Balanced On-line Learning Toolkit is an open-source library that contains a set of on-line classifier interfaces and their implementations. On-line learning (also called data stream mining) is the task of learning from streaming data. It means that a classifier should be always able to classify some data, even if learning process has not been finished yet. Moreover, the time of single example...

We develop the LidarK library. It is the open-source library which allows performing different operations on multidimensional point data such as 3D LIDAR scans. Currently it allows building a spatial index. Though it is intended to be used on LIDAR scans it can be applied to a wide range of problems which require spatial data processing.

We develop an interactive semantic segmentation system which efficiently analyzes road pavement photos. First, a user gets a piece of an image and marks road marking and road defects on it, then the classifier is being learned. The next image part is pretended to be classified, so the user should correct classification errors. After this correction the classifier is learned again. While more...