Halit Çetiner, Sedat Metlek

Classification of Weather Phenomenon with a New Deep Learning Method Based on Transfer Learning

Introduction

Classification of weather phenomenon with a new deep learning method based on transfer learning. New deep learning model with transfer learning automatically classifies 11 weather phenomena from aerial images with 88% accuracy, crucial for planning & industries.

Abstract

Recognition of weather conditions, which have an important effect on the planning of our daily lives, affects many events from transport to agriculture. Even on an ordinary day, the weather affects many events, from taking children to the market to taking a walk. In addition, in many commercial areas such as agriculture and animal husbandry, many issues from planting and planting time to production are directly or indirectly related to weather conditions. For these reasons, automatic analyses and classification of aerial images will provide significant convenience. New technologies based on deep learning are needed to minimize the errors of experts working in the towers established to monitor weather conditions. Deep learning based systems are preferred because they bring a new perspective to feature extraction and classification approaches in classical machine learning technologies. With deep learning based systems, it is possible to classify by obtaining distinctive features from different weather conditions. In this paper, a pre-trained architecture-based deep learning model is proposed to classify a dataset containing 6877 images of 11 weather conditions. In order to measure the effect of the proposed model on the performance, a comparison with the basic model is performed. The weather classification accuracy of the proposed model in the test set is 88%. This performance result shows that the model is competitive with its competitors. At this point, eleven different weather images can be automatically classified. As a result of the mentioned procedures, this study can be a reference for future weather classification studies.

Review

This paper addresses the critical problem of automatic weather phenomenon classification from aerial images, a task with significant implications across various daily activities and commercial sectors like agriculture and transportation. The authors propose a deep learning model leveraging transfer learning from a pre-trained architecture to classify 11 distinct weather conditions. The motivation is well-articulated, highlighting the limitations of manual expert analysis and the advantages of deep learning in feature extraction and classification over traditional machine learning methods. The study aims to provide a more efficient and accurate alternative for monitoring weather conditions, thereby contributing to the broader field of environmental informatics and intelligent systems. The methodology involves applying a pre-trained deep learning model to a dataset comprising 6877 images across the 11 weather categories. The core contribution is the proposed architecture, which is compared against a "basic model" to evaluate its performance impact. The reported classification accuracy of 88% on the test set is presented as a competitive result, suggesting the model's efficacy in automatically distinguishing between different weather phenomena. This quantitative outcome is the primary evidence for the model's potential utility and positions the study as a reference point for future research in this domain. While the abstract provides a clear overview of the problem, methodology, and outcome, several crucial details are absent that would strengthen an expert review. Specifically, the abstract lacks information regarding the *type* of pre-trained architecture used (e.g., ResNet, VGG, Inception) and the "basic model" against which the proposed model is compared. Understanding the specific transfer learning strategy (e.g., fine-tuning, feature extraction) and the characteristics of the dataset beyond its size and class count would also be beneficial. Furthermore, "competitive with its competitors" is a vague statement without mentioning specific benchmarks or state-of-the-art results from related works. A more comprehensive discussion on potential limitations, generalizability, or error analysis for misclassified weather conditions would enhance the paper's academic rigor and provide more actionable insights for follow-up studies.

Full Text

Comments

You need to be logged in to post a comment.