Implementations Cloud Coverage Prediction using SkyCam Images
System Architecture for the project:
- There are 2 pipelines as shown below, one is for Training both the models i.e. CLIP & Catboost & other is for Inference.
- Detailed explanation of system architecture is provided in Implementations Cloud Coverage Prediction using SkyCam Images
Prerequsite:
- Programming Language: Python
- AI/ML Platform for Model Training: Jupyter Notebook
- Web App: Gradio
- Libraries/Requirements: OpenCv, timm, pytorch, transformers, clip, Catboost
DataSet:
- Data Contains 1,33,653 skycam images with their opaque cloud coverage in percentage.
- During scraping this data, I have used OCR to extract the cloud coverage in percentage.
- Dataset Link : Skycam Images
Cloud Coverage Prediction using Skycam Images
Cloud coverage prediction is critical in weather forecasting and a variety of applications such as solar energy generation, aviation, and climate monitoring. Accurate forecasts help decision-makers and sectors plan for and adapt to changing weather conditions. The advancement of artificial intelligence and computer vision techniques in recent years has created new opportunities for enhancing cloud coverage forecasts.
One promising approach is the use of SkyCam images.
- In the face of rapidly changing global climate patterns, there is an urgent need for innovative tools and technologies to better understand and predict weather-related phenomena.
- One crucial aspect of climate analysis is the assessment of cloud coverage, which plays a pivotal role in influencing weather conditions and climate trends.
- Experts may not always be available to monitor climatic shifts. Therefore, developing an automated weather monitoring system is crucial for various applications, including agriculture and disaster management.
The purpose of this research is to estimate the opaque Cloud Coverage from a Skycam Image using AI/ML methodologies.
Table of Content
- Cloud Coverage Prediction using SkyCam Images
- Implementations Cloud Coverage Prediction using SkyCam Images
- Cloud Coverage Prediction Models:
- Part I. Model Building & Traning Pipeline
- A. Clip Model Finetuning
- B. Catboost Regressor Model Building
- Part II. UI Inference Codes for Deployed Model
- Results:
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