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 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.

Abstract: A method for optimizing image data for generating orthorectified image(s) related to an area of interest in an environment. The method includes receiving a first image dataset of the area of interest captured therein, identifying each of multiple objects in the area of interest, receiving attribute information related to each of the multiple identified objects, determining if one or more of the multiple identified objects satisfy at least one of a risk criteria based on the attribute information therefor, identifying a maximum relevant second area including at least the area of interest and each of the one or more of the multiple identified objects satisfying the at least one of risk criteria, and processing the first image dataset to either discard or down-sample areas other than the maximum relevant second area captured therein.