Techniques for deep learning with satellite & aerial imagery

An open source library and framework for deep learning on satellite and aerial imagery.

Semantic segmentation on aerial and satellite imagery. Extracts features such as: buildings, parking lots, roads, water, clouds

OpenBot leverages smartphones as brains for low-cost robots. We have designed a small electric vehicle that costs about $50 and serves as a robot body. Our software stack for Android smartphones supports advanced robotics workloads such as person following and real-time autonomous navigation.

Aerostack2 is a ROS 2 framework developed to create autonomous multi-aerial-robots systems in an easy and powerful way.

An open framework to simulate and deploy perception-based PX4/ArduPilot drone swarms with ROS2, YOLO, LiDAR, NVIDIA Jetson

Vehicle and mobile robotics simulator. C++ & Python API. Use it as a standalone application or via ROS 1 or ROS 2

Photogrammetry Guide. Photogrammetry is widely used for Aerial surveying, Agriculture, Architecture, 3D Games, Robotics, Archaeology, Construction, Emergency management, and Medical.

Vehicle detection using machine learning and computer vision techniques for Udacity's Self-Driving Car Engineer Nanodegree.

Aerial Object Detection using a Drone with PX4 Autopilot and ROS 2. PX4 SITL and Gazebo Garden used for Simulation. YOLOv8 used for Object Detection.

Data Driven Dynamics Modeling for Aerial Vehicles

Develop art direction for image generation with curated style cards distilled from Open Image Prompts. Use when choosing or comparing visual directions, improving an existing generation prompt, or translating a subject, use case, or supplied image into a compact creative spec. Use img-gen-prompts for real archive examples, exact source prompts, or gallery browsing.