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.
