.png&w=256&q=75)
🤝 Top Collaborators
.png&w=640&q=75)
.png&w=640&q=75)
.png&w=640&q=75)
🤓 Latest Submissions
ZameenEye-AI
ZameenEye-AI is a wildfire detection pipeline that bridges computer vision with real-world geospatial infrastructure. At its core is a custom-trained YOLOv8 object detection model, trained end-to-end on AMD Radeon hardware via ROCm across 50 epochs, learning to identify wildfire and smoke hotspots from satellite and aerial imagery, reaching an mAP50 of 0.94 on the validation set. The inference pipeline runs natively on AMD GPU compute, with the device explicitly targeted via PyTorch's ROCm-backed CUDA interface (device='cuda:0'), confirmed through torch.cuda.get_device_name(). A single image passes through the model in roughly 4 milliseconds, fast enough for near real-time monitoring workflows. Detection doesn't stop at a bounding box. Each hotspot's pixel coordinates are passed through a georeferencing transform — linear interpolation across a defined area-of-interest's bounding coordinates — converting pixel-space detections into real-world latitude and longitude. The output is a structured, PostGIS-compatible payload (SRID=4326, WKT POINT geometry) with hazard type and confidence score, ready for ingestion into a spatial database for mapping, alerting, or historical tracking. The stack: YOLOv8 (Ultralytics) for detection, PyTorch with ROCm for AMD GPU acceleration, OpenCV for image I/O, and a Python inference layer producing PostGIS-ready JSON. The long-term architecture is designed to plug into a live satellite tile feed and a multi-tenant backend, turning raw imagery into actionable, geolocated wildfire alerts for wildfire-prone regions, including areas across Pakistan where early-detection infrastructure is limited.
13 Jul 2026
