Semi-Automated Segmentation of Geoscientific Data Using Superpixels
- URL: http://arxiv.org/abs/2303.11404v1
- Date: Mon, 20 Mar 2023 19:21:46 GMT
- Title: Semi-Automated Segmentation of Geoscientific Data Using Superpixels
- Authors: Conrad P. Koziol and Eldad Haber
- Abstract summary: Geological processes determine the distribution of resources such as critical minerals, water, and geothermal energy.
Inspired by the concept of superpixels, we propose a deep-learning based approach to segmentized survey data into regions with similar characteristics.
- Score: 4.035753155957697
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Geological processes determine the distribution of resources such as critical
minerals, water, and geothermal energy. However, direct observation of geology
is often prevented by surface cover such as overburden or vegetation. In such
cases, remote and in-situ surveys are frequently conducted to collect physical
measurements of the earth indicative of the geology. Developing a geological
segmentation based on these measurements is challenging since individual
datasets can differ in properties (e.g. units, dynamic ranges, textures) and
because the data does not uniquely constrain the geology. Further, as the
number of datasets grows the information to constrain geology increases while
simultaneously becoming harder to make sense of. Inspired by the concept of
superpixels, we propose a deep-learning based approach to segment rasterized
survey data into regions with similar characteristics. We demonstrate its use
for semi-automated geoscientific mapping with datasets arising from independent
sensors and with diverse properties. In addition, we introduce a new loss
function for superpixels including a novel regularization parameter penalizing
image segmentation with non-connected component superpixels. This improves
integration of prior knowledge by allowing better control over the number of
superpixels generated.
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