Digital photogrammetric analysis approaches for the realization of detailed terrain models

2020 ◽  
Vol 52 ◽  
pp. 69-75
Author(s):  
Antonio Minervino Amodio ◽  
Pietro P.C. Aucelli ◽  
Vittorio Garfì ◽  
Carmen M. Rosskopf
Keyword(s):  
Terrain Models

Experimental research on development of the analytical Earth’s terrain models

2018 ◽  
Vol 62 (6) ◽  
pp. 649-658
Author(s):  
Zhdanov A.Yu. ◽  
◽  
Stepanova I.E. ◽  
Chugunov I.P. ◽  
◽  
...  
Keyword(s):  
Experimental Research ◽  
Terrain Models

Construction of 3-D Terrain Models from BIG Data Sets

2014 ◽  
Author(s):  
Pankaj K. Agarwal ◽  
Thomas Moelhave
Keyword(s):  
Big Data ◽  
Data Sets ◽  
Terrain Models

THE USE OF STRUCTURE FROM MOTION TO GENERATE HIGH RESOLUTION TERRAIN MODELS

2018 ◽  
Author(s):  
Russell Krueger ◽  
◽  
Alexander J.P. Idarraga ◽  
Lucas Zoet
Keyword(s):  
High Resolution ◽  
Structure From Motion ◽  
Terrain Models

scholarly journals Lidar-Based Morphometry of Conical Hills in Temperate Karst Areas in Slovenia

2021 ◽  
Vol 13 (14) ◽  
pp. 2668
Author(s):  
Tamás Telbisz
Keyword(s):  
Empirical Distribution ◽  
Inverse Gaussian ◽  
Digital Terrain ◽  
Terrain Models ◽  
Starting Point ◽  
Significant Difference ◽  
Karst Areas ◽  
Hill Area ◽  
The Hill ◽  
Comprehensive Study

Conical hills, or residual hills, are frequently mentioned landforms in the context of humid tropical karsts as they are dominant surface elements there. Residual hills are also present in temperate karsts, but generally in a less remarkable way. These landforms have not been thoroughly addressed in the literature to date, therefore the present article is the first attempt to morphometrically characterize temperate zone residual karst hills. We use the methods already developed for doline morphometry, and we apply them to the “inverse” topography using LiDAR-based digital terrain models (DTMs) of three Slovenian sample areas. The characteristics of hills and depressions are analysed in parallel, taking into account the rank of the forms. A common feature of hills and dolines is that, for both types, the empirical distribution of planform areas has a strongly positive skew. After logarithmic transformation, these distributions can be approximated by Inverse Gaussian, Normal, and Weibull distributions. Along with the rank, the planform area and vertical extent of the hills and dolines increase similarly. High circularity is characteristic only of the first-rank forms for both dolines and hills. For the sample areas, the the hill area ratios and the doline area ratios have similar values, but the total extent of the hills is slightly larger in each case. A difference between dolines and hills is that the shapes of hills are more similar to one another than those of dolines. The reason for this is that the larger, closed depressions are created by lateral coalescence, while the hills are residual forms carved from large blocks. Another significant difference is that the density of dolines is much higher than that of hills. This article is intended as a methodological starting point for a new topic, aiming at the comprehensive study of residual karst hills across different climatic areas.

scholarly journals IMG2nDSM: Height Estimation from Single Airborne RGB Images with Deep Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 2417
Author(s):  
Savvas Karatsiolis ◽  
Andreas Kamilaris ◽  
Ian Cole
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Aerial Imagery ◽  
Aerial Images ◽  
Surface Model ◽  
Large Area ◽  
Digital Terrain ◽  
Terrain Models ◽  
Architectural Features ◽  
Rgb Images

Estimating the height of buildings and vegetation in single aerial images is a challenging problem. A task-focused Deep Learning (DL) model that combines architectural features from successful DL models (U-NET and Residual Networks) and learns the mapping from a single aerial imagery to a normalized Digital Surface Model (nDSM) was proposed. The model was trained on aerial images whose corresponding DSM and Digital Terrain Models (DTM) were available and was then used to infer the nDSM of images with no elevation information. The model was evaluated with a dataset covering a large area of Manchester, UK, as well as the 2018 IEEE GRSS Data Fusion Contest LiDAR dataset. The results suggest that the proposed DL architecture is suitable for the task and surpasses other state-of-the-art DL approaches by a large margin.

scholarly journals Nationwide Digital Terrain Models for Topographic Depression Modelling in Detection of Flood Detention Areas

Water ◽  
2014 ◽  
Vol 6 (2) ◽  
pp. 271-300 ◽  
Author(s):  
Jenni-Mari Vesakoski ◽  
Petteri Alho ◽  
Juha Hyyppä ◽  
Markus Holopainen ◽  
Claude Flener ◽  
...  
Keyword(s):  
Digital Terrain Models ◽  
Digital Terrain ◽  
Terrain Models
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