Chokepoint detection using high resolution imagery
This project was class project done by me on my machine learning class in Brown University.
The major critical control of the flow of water is the chokepoints which are the bedrocks in the river. These bedrocks play a great role in hydrological connectivity. When there is low flow the bedrocks in the river obstruct the flow of the river and during the high flow the rivers overtop them forming white water riffle. Identifying them manually is a very tedious process. So, in this project, I have various object detection models for detecting bedrocks in the rivers. I used Single Shot Detector (SSD), Faster Region-based Convolutional Neural Network (Faster RCNN) and You look only once (Yolo)s object detection algorithms to detect bedrocks in the rivers. I used panchromatic Worldview imagery of 0.5 m resolution for this purpose. None of the models produce high accuracy. However faster RCNN model was better than other models in terms of precision and recall. The reason for bad results may be the use of panchromatic images because the model got confused with objects and background. More training data, pansharpened images, and the use of precise parameter tuning might increase the result
Go to this github link to see source code
