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# dji-aerial-georeferencing
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# DJI Aerial Georeferencing with Computer Vision
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This project takes a video from a DJI Mavic Air 2, combined with its flight log, Mapbox, and a computer vision model trained on Roboflow to find solar panels and uses them to visualize and extract the GPS coordinates of the recognized panels.
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This project takes a video from a DJI Mavic Air 2, combined with its flight log, Mapbox, and a computer vision model trained on Roboflow to find solar panels. It converts the machine learning model's predictions to GPS coordinates and uses them to visualize the recognized panels on a map.
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https://user-images.githubusercontent.com/870796/189461690-122f4e64-a66e-40f0-ac4b-68258a8abe7e.mov
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## Try It in Your Browser
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The project is [deployed to Github Pages here](https://roboflow-ai.github.io/dji-aerial-georeferencing/) and you can test it out with [this sample video and flight log](https://drive.google.com/drive/folders/1m0lmYyLEQJiaykf821rYtyRvlO5Q_SAf).
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## Resources
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* A blog post about how this works is in progress; check back soon.
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* Try the [aerial solar panels pre-trained computer vision model](https://universe.roboflow.com/brad-dwyer/aerial-solar-panels/model/5) in your browser on [Roboflow Universe](https://universe.roboflow.com)
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* Browse other [Aerial Imagery Datasets and Pre-Trained Models](https://universe.roboflow.com/browse/aerial)
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* [Train Your Own Computer Vision Model](https://docs.roboflow.com/quick-start) to use with this repo
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## Run It Locally
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* Clone this repo
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* Run `npm install` in the main directory
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* Run `npm run build:dev` to start a webpack build with livereload
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* Open a new terminal window and run `npx serve dist`
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* Open `http://localhost:3000` in your browser
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## Customize It
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This repo can easily be changed to run any custom model trained with [Roboflow](https://app.roboflow.com) including the thousands of [pre-trained models shared on Roboflow Universe](https://universe.roboflow.com/search?q=aerial%20imagery%20top%20down%20view%20trained%20model). Simply swap out your `publishable_key` and the `model` ID and `version` in the `ROBOFLOW_SETTINGS` at the top of [`main.js`](src/main.js).
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There are also some additional configuration options available at the top of [`renderMap.js`](src/renderMap.js).
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## Contributing
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Pull requests are welcome to improve this repo. Ideas for improvements that could be made:
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* Taking into account changes in the ground elevation & their impact on the `distance` calculations
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* Intelligently choosing the correct part of the flight log based on the duration of `isVideo` compared to the duration of the loaded video
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* Exporting a JSON file of the detected objects
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* Adding a CLI for processing outside of a web browser
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* Rendering the flight video and predictions into a single image (patching video frames together)
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* Video controls (play/pause, scrubbing)
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* Option to show the video in a static position vs flying over the flight path

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