Version of PING-Mapper v2.0.0 (11/13/2023) highlighted in a forthcoming manuscript on reproducible substrate mapping. PINGMapper v2.0.0 adds to existing functionality from v1.0.0 and many bug fixes. If you encounter any issues, please report them here with the corresponding logs. New Features Automated Substrate Classification Neural network models trained with Segmentation Gym have been incorporated into PINGMapper. The models will perform a pixel-wise prediction across 6 different substrate classes [Fines - Rippled, Fines - Flat, Cobble - Boulder, Hard Bottom, Wood, Other]. Plots of the predictions can be optionally exported. Raster and polygon maps can also be exported and overlayed on the sonar mosaics. NOTE: Exercise caution when interpreting and using the outputs from the substrate prediction. The models were trained on two river systems in Mississippi. It is currently unknown how well the models will perform on other aquatic systems. Image Corrections To correct for the impact of attenuation on the sonar imagery, a new feature called Empirical Gain Normalization (EGN) is now available. This process involves calculating the average pixel intensity for each range bin and dividing the raw backscatter by the associated average. NOTE: Correcting imagery with EGN does take some time; please be patient. Use Matplotlib colormaps on sonar mosaics You can now assign one of matplotlibs many colormaps to sonar mosaics. GUI PINGMapper now includes a simple GUI for passing processing parameters rather then editing the Python script. Run the GUI with python gui_main.py or python gui_main_batchDirectory.py. Ready to get started? New PINGMapper Users Please follow the installation instructions. Existing PINGMapper Users Update your current installation by following these instructions. Then check to make sure everything is running as expected by running the test. What's Changed Consult the release for more information.