Steps to Tree Segementation Code:

1. Python 3.6 version recommended
2. Install requirements.txt by pip install -r requirements.txt.
3. Navigate to the tree segemenation folder.
4. Run train.py
5. To visualize the segmentation from the pretrained model at an inference stage , run the inference_script.ipynb.

Steps to run the tree species classification:

1. Python 3.6 version recommended
2. Install requirements.txt by pip install -r requirements.txt.
3. Navigate to the tree species classifcation folder.
4. Unzip the files in data folder and save them in the same name train and test.
5. Ensure that train and test are the folder names.
6. Run the .ipynb file cell by cell to see the results

Steps to run planning:

1. Python libraries os, numpy, scipy, pandas, seaborn, matplotlib, and pickle required. Version must be recent. Note: seaborn and pandas must be compabtible with each other.
2. Navigate to planning/src.
3. Run main.py to run experiments. This will create planning/outputs and will fill with .png maps and .pkl data files.
4. Run process_results.py to process the results. This saves files planning/outputs/all_data_${i}.pkl with processed data in the form of numpy arrays.
5. To unzip and process data, run make_plots.py. This file reads outputs from step 4, and creates one large dataframe. Results can be sorted, sifted, etc as desired by the user. The output plots for the current file are presented in our paper.

planning/inputs contains the Google Maps inputs for the planning part of the project, as well as the 200ft-to-pixels ratio.

Addtionally, we also tried classifying tree species using the canopy images from Sierra Nevada forest. This is a work in progress and still needs further investigation which is currently not under the present scopr of the project.

1. Python 3.6 version recommended
2. Install requirements.txt by pip install -r requirements.txt.
3. Navigate to the Tree_Classification_Initial_Results and run the Training and Testing.ipynb .

Steps to collect data with the Sensor Logic Inc's uwb radar:

1. In a windows machine, connect the radar.
2. Identify the usb port (go to Device Manager and check under USB Connector Managers)
3. Change the port in line 5 of collect_data.m
4. Run collect_data.m
5. Run analyze_data.m