A Continuously Updated, Global Land Classification Map

We propose to demonstrate a fully automatic capability for generating a global, high resolution (30 m) land classification map, with continuous updates from satellite imagery and crowdsourced ground truth, and an annual publication cycle. The resulting map will be fully extensible, supporting many layers including Anderson type land cover categories, specific crop types in agricultural areas, and even continuous variables such as crop yield estimates. Our methodology involves fusing imagery with existing raster and vector ground truth data sets, applying a novel classification technique to enable vastly improved spatial/temporal model portability, and continuous updating as additional imagery and crowdsourced ground truth data becomes available. By employing a systematic crowdsourcing strategy that is responsive to local and regional market incentives, the map's accuracy will improve most over time in those geographic regions where higher accuracy is most needed. The resulting map product will find immediate use for a multitude of applications in agriculture, commodities trading, ecological forecasting, wildlife conservation and climate change studies. We foresee monetizing the continuous production effort by obtaining stakeholder funding to make a standard product freely available to the global public, but we also intend to develop higher precision, commercial products for lucrative applications such as precision agriculture in targeted locations worldwide.

The proposed technology, if successful, could add tremendous value to NASA's Landsat mission, as well as to MODIS and TRMM and new environmental systems – NPP and NPOESS. It will also directly relate to efforts of the Agriculture DISC program.

Our product will likely be most attractive commercially for precision agriculture. Using our approach, it will be possible to determine how much of a particular crop type is planted globally each year and to continuously update harvest estimates throughout the year. This will find direct application for commodity trading firms, as well as commercial enterprises that seek to hedge commodity purchases. Other applications include evaluation of crop health (stress by insufficient water, insect infestations, etc.), so our work should contribute to developing analytical models for pesticide/fertilizer/water application and efficacy. Farmers globally can submit crowd sourced point information (crop type, health, etc.), and our system will incorporate weather data (precipitation, temperature, etc.). Hence there will by myriad applications in the agricultural sector.

This is early/mid-stage (TRL 4) — the most realistic path in is NASA SBIR/STTR, which funds small businesses and research institutions to develop technology aligned with NASA's needs (equity-free, phased funding). Check whether a current SBIR/STTR solicitation topic overlaps with this project's technology area, or contact the project directly (above) to ask.

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