---
license: mit
tags:
- remote-sensing
- satellite-imagery
- multi-label-classification
- pytorch
- efficientnetv2
- mobilenetv2
datasets:
- planet-understanding-the-amazon-from-space
metrics:
- fbeta_score
---

# 🛰️ Planet Amazon Satellite Scene Multi-Label Classifier

Multi-label classification of remote sensing satellite images over the Amazon rainforest using **MobileNetV2** (Baseline) and **EfficientNetV2-S** (Proposed Novel Architecture).

## 📊 Performance Benchmarks
- **Dataset:** Planet Amazon (40,479 images, 17 classes)
- **Macro Recall:** 98.72% (EfficientNetV2-S)
- **Official Kaggle Public Score:** 0.60475
- **Official Kaggle Private Score:** 0.59929 (Zero Overfitting, Δ = 0.0054)

## 🏗️ Architecture Innovations
- Backbone: `EfficientNetV2-S` with Fused-MBConv layers
- Loss: Label Smoothing Binary Cross-Entropy (ε = 0.05)
- Schedule: Cosine Annealing Learning Rate
- Activation: GELU non-linearities in 2-stage classification head