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:
Class
Description
Barren Land
Exposed soil, rock, and sparsely vegetated areas
Built-up Area
Urban and suburban structures, roads
Crop
Agricultural and cultivated farmland
Forest
Dense tree cover and woodland areas
Water
Rivers, lakes, reservoirs, and water bodies
🏗️ 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.
Per-pixel class label (one of 5 land cover classes)
Format
Integer class map derived from softmax probabilities
📊 Model Performance
Evaluated on 5,746,688 test pixels across all 5 classes.
Overall Metrics
Metric
Score
Overall Accuracy
93.04%
Validation Accuracy
93.04%
Validation Loss
0.2678
Macro Avg Precision
92.58%
Macro Avg Recall
92.88%
Macro Avg F1-Score
92.69%
Weighted Avg F1
93.06%
Per-Class Performance
Class
Precision
Recall
F1-Score
Barren Land
84.30%
90.59%
87.33%
Built-up Area
93.32%
89.29%
91.26%
Crop
93.53%
95.42%
94.47%
Forest
95.55%
96.84%
96.19%
Water
96.23%
92.27%
94.21%
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.
