AI-enhanced satellite view of Earth, highlighting cloud detection

Unlock Earth's Secrets: How AI Cloud Detection is Revolutionizing Satellite Imagery

Beau Callahan in Climate & Environment August 2025 • 4 min read.

"From Landsat-8 to Proba-V: Transfer learning breakthroughs in satellite cloud screening for a clearer view of our planet"

Imagine trying to understand what's happening on Earth from space, but your view is constantly obscured by clouds. This is the challenge faced by scientists and researchers who rely on satellite images to monitor our planet. Cloud cover makes it difficult to gather accurate data, hindering efforts to track deforestation, monitor urban growth, and study climate change.

Traditional methods of cloud detection often struggle, especially with satellites that have fewer spectral bands—the specific ranges of light they can 'see.' Proba-V, a European Space Agency satellite, faces this challenge. Its limited spectral information makes it tough to distinguish clouds from other features on the ground, leading to less reliable data.

But what if we could teach computers to 'see' clouds more accurately, even with limited information? That's where artificial intelligence (AI) comes in. Recent research explores how convolutional neural networks (CNNs), a type of AI, can be trained to identify clouds using data from other satellites, like Landsat-8, and then apply that knowledge to Proba-V images.

AI Cloud Detection: Smarter Satellite Imagery

AI-enhanced satellite view of Earth, highlighting cloud detection

Convolutional neural networks (CNNs) have revolutionized image recognition, and their application to cloud detection is no exception. CNNs excel at identifying patterns and spatial relationships within images, making them ideal for distinguishing clouds from land, water, and other atmospheric phenomena. Unlike traditional threshold-based methods, which often struggle with complex or varying conditions, CNNs can learn to adapt and generalize, leading to more accurate results.

The challenge, however, lies in the need for large amounts of labeled data to train these AI models. Gathering this data—identifying and labeling clouds in countless satellite images—is a time-consuming and labor-intensive process. This is where transfer learning steps in to solve this problem by leveraging existing data sets and reapplying it to new problems.

Landsat-8 to the Rescue: Researchers use the Landsat-8 satellite, which has more detailed spectral information and a comprehensive cloud mask dataset, to train CNNs.
Adapting the View: They then adapt the Landsat-8 data to mimic the characteristics of Proba-V images, bridging the gap between the two datasets.
Transfer Learning Magic: The CNN, trained on Landsat-8 data, is then applied to Proba-V images, effectively transferring its knowledge to the new task.

The results are impressive. Studies show that CNN models trained using this transfer learning approach outperform existing operational cloud detection methods for Proba-V. In other words, the AI can identify clouds more accurately than traditional algorithms, even without being directly trained on a large Proba-V dataset. It is able to be more accurate due to previous learning.

A Clearer Future for Earth Observation

AI-powered cloud detection is paving the way for more accurate and reliable Earth observation. By leveraging transfer learning techniques, researchers are overcoming the limitations of traditional methods and unlocking the full potential of satellite imagery. This has implications for climate monitoring, environmental protection, and a deeper understanding of our changing planet. As AI technology continues to advance, we can expect even more sophisticated and effective solutions for cloud detection, providing us with a clearer view of Earth's secrets.

About this Article -

This article was crafted using a human-AI hybrid and collaborative approach. AI assisted our team with initial drafting, research insights, identifying key questions, and image generation. Our human editors guided topic selection, defined the angle, structured the content, ensured factual accuracy and relevance, refined the tone, and conducted thorough editing to deliver helpful, high-quality information.See our About page for more information.

This article is based on research published under:

DOI-LINK: 10.1109/igarss.2018.8517975, Alternate LINK

Title: Convolutional Neural Networks For Cloud Screening: Transfer Learning From Landsat-8 To Proba-V

Journal: IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium

Publisher: IEEE

Authors: Gonxalo Mateo-Garcia, Luis Gomez-Chova

Published: 2018-07-01

Everything You Need To Know

How do Convolutional Neural Networks (CNNs) improve cloud detection in satellite imagery compared to traditional methods, and what challenges do they address?

Convolutional Neural Networks (CNNs) enhance cloud detection by identifying spatial patterns in satellite images, distinguishing clouds from land, water, and other atmospheric phenomena. Unlike traditional methods, CNNs adapt to varying conditions, improving accuracy. The challenge is the need for extensive labeled data, which transfer learning addresses.

Can you explain how transfer learning works in the context of satellite cloud detection, specifically using Landsat-8 and Proba-V?

Transfer learning uses data from satellites like Landsat-8, which has detailed spectral information, to train CNNs. This knowledge is then applied to satellites with less data, like Proba-V. By adapting Landsat-8 data to mimic Proba-V characteristics, the CNN effectively transfers its learning, improving cloud detection accuracy for Proba-V.

In what ways does AI-powered cloud detection with Convolutional Neural Networks (CNNs) enhance climate monitoring efforts?

AI-powered cloud detection, specifically utilizing Convolutional Neural Networks (CNNs) and transfer learning, enhances climate monitoring by providing more accurate and reliable satellite imagery. This allows for improved tracking of deforestation, urban growth, and other environmental changes, leading to a more comprehensive understanding of our planet's climate dynamics.

Why do traditional cloud detection methods struggle with satellites like Proba-V, and how do Convolutional Neural Networks (CNNs) offer a solution?

Traditional cloud detection methods struggle with satellites like Proba-V due to their limited spectral bands, making it hard to differentiate clouds from ground features. Convolutional Neural Networks (CNNs) offer a solution by learning from more detailed satellite data, such as that from Landsat-8, and then applying this knowledge to enhance the accuracy of cloud detection in Proba-V images.

What are the broader implications of using Convolutional Neural Networks (CNNs) and transfer learning for cloud detection beyond just improving image clarity?

The implications of using Convolutional Neural Networks (CNNs) and transfer learning for cloud detection extend to various fields. More accurate satellite imagery supports better environmental monitoring, disaster response, and agricultural management. Furthermore, advances in AI cloud detection could lead to more precise weather forecasting and climate modeling, improving our ability to understand and address global environmental challenges.