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.

Drone GeoTIFF üzerinde genç mısır segmentasyonu, sıra analizi ve aday ekim boşluğu tespiti

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.

Deep learning pipeline (YOLOv5 + Detectron2) for detecting maize tassels in UAV imagery — CSE499 senior design project.

UNet-based density estimation framework for automated elephant counting using UAV imagery.

FieldSight AI is a full-stack platform for analyzing drone imagery to detect water pooling, generate field insights, and visualize results through interactive heatmaps.

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.

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.

Genomic and phenomic prediction of agronomic traits and malt quality in NY Winter Malting Barley

DNN-derived synthetic vegetation index for agronomic trait prediction from multispectral drone imagery

End-to-end MLOps pipeline for 5-class semantic segmentation of drone imagery using nnU-Net (PyTorch). Features DVC, Docker, BentoML serving & CI/CD.

High-resolution drone imagery dataset of rural landscapes in Central Europe, designed for AI/ML training, computer vision, and geospatial analysis.

OpenSpace — 360-degree jobsite documentation and analytics

Detection of mosquito breeding sites in aerial drone imagery using YOLO and data augmentation techniques.