A QGIS plugin for tree monitoring using AI.

GreenShield Mobile App

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

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).

Final Year Project developed for my BSc (Hons) in Computing.

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.

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.

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

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.

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

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

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

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.

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

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

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