A methodology for automated labelling a geospatial image dataset of applicable locations for installing a wireless nodal seismic system

A developing area of wireless nodal seismic systems installation rises an urgent problem of identification of applicable areas for mounting wireless seismic modules. The identification of applicable areas could be done using geospatial image analysis methods, which require representative datasets that reflect proper features of the surfaces related exactly to the requirements of seismic module installation. This states the problem of development of a methodology for labelling such datasets. This work is devoted to developing methodology for automated labelling of geospatial images using georeferece data from OpenStreetMap that provides accurate vector georeferences of distinct objects, however, suffer from class labels inconsistence (labelling the same object by multiple classes, labelling mistakes, objects overlapping). The distinctive features of the methodology are the development of system of surface classes specific to the properties of applicable surfaces for seismic modules installation and mapping procedure of OSM objects to the developed classification classes based on manual inspection of the OSM objects. The other features of the methodology are data representativeness in terms of geography, obtaining time, as well as maintaining the same lightning conditions. The collected according to the methodology dataset consists of 200 labelled images. The mapping procedure allows avoiding collisions in classes’ labels caused by OSM class hierarchy inconsistency. OSM labels covers 90% of the obtained images.