Update README.md

README.md

@@ -21,7 +21,7 @@ In [Track 4](track4) the aim is to label points from the given aerial point clou
 For tracks 1-3, performance is assessed using the pixel-wise mean Intersection over Union (mIoU) for which true positives must have both the correct semantic label and height error less than a given threshold (1 meter for heights or 3 pixels for disparities). We call this metric mIoU-3.
 
 ## Data
-JHU/APL has provided a large data set, including ground truth, for training and testing. Instructions for acquiring and using the data are located in the [data](data) directory.
+JHU/APL has provided a large data set, including ground truth, for training and testing. Instructions for acquiring and using the data are located in the [data](data) directory. Data is also available at IEEE DataPort (https://ieee-dataport.org/open-access/data-fusion-contest-2019-dfc2019).
 
 ## Baseline algorithms
 JHU/APL has developed baseline implementations in python for each challenge track to demonstrate how to manipulate the challenge data and produce valid submission files. These baselines are available in the Track 1-4 folders referenced above.