droneimaginery

Semantic segmentation of aerial imagery into 5 land-cover classes - 0.72 mIoU on LandCover.ai with a pretrained EfficientNet-B0 U-Net and Gradio demo.

Semantic segmentation of aerial drone imagery using DeepLabV3+, PSPNet and ConvNeXt-Tiny.

The proposed system is a hybrid deep-learning architecture for landslide detection and segmentation from satellite, aerial, and drone imagery

Comparing YOLOv8n and YOLOv8s for object detection on VisDrone aerial drone imagery. YOLOv8s reaches 0.420 mAP@50 vs 0.329 for YOLOv8n.

Multi-task deep learning pipeline for Ground Control Point localization and shape classification in aerial drone imagery.

Drone-assisted pest detection and plant health monitoring using CNNs, OpenCV, and TensorFlow.