road-surface-detection

This is an example road crack detection deep learning workflow using an ArcGIS Learn Single Shot Detection model. This running demonstration was built off the example notebook with a lot more detailed theoretical explanation included in the Esri documentation demonstrating road surface investigation.

Getting Started

After cloning the directory, everything you need is in the three notebooks in the notebook directory. They are numbered, and are in order. The first notebook sets up all the data, and you only need to run it once. The second, trains the model, and the last demonstrates inferencing.

Project Organization

---

    ├── LICENSE
    ├── make.bat           <- Windows batch file with commands like `make data`
    ├── setup.py           <- Setup script for the library (road_surface_detection)
    ├── .env               <- Any environment variables here - created as part of project creation, 
    │                         but NOT syncronized with git repo for project.                
    ├── README.md          <- The top-level README for developers using this project.
    ├── arcgis             <- Root location for ArcGIS Pro project created as part of
    │   │                     data science project creation.
    │   ├── road-surface-detection.aprx <- ArcGIS Pro project.    
    │   └── road-surface-detection.tbx  <- ArcGIS Pro toolbox associated with the project.
    ├── data
    │   ├── external       <- Data from third party sources.
    │   ├── interim        <- Intermediate data that has been transformed.
    │   │   └── interim.gdb
    │   ├── processed      <- The final, canonical data sets for modeling.
    │   │   └── processed.gdb
    │   └── raw            <- The original, immutable data dump.
    │       └── raw.gdb
    ├── docs               <- A default Sphinx project; see sphinx-doc.org for details
    ├── models             <- Trained and serialized models, model predictions, or model summaries
    ├── notebooks          <- Jupyter notebooks. Naming convention is a 2 digits (for ordering),
    │                         descriptive name. e.g.: 01_exploratory_analysis.ipynb
    ├── references         <- Data dictionaries, manuals, and all other explanatory materials.
    ├── reports            <- Generated analysis as HTML, PDF, LaTeX, etc.
    │   └── figures        <- Generated graphics and figures to be used in reporting
    ├── environment.yml    <- The requirements file for reproducing the analysis environment. This 
    │                         is generated by running `conda env export > environment.yml.`                        
    └── src                <- Source code for use in this project.
        └── road_surface_detection <- Library containing the bulk of code used in this 
                                                  project. 

Project based on the cookiecutter GeoAI project template. This template, in turn, is simply an extension and light modification of the cookiecutter data science project template. #cookiecutterdatascience