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms

Liu, Man; Fu, Bolin; Fan, Donglin; Zuo, Pingping; Xie, Shaofeng; He, Hongchang; Liu, Lilong; Huang, Liangke; Gao, Ertao; Zhao, Min

doi:10.1016/j.jag.2021.102531

Cited by 17 publications

(5 citation statements)

References 47 publications

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“…By integrating data sources to assess the inner quality and accommodate specific preferences, AI-driven systems can deliver substantiated health recommendations in real time. This capability has the implicit to significantly ameliorate public health issues by using AI and IoT algorithms to optimize air quality operation according to individual conditions and preferences [101].…”

Section: B Personalized Health Recommendations Based On Occupant Pref...mentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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This exploratory disquisition delves into the world of Indoor Air Quality( IAQ) monitoring systems, using the solidarity of Artificial Intelligence( AI) and Internet of Effects ( IoT) technologies. Its overarching thing is to check the efficacity of these structures in regulating IAQ within structures, with a specific focus on mollifying pollutant degrees and their dangerous results on inhabitants. The study undertakes a comprehensive review of present literature and exploration trials, which depend upon AI and IoT algorithms for border monitoring, records analysis, and contrivance evaluation. also, it delves into the complications of machine armature, deployment ways, and functional efficiency. Furthermore, the exploration attracts different instructional budgets, including clever detectors and IoT bias stationed within the ambient surroundings. It elucidates the functionality of those instruments to accumulate real-time statistics, encompassing variables together with unpredictable natural composites, temperature oscillations, and moisture ranges. A vital aspect of this study is the disquisition of AI, contrivance getting to know Machine Learning ( ML), and Deep Learning ( DL) algorithms, showcasing their prophetic prowess within shadowing fabrics. also, they have a look at delving into the symbiotic dating among those algorithms, expounding their function in enhancing machine delicacy and optimizing energy intake. Moreover, the studies trials to delineate personalized health tips knitter-made to character inhabitants, decided from the wealth of records accrued through these structures. By integrating present-day technologies with empirical perceptivity, this takes a look at trials to pave the manner for better IAQ control strategies, fostering more healthy and lesser sustainable lodging surroundings.

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Section: B Personalized Health Recommendations Based On Occupant Pref...mentioning

confidence: 99%

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

Qasim Flayyih,

Waleed,

M. Ibrahim

2024

IJCDS

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“…Firstly, this study obtained image objects through the image segmentation algorithm provided by GEE. The pixel-based crop rotation results were then optimized based on the object layers using the majority voting scheme, which has been used in previous studies [42,43]. Then, the final crop rotation map for Shandong for 2020 was generated.…”

Section: Crop Type Identification and Rotation Mappingmentioning

confidence: 99%

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

2022

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Accurate crop rotation information is essential for understanding food supply, cropland management, and resource allocation, especially in the context of China’s basic situation of “small farmers in a big country”. However, crop rotation mapping for smallholder agriculture systems remains challenging due to the diversity of crop types, complex cropping practices, and fragmented cropland. This research established a sub-seasonal crop information identification framework for crop rotation mapping based on time series Sentinel-2 imagery. The framework designed separate identification models based on the different growth seasons of crops to reduce interclass similarity caused by the same crops in a certain growing season. Features were selected separately according to crops characteristics, and finally explored rotations between them to generate the crop rotation map. This framework was evaluated in the study area of Shandong Province, China, a mix of single-cropping and double-cropping smallholder area. The accuracy assessment showed that the two crop maps achieved an overall accuracy of 0.93 and 0.85 with a Kappa coefficient of 0.86 and 0.80, respectively. The results showed that crop rotation practice mainly occurred in the plains of Shandong, and the predominant crop rotation pattern was wheat and maize. In addition, Land Surface Water Index (LSWI), Soil-Adjusted Vegetation Index (SAVI), Green Chlorophyll Vegetation Index (GCVI), red-edge, and other spectral bands during the peak growing season enabled better performance in crop mapping. This research demonstrated the capability of the framework to identify crop rotation patterns and the potential of the multi-temporal Sentinel-2 for crop rotation mapping under smallholder agriculture system.

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“…With the respective characteristics of the two methods, this research proposed a fusion method that combines the advantages of pixelbased and object-oriented classification methods to obtain better classification accuracy. The fusion strategy has been mentioned in some previous studies [19,20], but in related studies, the final result was to constrain the pixel-based classification with the image segmentation objects in terms of majority voting [18,21]. In this paper, finer constraint rules were used to optimize the classification results while preserving the fine features obtained from pixelbased classification.…”

Section: Introductionmentioning

confidence: 99%

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

Feng,

Chen,

Liu

et al. 2024

Applied Sciences

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Wheat plays an important role in China’s and the world’s food supply, and it is closely related to economy, culture and life. The spatial distribution of wheat is of great significance to the rational planning of wheat cultivation areas and the improvement of wheat yield and quality. The current rapid development of remote sensing technology has greatly improved the efficiency of traditional agricultural surveys. The extraction of crop planting structure based on remote sensing images and technology is a popular topic in many researches. In response to the shortcomings of traditional methods, this research proposed a method based on the fusion of the pixel-based and object-oriented methods to map the spatial distribution of winter wheat. This method was experimented and achieved good results within Shandong Province. The resulting spatial distribution map of winter wheat has an overall accuracy of 92.2% with a kappa coefficient of 0.84. The comparison with the actual situation shows that the accuracy of the actual recognition of winter wheat is higher and better than the traditional pixel-based classification method. On this basis, the spatial pattern of winter wheat in Shandong was analyzed, and it was found that the topographic undulations had a great influence on the spatial distribution of wheat. This study vividly demonstrates the advantages and possibilities of combining pixel-based and object-oriented approaches through experiments, and also provides a reference for the next related research. Moreover, the winter wheat map of Shandong produced in this research is important for yield assessment, crop planting structure adjustment and the rational use of land resources.

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Study on transfer learning ability for classifying marsh vegetation with multi-sensor images using DeepLabV3+ and HRNet deep learning algorithms

Cited by 17 publications

References 47 publications

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

IoT-based AI Methods for Indoor Air Quality Monitoring Systems: A Systematic Review

A Sub-Seasonal Crop Information Identification Framework for Crop Rotation Mapping in Smallholder Farming Areas with Time Series Sentinel-2 Imagery

Winter Wheat Mapping in Shandong Province of China with Multi-Temporal Sentinel-2 Images

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