To do

This list needs fleshing out. Copy high-priority features from the following software projects and add them below as "Projects" at a scope reasonable for students to get involved.

Clashifier open source image classification

• Goal: identify wetlands species and/or oil contamination
• http://github.com/jywarren/clashifier
• GPLv3

Project: abstract Classifiers class to make different classifiers more pluggable

Description: Some structural changes are necessary to allow people to develop and add new classifiers to the system. It should be as easy as having a "classifier.classify()" function which accepts an RGB (or more colors) pixel value, or perhaps an image and x,y coordinates. Some of this work has been started in the /lib/ directory, but it will require some architectural changes.

• Links:
  • https://github.com/jywarren/clashifier/issues/4
  • https://github.com/jywarren/clashifier/issues/3
• Prerequisites: Ruby/Rails, some familiarity with classification algorithms like naive bayes or cartesian, or anything else
• Difficulty level: medium
• Mentor: Jeff Warren (jeff@publiclaboratory.org)

Spectral Workbench open source spectral analysis

• Goal: spectrum pattern matching to identify oil contamination
• http://github.com/jywarren/spectral-workbench
• GPLv3

Project: import open spectral databases

Description: Determine which spectral databases can be used in an open source manner (such as perhaps the HITRAN and ASTER datasets) and import them, tagging them with their source and relevant metadata. Focus on near-infrared, visible, and ultraviolet ranges.

• Links:
  • https://github.com/jywarren/spectral-workbench/issues/54
• Prerequisites: Ruby/Rails, familiarity with open data licensing and database parsing/scripting
• Difficulty level: easy
• Mentor: Jeff Warren (jeff@publiclaboratory.org)

Project: find closest matched spectra from database

Description: Given a spectrum from http://SpectralWorkbench.org, develop a search function for similar spectra.

• Links:
  • https://github.com/jywarren/spectral-workbench/issues/53
• Prerequisites: Ruby/Rails, some familiarity with (spectral) pattern matching
• Difficulty level: hard
• Mentor: Jeff Warren (jeff@publiclaboratory.org)

MapKnitter open source image rectification and GIS

• Goal: spectrum pattern matching to identify oil contamination
• http://github.com/jywarren/mapknitter
• GPLv3

open source live NDVI/NRG infrared vegetation analysis from an Android phone

• (based on code in https://github.com/jywarren/infrared-visible-video-kit)
• MIT license