Land Cover Mapping Tool

This repository hold both the "frontend" web-application and "backend" web API server that make up our "Land Cover Mapping" demo. An instance of this demo is live, here.

Overview

• "Frontend"
  • index.html, endpoints.js
  • Whenever an user clicks somewhere on the map, the app will query each server defined in endpoints.js and show the results overlayed on the map.
• "Backend"
  • Consists of backend_server.py, ServerModels*.py, DataLoader.py
  • backend_server.py starts a bottle server to serve the API
    • Can be provided a port via command line argument, must be provided a "model" to serve via command line argument.
    • The "model" that is provided via the command line argument corresponds to one of the ServerModels*.py files. Currently this interface is just an ugly hack.
  • DataLoader.py contains all the code for finding the data assosciated with a given spatial query.

API

The "backend" server provides the following API:

POST /predPatch

Input example:

{
    "extent": { // definition of bounding box to run model on
        "xmax": bottomright.x,
        "xmin": topleft.x,
        "ymax": topleft.y,
        "ymin": bottomright.y,
        "spatialReference": {
            "latestWkid": 3857 // CRS of the coordinates
        }
    },
    "weights": [0.25, 0.25, 0.25, 0.25], // reweighting of the softmax outputs, there should be one number (per class)
}

Output example:

{
    "extent": ..., // copied from input
    "weights": ..., // copied from input
    "model_name": "Full-US-prerun", // name of the model being served
    "input_naip": "..." // base64 encoding of input NAIP imagery used to generate the model output, as PNG
    "output_hard": "..." // base64 encoding of hard class estimates, also as PNG
    "output_soft": "..." // base64 encoding of soft class estimates, see `utils.class_prediction_to_img()` for how image is generated

}

POST /getInput

Input example:

{
    "extent": { // definition of bounding box to run model on
        "xmax": bottomright.x,
        "xmin": topleft.x,
        "ymax": topleft.y,
        "ymin": bottomright.y,
        "spatialReference": {
            "latestWkid": 3857 // CRS of the coordinates
        }
    },
}

Output example:

{
    "extent": ..., // copied from input
    "input_naip": "..." // base64 encoding of input NAIP imagery used to generate the model output, as PNG
}

Setup

Copy the files from //mslandcoverstorageeast.file.core.windows.net/chesapeake/demo_data/ into data/. This should include: list_all_naip.txt, tile_index.dat, tile_index.idx, tiles.p.

Issues/To-do list

• /predPatch will probably not work with other CRSs (besides EPSG:3857)
• /predPatch will probably not fail in an useful way
• We want the backend_server.py to be decoupled from the implementation of the code needed to run the model. The way this currently works (in main() of backend_server.py) is really hacky.