lulc-dl-model

🛰️ Land Cover Segmentation — U-Net

A U-Net deep learning model for pixel-wise land cover classification from Sentinel-2 multi-spectral satellite imagery. Trained to identify 5 land cover types across large-scale geospatial datasets.

---

🗺️ Land Cover Classes

The model is trained to classify every pixel into one of the following 5 categories:

---

🏗️ Model Architecture

The model is based on the U-Net architecture — a fully convolutional encoder-decoder network with skip connections designed for semantic image segmentation.

Encoder progressively extracts spatial features through two blocks of convolutions followed by max pooling, reducing the spatial resolution while increasing feature depth (32 → 64 filters).

Bottleneck captures the highest-level abstract features at the compressed representation (128 filters).

Decoder restores the original spatial resolution through two upsampling blocks. At each step, skip connections from the corresponding encoder block are concatenated to recover fine-grained spatial detail (64 → 32 filters).

Output is a 1×1 convolution with softmax activation that produces a per-pixel probability distribution over the 5 land cover classes.

Input (64×64×16)
    │
    ├─── Encoder Block 1 — Conv(32) → Conv(32) → MaxPool ──────────────┐ skip
    ├─── Encoder Block 2 — Conv(64) → Conv(64) → MaxPool ────────┐ skip│
    ├─── Bottleneck      — Conv(128) → Conv(128)                  │     │
    ├─── Decoder Block 1 — UpSample → Concat ←───────────────────┘     │
    │                      Conv(64) → Conv(64)                          │
    ├─── Decoder Block 2 — UpSample → Concat ←─────────────────────────┘
    │                      Conv(32) → Conv(32)
    └─── Output          — Conv(5, 1×1) → Softmax
                           (64×64×5)

---

📥 Input

---

📤 Output

---

📊 Model Performance

Evaluated on 5,746,688 test pixels across all 5 classes.

Overall Metrics

Per-Class Performance

Forest and Water achieve the highest classification accuracy. Barren Land is the most challenging class, likely due to spectral overlap with Built-up Areas and Crop fields.

---

📁 Model File