Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

Lee, Saro

doi:10.3390/app8010055

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…We implemented our models in Python 3.10.8 and Pytorch 1.13.1 using the publicly available code repository referenced in [35], while our code and list of additional computed input features are publicly available in the Github repository. 1 The strategy for loading the data into memory was changed when we preprocessed our data by unifying the number of timestamps. Since we pre-trained the model on the BreizhCrops dataset, we used the same architectural hyperparameters referenced in [35].…”

Section: Resultsmentioning

confidence: 99%

“…This is largely due to the rapid development of spatial information acquisition techniques (remote sensing, global navigation satellite systems, etc.) and the integration of mathematical algorithms into spatial mapping and analysis software [1]. One of the areas of geoscience that has successfully harnessed these innovations is the field of agriculture and crop production, which has led to the development of new disciplines such as digital agriculture (smart agriculture) and precision agriculture [2,3].…”

Section: Introductionmentioning

confidence: 99%

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Transfer learning approach based on satellite image time series for the crop classification problem

et al. 2023

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This paper presents a transfer learning approach to the crop classification problem based on time series of images from the Sentinel-2 dataset labeled for two regions: Brittany (France) and Vojvodina (Serbia). During preprocessing, cloudy images are removed from the input data, the time series are interpolated over the time dimension, and additional remote sensing indices are calculated. We chose TransformerEncoder as the base model for knowledge transfer from source to target domain with French and Serbian data, respectively. Even more, the accuracy of the base model with the preprocessing step is improved by 2% when trained and evaluated on the French dataset. The transfer learning approach with fine-tuning of the pre-trained weights on the French dataset outperformed all other methods in terms of overall accuracy 0.94 and mean class recall 0.907 on the Serbian dataset. Our partially fine-tuned model improved recall of crop types that were poorly classified by the base model. In the case of sugar beet, class recall is improved by 85.71%.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Transfer learning approach based on satellite image time series for the crop classification problem

et al. 2023

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“…Moreover, RS and GIS technologies have been more essential for sustainable applications to the ecological, geological, physical, hydrological, and environmental research fields. The advances of RS and GIS technologies can also be found in other Special Issues [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

2020

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The Special Issue on "Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations" is published. A total of 20 qualified papers are published in this Special Issue. The topics of the papers are the application of remote sensing and geospatial information systems to Earth observations in various fields such as (1) object change detection, (2) air pollution, (3) earthquakes, (4) landslides, (5) mining, (6) biomass, (7) groundwater, and (8) urban development using the techniques of remote sensing and geospatial information systems. More than 100 researchers have participated in this Special Issue. We hope that this Special Issue is helpful for sustainable applications.

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Analysis of Variable Learning Rate Back Propagation with Cuckoo Search Algorithm for Data Classification

Ali¹,

Khan²,

Khan³

et al. 2021

International Conference on Emerging Applications and Technologies for Industry 4.0 (EATI’2020)

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Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

Abstract: Recently, a need has arisen for prediction techniques that can address a variety of problems by combining methods from the rapidly developing field of machine learning with geoinformation technologies such as GIS, remote sensing, and GPS.[…]

Cited by 5 publications

References 14 publications

Transfer learning approach based on satellite image time series for the crop classification problem

Transfer learning approach based on satellite image time series for the crop classification problem

Sustainable Applications of Remote Sensing and Geospatial Information Systems to Earth Observations

Analysis of Variable Learning Rate Back Propagation with Cuckoo Search Algorithm for Data Classification

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