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language: en
license: cc-by-4.0
tags:
- vision
- image-classification
- cnn
- satellite-imagery
- land-use-classification
- france
- remote-sensing
model-index:
- name: satellite-land-use-classifier-france
results:
- task:
type: image-classification
dataset:
name: hadrilec/satellite-pictures-classification-ign-france
type: image-dataset
metrics:
- name: accuracy
type: classification
value: 0.9728
split: validation
widget:
- task: image-classification
inputs:
- name: example_input
type: image
url: >-
https://huggingface.co/datasets/hadrilec/satellite-pictures-classification-ign-france/viewer/default/train?views%5B%5D=train&image-viewer=9473D066F7CE8EC28648894FD2FDAAF273ED2520
model:
architecture: Custom CNN (2-layer Conv + 2-FC)
framework: PyTorch
input_shape:
- 3
- 256
- 256
output_labels:
- forest
- sea
- urban
- field
model_description: >
This model uses a custom convolutional neural network, trained on satellite
images provided by IGN, to classify areas in France into four categories:
forest, sea, urban, and field
citation:
- authors:
- Hadrien Leclerc
title: Satellite Image Classifier for Land-Use in France
year: 2025
datasets:
- hadrilec/satellite-pictures-classification-ign-france
metrics:
- accuracy
---
# Satellite Image Classifier (Custom CNN)
This repository contains a **custom convolutional neural network** trained on satellite imagery for land classification in France. The model is inspired by the foundational book *Deep Learning with Pytorch, Eli Stevens, Luca Antiga, Thomas Viehmann*
**Dataset:** [hadrilec/satellite-pictures-classification-ign-france](https://huggingface.co/datasets/hadrilec/satellite-pictures-classification-ign-france)
---
## 🏗 Model Architecture
```python
class Net(nn.Module):
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1)
self.conv2 = nn.Conv2d(16, 8, kernel_size=3, padding=1)
self.fc1 = nn.Linear(8 * 64 * 64, 32)
self.fc2 = nn.Linear(32, 4)
def forward(self, x):
out = F.max_pool2d(torch.tanh(self.conv1(x)), 2)
out = F.max_pool2d(torch.tanh(self.conv2(out)), 2)
out = out.view(-1, 8 * 64 * 64)
out = torch.tanh(self.fc1(out))
out = self.fc2(out)
return out
```
## Example Notebook
We provide an example notebook that demonstrates how to train the model:
[Open the example notebook](./example.ipynb)
This notebook is intended as a starting point for experimentation and helps you quickly see how to use the dataset in practice.
Accuracy is 0.9728
## IT infrastructure
The model has been trained on [Onyxia datalab platform](https://www.onyxia.sh/), with an NVIDIA GPU Tesla T4.
## 📈 Training & Validation Loss
Below is the training vs. validation loss curve:
---
## 🖼 Misclassified Samples
Here is a facet plot showing some **misclassified images** from the validation set:
---
## 🖼 Correctly Classified Samples
Here is a facet plot showing some **well-classified images** from the validation set:
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