Modern remote sensing methods such as photogrammetry, photoclinometry, LIDAR, laser altimetry, multi/hyperspectral sensors, radar and satellite imaging, represent an invaluable and increasingly accessible source of geoscience data, from the scale of the outcrop (1 m to 1 km) to the geological map (1 km to 100 km). In addition, these technologies have high potential for forthcoming human exploration of planetary bodies, such as the Moon and Mars. Development of efficient workflows allowing extraction of useful quantitative geological information in a time- and cost-effective manner remains an open issue, even if many advances have been achieved over the last few years.
Many geologists use 3D image products and simple but widely available software (e.g. GoogleEarth) as a framework for “manual” interpretation of geological features, such as stratigraphic/lithological contacts, fractures, faults, foliations, etc. This approach is cost-effective but sometimes time-consuming, and often not so accurate.
In recent research projects, an increasingly numerous community is trying to develop automatic or semiautomatic tools allowing to make the interpretation of 2D or 3D datasets less time-consuming and, at the same time, more quantitative and replicable (so less influenced by the geologist’s subjectivity). Interesting results have been reported, but we are still far from having a solution to this problem.
Another research thread regards technological developments allowing carrying out interpretation directly in the field, using mobile devices (rugged PCs, tablets, smartphones). The advantages are both in terms of time required back in the office and, more importantly, direct validation of interpretations while physically “standing on the outcrop”.
Finally, software is being developed to perform detailed quantitative analysis on the results of an interpretation workflow, including structural analysis (orientation analysis, scanlines and scan areas, etc.), stratigraphic and sedimentological analysis (facies mapping, virtual borehole logs, etc.), and generating training images for geostatistical modelling.
We solicit contributions addressing all these technical challenges and case studies where digital mapping techniques are being applied to real geological problems, improving our ability to collect quantitative data in the field.