---
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.