Shoriful025/satellite_deforestation_segmenter

1---2language: en3license: apache-2.04tags:5- vision6- segmentation7- satellite-imagery8- ecology9- climate-change10---11 12# satellite_deforestation_segmenter13 14## Overview15This 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.16 17## Model Architecture18The model utilizes the **SegFormer** architecture, which combines a hierarchical Transformer encoder with a lightweight All-MLP decoder.19 20 21 22- **Encoder:** Hierarchical Transformer that outputs multi-scale features. Unlike traditional ViT, it does not require positional encodings, making it robust to varying input resolutions.23- **Decoder:** A simple MLP-based head that aggregates features from different layers to produce the final segmentation mask.24 25## Intended Use26- **Environmental Monitoring:** Automated detection of illegal logging activities.27- **Carbon Credit Verification:** Estimating biomass loss in specific geographical sectors.28- **Urban Planning:** Tracking the encroachment of urban infrastructure into protected green zones.29 30## Limitations31- **Cloud Cover:** Performance significantly degrades in images with high cloud density or heavy atmospheric haze.32- **Topography:** Steep terrain shadows may be misclassified as water bodies or dense forest.33- **Sensor Variance:** Optimized for Sentinel-2 and Landsat-8 data; performance on commercial high-res imagery (e.g., Planet) may require further fine-tuning.