A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

Dastour, Hatef; Hassan, Quazi K.

doi:10.3390/su15107854

Cited by 11 publications

(3 citation statements)

References 61 publications

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“…The evaluation of the model's performance encompassed the application of several key accuracy metrics, namely precision, recall, F1-score, and overall accuracy (Powers 2011, Dastour et al 2022, Dastour and Hassan 2023. These metrics are expounded upon below:…”

Section: Accuracy Metricsmentioning

confidence: 99%

Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada

Dastour,

Hassan

2024

Environ. Res. Commun.

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Accurate prediction of fire spread is considered crucial for facilitating effective fire management, enabling proactive planning, and efficient allocation of resources. This study places its focus on wildfires in two regions of Alberta, Fort McMurray and Slave Lake, in Southwest Canada. For the simulation of wildfire spread, an adapted fire propagation model was employed, incorporating MODIS datasets such as land surface temperature, land cover, land use, and integrated climate data. The pixels were classified as burned or unburned in relation to the 2011 Slave Lake wildfire and the initial 16 days of the 2016 Fort McMurray wildfire, utilizing defined starting points and the aforementioned specified datasets. The simulation for the 2011 Slave Lake wildfire achieved an weighted average precision, recall, and f1-scores of 0.989, 0.986, and 0.987, respectively. Additionally, macro-averaged scores across these three phases were 0.735, 0.829, and 0.774 for precision, recall, and F1-scores, respectively. The simulation of the 2016 Fort McMurray wildfire introduced a phased analysis, dividing the initial 16 days into three distinct periods. This approach led to average precision, recall, and f1-scores of 0.958, 0.933, and 0.942 across these phases. Additionally, macro-averaged scores across these three phases were 0.681, 0.772, and 0.710 for precision, recall, and F1-scores, respectively. The strategy of segmenting simulations into phases may enhance adaptability to dynamic factors like weather conditions and firefighting strategies.

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Section: Accuracy Metricsmentioning

confidence: 99%

Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada

Dastour,

Hassan

2024

Environ. Res. Commun.

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“…Most commonly, the task of land use/land cover classification is improved by starting from pretrained models. Dastour and Hassan [19] give an in-depth analysis of different deep learning architectures for this task. In general, all CNNs tested are pretrained on the wellknown ImageNet dataset [20] and applied to land cover classification on Sentinel-2A images.…”

Section: Transfer Learning For Remote Sensing Applicationsmentioning

confidence: 99%

Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning

Huber,

Inderka,

Steinhage

2024

Sensors

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Remote sensing data represent one of the most important sources for automized yield prediction. High temporal and spatial resolution, historical record availability, reliability, and low cost are key factors in predicting yields around the world. Yield prediction as a machine learning task is challenging, as reliable ground truth data are difficult to obtain, especially since new data points can only be acquired once a year during harvest. Factors that influence annual yields are plentiful, and data acquisition can be expensive, as crop-related data often need to be captured by experts or specialized sensors. A solution to both problems can be provided by deep transfer learning based on remote sensing data. Satellite images are free of charge, and transfer learning allows recognition of yield-related patterns within countries where data are plentiful and transfers the knowledge to other domains, thus limiting the number of ground truth observations needed. Within this study, we examine the use of transfer learning for yield prediction, where the data preprocessing towards histograms is unique. We present a deep transfer learning framework for yield prediction and demonstrate its successful application to transfer knowledge gained from US soybean yield prediction to soybean yield prediction within Argentina. We perform a temporal alignment of the two domains and improve transfer learning by applying several transfer learning techniques, such as L2-SP, BSS, and layer freezing, to overcome catastrophic forgetting and negative transfer problems. Lastly, we exploit spatio-temporal patterns within the data by applying a Gaussian process. We are able to improve the performance of soybean yield prediction in Argentina by a total of 19% in terms of RMSE and 39% in terms of R2 compared to predictions without transfer learning and Gaussian processes. This proof of concept for advanced transfer learning techniques for yield prediction and remote sensing data in the form of histograms can enable successful yield prediction, especially in emerging and developing countries, where reliable data are usually limited.

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“…One promising approach is to leverage unsupervised or semi-supervised learning techniques, which can make use of unlabeled data to improve model performance [158][159][160]. Transfer learning, where a model trained on a large dataset is fine-tuned on a smaller, task-specific dataset, could also be a promising approach for dealing with the scarcity of labeled data [161][162][163].…”

Section: Future Workmentioning

confidence: 99%

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

Choi

Lee

2023

Biology

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The emergence and rapid development of deep learning, specifically transformer-based architectures and attention mechanisms, have had transformative implications across several domains, including bioinformatics and genome data analysis. The analogous nature of genome sequences to language texts has enabled the application of techniques that have exhibited success in fields ranging from natural language processing to genomic data. This review provides a comprehensive analysis of the most recent advancements in the application of transformer architectures and attention mechanisms to genome and transcriptome data. The focus of this review is on the critical evaluation of these techniques, discussing their advantages and limitations in the context of genome data analysis. With the swift pace of development in deep learning methodologies, it becomes vital to continually assess and reflect on the current standing and future direction of the research. Therefore, this review aims to serve as a timely resource for both seasoned researchers and newcomers, offering a panoramic view of the recent advancements and elucidating the state-of-the-art applications in the field. Furthermore, this review paper serves to highlight potential areas of future investigation by critically evaluating studies from 2019 to 2023, thereby acting as a stepping-stone for further research endeavors.

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A Comparison of Deep Transfer Learning Methods for Land Use and Land Cover Classification

Cited by 11 publications

References 61 publications

Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada

Utilizing MODIS remote sensing and integrated data for forest fire spread modeling in the southwest region of Canada

Leveraging Remote Sensing Data for Yield Prediction with Deep Transfer Learning

Transformer Architecture and Attention Mechanisms in Genome Data Analysis: A Comprehensive Review

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