Create README.md

README.md

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+---
+license: apache-2.0
+tags:
+- wildfire-detection
+- computer-vision
+- cnn
+- remote-sensing
+- image-classification
+- pytorch
+language:
+- en
+---
+
+# PyroSight: Wildfire Detection CNN
+
+**PyroSight** is a convolutional neural network for automated wildfire detection from satellite and aerial imagery. It was developed by Veer Goradia as an independent AI research project.
+
+## Model Description
+
+Wildfires are among the most destructive natural disasters, and early detection is critical for rapid response. PyroSight uses deep learning to classify remote sensing imagery as wildfire or non-wildfire, enabling automated large-scale monitoring.
+
+**Architecture:**
+- Custom CNN backbone trained from scratch for wildfire-specific feature extraction
+- Pretrained baseline comparisons: ResNet50, EfficientNetB0 (frozen + finetuned)
+- Resolution ablation: 150x150 vs 224x224 input
+- Saliency map analysis for failure case interpretability
+- Geographic split analysis to evaluate spatial generalization
+
+**Total accuracy:** 97.62% (deterministic across repeated runs)
+
+## Performance
+
+| Model | Accuracy |
+|-------|----------|
+| PyroSight (custom CNN) | **97.62%** |
+| ResNet50 (finetuned) | baseline |
+| EfficientNetB0 (finetuned) | baseline |
+
+Geographic split analysis: 28% of test images within proximity of training regions — model evaluated on strict geographic splits to assess real-world generalization.
+
+## Training Data
+
+- Satellite and aerial wildfire imagery dataset
+- Binary classification: wildfire vs. non-wildfire
+- Geographic split applied to prevent data leakage between train/test sets
+
+## Intended Use
+
+- Automated wildfire monitoring from satellite imagery feeds
+- Early warning systems for fire detection
+- Research on remote sensing and environmental AI
+
+## How to Use
+
+```python
+import torch
+from tensorflow import keras
+
+# Load model
+model = keras.models.load_model('pyrosight_model')
+
+# Predict on an image (224x224 RGB)
+import numpy as np
+img = np.random.rand(1, 224, 224, 3)  # replace with real satellite image
+prediction = model.predict(img)
+print(f"Wildfire probability: {prediction[0][0]:.3f}")
+```
+
+## Publications
+
+- Submitted to **IEEE Big Data High School Symposium 2026** (Paper ID: SP19206)
+- Accepted to **Harvard HSRC 2026**
+
+## Citation
+
+```
+@misc{goradia2026pyrosight,
+  title={PyroSight: Deep Learning for Automated Wildfire Detection from Remote Sensing Imagery},
+  author={Goradia, Veer},
+  year={2026}
+}
+```
+
+## License
+
+Apache 2.0 — see LICENSE file.
+
+## Contact
+
+Veer Goradia · vgoradia07@gmail.com