Abstract: A system for remote labelling of hyperspectral data, the system including: visible-light camera(s), hyperspectral camera(s), Light Detection and Ranging (LiDAR) scanner for capturing LiDAR data, geolocation device for generating geolocation data, and processor(s) configured to: control hyperspectral camera(s) to capture hyperspectral image(s) of first location in real-world environment; control visible-light camera(s) to capture visible-light image(s) of first location; georeference hyperspectral image(s) and visible-light image(s) using at least LiDAR data and geolocation data; align hyperspectral image(s) and visible-light image(s) with respect to each other in pixel-wise manner; and label pixels of hyperspectral image(s) to generate labelled hyperspectral image(s), by using corresponding pixels of visible-light image(s) as ground truth material.

Abstract: A method and a system for processing a point-cloud data. The method includes splitting the point-cloud data into a training dataset and a test dataset; segmenting the point-cloud data in the training dataset to define a plurality of point-cloud data tiles corresponding to an area of predetermined size; sampling the plurality of point-cloud data tiles to select the point-cloud data tiles including at least one data point corresponding to one or more predefined classes; dividing each of the selected point-cloud data tiles into a plurality of voxels of a predetermined volume; filtering a data point from each of the plurality of voxels having a lowest value for corresponding pulse returns ratio; normalizing the point-cloud data tiles with the filtered data points; and implementing the normalized point-cloud data tiles in a graph neural network for training thereof.

Abstract: A method for remotely analysing trees present in environment, including: obtaining LiDAR dataset of environment; detecting tree(s) represented in LiDAR dataset using pre-trained graph neural network, wherein tree(s) is assigned unique identifier upon detection; identifying trunk of tree(s) using statistical technique(s); determining directional vector of trunk of tree(s) using linear fitting technique(s); determining diameter of trunk of tree(s) at predetermined height from highest point of ground surface surrounding trunk, wherein directional vector is employed for determining diameter of the trunk; and predicting age of tree(s), based at least on diameter of trunk.