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--- |
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language: en |
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license: apache-2.0 |
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tags: |
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- vision |
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- segmentation |
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- satellite-imagery |
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- ecology |
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- climate-change |
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--- |
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# satellite_deforestation_segmenter |
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## Overview |
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This model is designed for high-resolution semantic segmentation of satellite imagery (RGB) to detect changes in forest cover. It categorizes pixels into six classes, prioritizing the identification of `deforested_area` and `sparse_vegetation` to assist in real-time ecological monitoring and conservation efforts. |
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## Model Architecture |
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The model utilizes the **SegFormer** architecture, which combines a hierarchical Transformer encoder with a lightweight All-MLP decoder. |
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- **Encoder:** Hierarchical Transformer that outputs multi-scale features. Unlike traditional ViT, it does not require positional encodings, making it robust to varying input resolutions. |
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- **Decoder:** A simple MLP-based head that aggregates features from different layers to produce the final segmentation mask. |
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## Intended Use |
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- **Environmental Monitoring:** Automated detection of illegal logging activities. |
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- **Carbon Credit Verification:** Estimating biomass loss in specific geographical sectors. |
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- **Urban Planning:** Tracking the encroachment of urban infrastructure into protected green zones. |
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## Limitations |
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- **Cloud Cover:** Performance significantly degrades in images with high cloud density or heavy atmospheric haze. |
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- **Topography:** Steep terrain shadows may be misclassified as water bodies or dense forest. |
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- **Sensor Variance:** Optimized for Sentinel-2 and Landsat-8 data; performance on commercial high-res imagery (e.g., Planet) may require further fine-tuning. |
