A QGIS plugin for tree monitoring using AI.
MegaDetector-Overhead — The Microsoft open-source AI for overhead wildlife detection. Point-based detection model for aerial and drone imagery, identifying wildlife from above. Maintained by Microsoft AI for Good Lab. Part of the Pytorch-Wildlife ecosystem.
GreenShield Mobile App
This repository provides a tutorial and code for reproducing the data and results presented in the following publication: Automated Mapping of Post-Storm Roof Damage Using Deep Learning and Aerial Imagery: A Case Study in the Caribbean (Kucharczyk, Nesbit, & Hugenholtz, 2025, Remote Sensing).
Semantic segmentation of drone imagery using U-Net deep learning architecture | TensorFlow/Keras implementation with automated preprocessing and multi-class pixel classification | Dron görüntülerinin U-Net ile semantik segmentasyonu
Final Year Project developed for my BSc (Hons) in Computing.
Counting sunflower heads in drone images with YOLOv7 (about 93% accuracy) to estimate crop yield, with a dashboard for exploring the results. Final year project.
An automated vineyard detection system using the Segment Anything Model.
Companion code for the KST 2025 paper comparing deep learning detectors for cabbage detection and counting in drone imagery. Trains and scores 14 models (Faster R-CNN, SSD, RetinaNet, FCOS, YOLOv8/11/12/26, RT-DETR) on a public UAV dataset, with verified run evidence and released checkpoints.
DNN-derived synthetic vegetation index for agronomic trait prediction from multispectral drone imagery
AI-powered traffic analysis system for detecting and tracking road users from aerial drone footage.
Young corn segmentation, row analysis, and candidate planting gap detection on drone imagery
Deep learning pipeline (YOLOv5 + Detectron2) for detecting maize tassels in UAV imagery — CSE499 senior design project.
A modern desktop app for calculating areas from UAV-captured GeoTIFFs and shapefiles. Features support for alpha masking, automatic UTM projection, clean map overlays, and precise dimension reporting.
Genomic and phenomic prediction of agronomic traits and malt quality in NY Winter Malting Barley
Tiered edge AI land cover classification across ESP32, STM32, and Jetson Nano — the same task, scoped and quantized differently for each board's memory budget.
End-to-end drone remote sensing platform: schema-agnostic data pipeline, GeoLibre-based GIS mapping with IDW heat-surface interpolation, OpenCV computer vision for land-cover classification, and AI-assisted automated reporting — built with Streamlit.
Drone-based livestock detection system using YOLOv8 for identifying and counting sheep in aerial imagery. Includes dataset details, training pipeline, model weights, and Colab notebook.
DroneSeg is a full-stack semantic segmentation platform for drone/aerial imagery. Upload drone photos, run SegFormer-B2 deep learning inference to generate land-cover classification masks with bounding boxes and visualize results on an interactive map and GeoJSON export.
UNet-based density estimation framework for automated elephant counting using UAV imagery.
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