The Community for Technology Leaders
2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE) (2018)
Laguna Hills, CA, USA
Sep 26, 2018 to Sep 28, 2018
ISBN: 978-1-5386-9555-5
pp: 212-217
ABSTRACT
Analyzing Digital Elevation Model (DEM) data to identify and classify landforms is an important task, which can contribute to improve the availability and quality of public open source cartography and to develop novel applications for tourism and environment monitoring. In the literature, several heuristic algorithms are documented for identifying the features of mountain regions, most notably the coordinate of summits. All these algorithms depend on parameters, which are manually set. In this paper, we explore the use of Deep Learning methods to train a model capable of identifying mountain summits, which learns from a gold standard dataset containing the coordinates of peaks in a region. The model has been trained and tested with Switzerland DEM and peak data.
INDEX TERMS
cartography, digital elevation models, learning (artificial intelligence), topography (Earth)
CITATION

R. N. Torres, P. Fraternali, F. Milani and D. Frajberg, "A Deep Learning Model for Identifying Mountain Summits in Digital Elevation Model Data," 2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), Laguna Hills, CA, USA, 2018, pp. 212-217.
doi:10.1109/AIKE.2018.00049
180 ms
(Ver 3.3 (11022016))