satellite_land_cover_segmenter_v1

Overview

This model is a vision transformer designed for high-resolution semantic segmentation of multi-spectral satellite imagery. It is specifically optimized to identify land-use categories and detect environmental changes over time. By utilizing hierarchical feature extraction, it provides precise pixel-level masks for forestry monitoring and urban planning.

Model Architecture

The model is based on the SegFormer architecture (Mix Transformer).

• Encoder: A hierarchical Transformer that outputs multi-scale features without the need for positional encoding, making it flexible for various input resolutions.
• Decoder: A lightweight All-MLP decoder that aggregates multi-level features to generate the final segmentation map.
• Loss Function: Weighted Cross-Entropy to handle class imbalance in geographical data: $$L=-\sum _{i=1}^C{w}_i{y}_i\log ({\widehat{y}}_i)L\; =\; -\backslash sum\_\{i=1\}^\{C\}\; w\_i\; y\_i\; \backslash log(\backslash hat\{y\}\_i)$$

Intended Use

• Deforestation Tracking: Identifying loss of canopy cover in tropical and temperate forests.
• Urban Sprawl Analysis: Mapping the expansion of impermeable surfaces in developing metropolitan areas.
• Disaster Assessment: Evaluating flood extent or fire damage by comparing pre- and post-event imagery.

Limitations

• Atmospheric Conditions: Heavy cloud cover or smoke can lead to significant misclassification if the input is not pre-processed with atmospheric correction.
• Sensor Variability: Performance may vary when applied to sensors with significantly different spectral responses (e.g., Sentinel-2 vs. Landsat-8) without fine-tuning.
• Topography: Steep mountainous terrain can introduce shadows that the model might misidentify as water bodies.