Create README.md

README.md

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+---
+language: en
+license: mit
+tags:
+- environment
+- computer-vision
+- vit
+- climate-change
+---
+
+# wildfire_smoke_segmentation_vit
+
+## Overview
+This model is a Vision Transformer (ViT) designed for the early detection of wildfires via satellite and aerial imagery. By identifying specific smoke patterns and thermal anomalies, it provides real-time alerts for environmental monitoring agencies.
+
+
+
+## Model Architecture
+The model is based on the **ViT-Base** (Patch 16) architecture:
+- **Patching**: Divides input images into 16x16 patches to capture global spatial dependencies.
+- **Attention**: Uses multi-head self-attention to distinguish between cloud cover and low-density smoke plumes.
+- **Pre-training**: Initialized on ImageNet-21k and fine-tuned on the FIRESAT dataset.
+
+## Intended Use
+- **Remote Sensing**: Automated monitoring of vast forested areas via Sentinel-2 or Landsat imagery.
+- **Early Warning Systems**: Integration into IoT-enabled lookout towers for local fire departments.
+- **Post-Fire Analysis**: Assessing the spread and intensity of smoke for environmental impact studies.
+
+## Limitations
+- **Atmospheric Conditions**: Heavy cloud cover or fog can lead to false positives.
+- **Resolution**: Accuracy drops significantly for images where the smoke plume is smaller than 32x32 pixels.
+- **Time of Day**: Optimized for daytime multi-spectral imagery; night-time performance relies on thermal band availability.