Smart-Innovations-Ghana/Illegal-Mining-Georeferencing

////////////////////////////////////////////// npm install npm run build:dev npx serve dist ////////////////////////////////////////////// # Detect and Map Objects of Interest from Drone Videos This project extracts the location of objects of interest from a drone video and plots them on a map. By combining the video with data from its flight log and a computer vision model trained on Roboflow, it demonstrates georeferencing a machine learning model's predictions to GPS coordinates and using them to visualize the location of detected solar panels on a map using Mapbox. user-images.githubuserconte… ## Try It in Your Browser The project is deployed to Github Pages here and you can test it out with this sample video and flight log. If you have your own Drone video you'd like to use, follow the instructions in the blog post to pull your detailed flight log from Airdata. ## Resources * Accompanying Blog Post: Georeferencing Objects in Drone Videos * Try the aerial solar panels pre-trained computer vision model in your browser on Roboflow Universe * Browse other Aerial Imagery Datasets and Pre-Trained Models * Train Your Own Computer Vision Model to use with this repo ## Run It Locally * Clone this repo * Run `npm install` in the main directory * Run `npm run build:dev` to start a webpack build with livereload * Open a new terminal window and run `npx serve dist` * Open `localhost` in your browser ## Customize It This repo can easily be changed to run any custom model trained with Roboflow including the thousands of pre-trained models shared on Roboflow Universe. Simply swap out your `publishable_key` and the `model` ID and `version` in the `ROBOFLOW_SETTINGS` at the top of `main.js`. There are also some additional configuration options available at the top of `renderMap.js`. For example, changing the model to `swimming-pool-b6pz4` to use this swimming pool computer …