Create README.md

README.md

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