metadata
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
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