# LumoraQ: Exploration of Quantum Machine Learning in Satellite Image Analysis

> A land cover classification project based on the EuroSAT dataset. First, a classical CNN baseline (accuracy: 88.83%) is established to provide a comparative benchmark for the subsequent introduction of quantum-classical hybrid models (VQC).

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-04T16:45:58.000Z
- 最近活动: 2026-06-04T16:52:55.431Z
- 热度: 139.9
- 关键词: quantum machine learning, satellite imagery, CNN, EuroSAT, remote sensing, PennyLane, VQC
- 页面链接: https://www.zingnex.cn/en/forum/thread/lumoraq
- Canonical: https://www.zingnex.cn/forum/thread/lumoraq
- Markdown 来源: floors_fallback

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## LumoraQ Project Guide: Exploration of Quantum Machine Learning in Satellite Image Analysis

# LumoraQ: Exploration of Quantum Machine Learning in Satellite Image Analysis

## Core Information
- **Original Author/Maintainer**: BlackCherry
- **Source Platform**: GitHub
- **Release Time**: June 4, 2026

## Project Objectives
Explore the application of quantum-classical hybrid machine learning methods on Earth observation data, using the EuroSAT satellite image dataset to verify the potential of quantum computing in remote sensing classification tasks. The current phase focuses on building a classical CNN baseline to provide a comparative benchmark for subsequent quantum models

## Background: Introduction to the EuroSAT Dataset

## EuroSAT Dataset
A widely used benchmark dataset in the remote sensing field:
- 27,000 satellite images
- 10 land cover categories: AnnualCrop, Forest, HerbaceousVegetation, Highway, Industrial, Pasture, PermanentCrop, Residential, River, SeaLake
- All images are uniformly resized to 64×64 pixels and normalized

## Methodology: Construction of Classical CNN Baseline

## Initial CNN Architecture
- Convolutional Layer 1: 3→16 filters (ReLU + Max Pooling)
- Convolutional Layer 2:16→32 filters (ReLU + Max Pooling)
- Fully Connected Layer1:8192→128
- Fully Connected Layer2:128→10
- Loss Function: Cross-entropy; Optimizer: Adam
Result: Final accuracy of 80.28%

## StrongCNN Upgrade
Improvements: Batch normalization, deeper convolutional architecture, Dropout regularization
Data Augmentation: Random horizontal flip, rotation, color jitter
Result: Accuracy increased to 88.83%

## Evidence: Training Results and Insights

## Training Observations
- Loss steadily decreased from 1.26 to 0.0791 with smooth convergence
- No signs of instability or divergence
- CNN effectively learns spatial patterns in satellite images

## Benchmark Significance
The 88.83% accuracy of StrongCNN sets a performance threshold for quantum models

## Future Plan: Quantum-Classical Hybrid Model

## Next Phase Roadmap
- Install the PennyLane quantum framework
- Implement Variational Quantum Classifier (VQC)
- Compare with the StrongCNN baseline

## Design Idea
A progressive approach ensures the classical component is optimized before introducing quantum components, enabling fair comparison

## Conclusion: Technical Significance of the Project

## Core Value
- Provide a reproducible benchmark for quantum machine learning in the remote sensing field
- Demonstrate a progressive research path from classical baseline to quantum enhancement
- Offer data support for evaluating quantum advantage

## Practical Reference
Provide a clear template for researchers working on the practical application of quantum machine learning