Building a Satellite Image Classification System from Scratch: A Complete Practical Analysis of EuroSAT-CNN

This article analyzes the open-source project EuroSAT-CNN, which builds a satellite image classification system from scratch using PyTorch without pre-trained weights, achieving a test accuracy of 92.2% on the EuroSAT dataset. It covers CNN architecture design, data preprocessing, interpretable AI (GradCAM), and a complete CI/CD production deployment process, providing an end-to-end reference for the integration of remote sensing and deep learning.

Satellite image classification is an important application of the integration of remote sensing and deep learning. The EuroSAT dataset contains 27,000 64×64 images from the Sentinel-2 satellite, covering 10 land use categories (e.g., forest, residential area, river, etc.). The unique feature of this project is that it is trained completely from scratch without pre-trained weights, making it an excellent case for understanding the working principles of CNNs.

The model uses a custom CNN architecture, including 3 convolutional blocks (primary → intermediate → advanced feature extraction, with filter count doubling) and a classifier; key designs include batch normalization, hierarchical regularization (Dropout2d + Dropout), adaptive pooling, etc. Data processing uses a stratified split (70/15/15), and only the training set is used to calculate statistics to prevent data leakage.

The test accuracy is 92.2%. Performance of some categories: Forest (F1=0.928), SeaLake (F1=0.981), etc.; HerbaceousVegetation and AnnualCrop are challenging (F1 scores of 0.837 and 0.892 respectively). GradCAM visualization is implemented to generate heatmaps explaining model decisions, verifying that the model focuses on correct geographic features.

The deployment architecture includes FastAPI REST API, dark-themed Web UI, and Docker containerization; CI/CD pipeline (code quality check, automated testing, container push). Technical highlights: Using random weights instead of the real model in testing to ensure independence, and configuration-driven design (centralized parameter management via config.py).

The project provides an end-to-end solution for remote sensing image analysis, which can be applied to scenarios such as agricultural monitoring, urban planning, and environmental protection. It proves that excellent performance can be achieved without pre-trained weights, with clear code structure and complete documentation, making it an excellent reference for learning CNNs and production deployment.