Predict surface reflectivity for streets using high resolution imagery

**Input Data:** - geometries of features of interest. For streets, official street centerline from LA city government was used for LA and OSM street data was used for other cities. - high-resolution four-band imagery for features of interest for times of interest. NAIP was used as the primary source of imagery. The input data consist of 105 rows and each represent data for individual streets in LA city. This is a sample of 105 streets from our master dataset of all the streets in LA city. Each row contain 4 columns. "norm_red_mean", "norm_green_mean", "norm_blue_mean" and "norm_nir_mean" are the main inputs and represent preprocessed, normalized mean band values calculated from all the pixels within a street for the year 2009. NAIP was used as the source of imagery. **Model description:** A decision forest regression model trained in this [experiment][1] was used to make prediction. **Output:** The "Scored Label Mean" column from the "Score Model" module represent the albedo prediction for each street in 2009. This output will be used to create the final product which is a vector dataset with estimated mean reflectivity (unitless albedo, 0-1) for each feature of interest (roof, street segment, etc.) for each time of interest. Possibility to also produce a raster version of this dataset--estimating the reflectivity at each pixel. [1]: https://gallery.azure.ai/Experiment/Train-a-decision-forest-model-to-predict-surface-reflectivity-of-streets