Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps via Hierarchical Computation of Median Elevation Regions

• URL: http://arxiv.org/abs/2307.09239v1
• Date: Tue, 18 Jul 2023 13:19:39 GMT
• Title: Generation of High Spatial Resolution Terrestrial Surface from Low Spatial Resolution Elevation Contour Maps via Hierarchical Computation of Median Elevation Regions
• Abstract summary: The conversion is similar to that of the generation of high-resolution DEM from its low-resolution DEM. It is a sequential step of the I) decomposition of the existing sparse Contour map into the maximum possible Threshold Elevation Region (TERs)
• Score: 2.4265283126387165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We proposed a simple yet effective morphological approach to convert a sparse Digital Elevation Model (DEM) to a dense Digital Elevation Model. The conversion is similar to that of the generation of high-resolution DEM from its low-resolution DEM. The approach involves the generation of median contours to achieve the purpose. It is a sequential step of the I) decomposition of the existing sparse Contour map into the maximum possible Threshold Elevation Region (TERs). II) Computing all possible non-negative and non-weighted Median Elevation Region (MER) hierarchically between the successive TER decomposed from a sparse contour map. III) Computing the gradient of all TER, and MER computed from previous steps would yield the predicted intermediate elevation contour at a higher spatial resolution. We presented this approach initially with some self-made synthetic data to show how the contour prediction works and then experimented with the available contour map of Washington, NH to justify its usefulness. This approach considers the geometric information of existing contours and interpolates the elevation contour at a new spatial region of a topographic surface until no elevation contours are necessary to generate. This novel approach is also very low-cost and robust as it uses elevation contours.

Related papers

• Learning Manifolds in High-D Point Embedding for Anisotropic Surface Approximation from Unstructured Point Clouds [33.49331958850171]
  HD-PEA is a learning-based anisotropic surface approximation framework that operates directly on unstructured point clouds.<n>We develop a novel learning-based high-dimensional (high-d) Euclidean point embedding method to map the input point clouds into a high-d manifold embedding space.<n>We extensively evaluate our method against state-of-the-art surface reconstruction approaches using several datasets.
  arXiv  Detail & Related papers  (2026-07-30T21:40:15Z)
• ARDepth: Auto-regressive Monocular Depth Estimation with Progressive Visual Conditioning [66.42476564611607]
  Diffusion models assume that depth can be recovered as a globally smooth field through iterative denoising.<n>We introduce ARDepth, which formulates depth estimation as structured auto-regressive generation.<n>We show that our approach achieves strong performance and produces structurally consistent depth predictions across scales.
  arXiv  Detail & Related papers  (2026-07-14T07:06:46Z)
• SubdivAR: Autoregressive Next-Scale Prediction for Neural Mesh Subdivision [10.62097148325093]
  Mesh subdivision is a fundamental operation for converting coarse, editable meshes into high-resolution surfaces.<n>We present SubdivAR, a neural mesh subdivision framework based on our proposed Mesh Autoregressive Representation (MAR)<n>MAR arranges meshes at different subdivision levels into an ordered scale sequence, reformulating subdivision as autoregressive next-scale prediction.
  arXiv  Detail & Related papers  (2026-06-25T14:24:03Z)
• MeshWeaver: Sparse-Voxel-Guided Surface Weaving for Autoregressive Mesh Generation [53.06620176910962]
  MeshWeaver is an autoregressive framework that treats mesh generation as a surface weaving process.<n>MeshWeaver achieves a state-of-the-art compression ratio of 18%, can generate meshes with up to 16K faces, and significantly improves geometric fidelity over prior approaches.
  arXiv  Detail & Related papers  (2026-06-03T10:15:43Z)
• Geodesic Gradient Descent: A Generic and Learning-rate-free Optimizer on Objective Function-induced Manifolds [15.866000450171887]
  Euclidean gradient descent algorithms barely capture the geometry of objective function-induced hypersurfaces.<n>We propose geodesic gradient descent (GGD), a generic and learning-rate-free Riemannian gradient descent algorithm.<n> GGD achieves test MSE reductions ranging from 35.79% to 48.76% for fully connected networks on the Burgers' dataset, and cross-entropy loss reductions ranging from 3.14% to 11.59% for convolutional neural networks on the MNIST dataset.
  arXiv  Detail & Related papers  (2026-02-28T02:04:15Z)
• Inverse Rendering for High-Genus Surface Meshes from Multi-View Images [23.03019377701584]
  Mesh-based representations are preferred as they enable the application of differential geometry theory and are optimized for modern graphics pipelines.<n>Existing inverse rendering methods often fail catastrophically on high-genus surfaces, leading to the loss of key topological features.<n>We present a topology-informed inverse rendering approach for reconstructing high-genus surface meshes from multi-view images.
  arXiv  Detail & Related papers  (2025-11-24T01:44:09Z)
• Reconstruction of Contour Lines During the Digitization of Contour Maps to Build a Digital Elevation Model [0.0]
  broken contour segments impose a greater risk while building a Digital Elevation Model (DEM) In this project, a simple yet efficient mechanism is used to match and reconnect the endpoints of the broken segments accurately and efficiently. The purpose of this work is to reconnect the broken contour lines generated during the digitization of the contour map, to help build the most appropriate digital elevation model for the corresponding contour map.
  arXiv  Detail & Related papers  (2024-12-20T03:02:42Z)
• Hyperboloid GPLVM for Discovering Continuous Hierarchies via Nonparametric Estimation [41.13597666007784]
  Dimensionality reduction (DR) offers a useful representation of complex high-dimensional data. Recent DR methods focus on hyperbolic geometry to derive a faithful low-dimensional representation of hierarchical data. This paper presents hGP-LVMs to embed high-dimensional hierarchical data with implicit continuity via nonparametric estimation.
  arXiv  Detail & Related papers  (2024-10-22T05:07:30Z)
• ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis [14.013976303831313]
  ImplicitTerrain is an implicit neural representation (INR) approach for modeling high-resolution terrain continuously and differentiably. Our experiments demonstrate superior surface fitting accuracy, effective topological feature retrieval, and various topographical feature extraction.
  arXiv  Detail & Related papers  (2024-05-31T23:05:34Z)
• Implicit-ARAP: Efficient Handle-Guided Deformation of High-Resolution Meshes and Neural Fields via Local Patch Meshing [18.353444950896527]
  We present the local patch mesh representation for neural signed distance fields. This technique allows to discretize local regions of the level sets of an input SDF by projecting and deforming flat patch meshes onto the level set surface. We introduce two distinct pipelines, which make use of 3D neural fields to compute As-Rigid-As-Possible deformations of both high-resolution meshes and neural fields.
  arXiv  Detail & Related papers  (2024-05-21T16:04:32Z)
• GeoGaussian: Geometry-aware Gaussian Splatting for Scene Rendering [83.19049705653072]
  During the Gaussian Splatting optimization process, the scene's geometry can gradually deteriorate if its structure is not deliberately preserved. We propose a novel approach called GeoGaussian to mitigate this issue. Our proposed pipeline achieves state-of-the-art performance in novel view synthesis and geometric reconstruction.
  arXiv  Detail & Related papers  (2024-03-17T20:06:41Z)
• Hi-Map: Hierarchical Factorized Radiance Field for High-Fidelity Monocular Dense Mapping [51.739466714312805]
  We introduce Hi-Map, a novel monocular dense mapping approach based on Neural Radiance Field (NeRF) Hi-Map is exceptional in its capacity to achieve efficient and high-fidelity mapping using only posed RGB inputs.
  arXiv  Detail & Related papers  (2024-01-06T12:32:25Z)
• PRS: Sharp Feature Priors for Resolution-Free Surface Remeshing [30.28380889862059]
  We present a data-driven approach for automatic feature detection and remeshing. Our algorithm improves over state-of-the-art by 26% normals F-score and 42% perceptual $textRMSE_textv$.
  arXiv  Detail & Related papers  (2023-11-30T12:15:45Z)
• Neural Gradient Learning and Optimization for Oriented Point Normal Estimation [53.611206368815125]
  We propose a deep learning approach to learn gradient vectors with consistent orientation from 3D point clouds for normal estimation. We learn an angular distance field based on local plane geometry to refine the coarse gradient vectors. Our method efficiently conducts global gradient approximation while achieving better accuracy and ability generalization of local feature description.
  arXiv  Detail & Related papers  (2023-09-17T08:35:11Z)
• Deep Manifold Learning with Graph Mining [80.84145791017968]
  We propose a novel graph deep model with a non-gradient decision layer for graph mining. The proposed model has achieved state-of-the-art performance compared to the current models.
  arXiv  Detail & Related papers  (2022-07-18T04:34:08Z)
• Improving Metric Dimensionality Reduction with Distributed Topology [68.8204255655161]
  DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term. We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets.
  arXiv  Detail & Related papers  (2021-06-14T17:19:44Z)
• Understanding Integrated Gradients with SmoothTaylor for Deep Neural Network Attribution [70.78655569298923]
  Integrated Gradients as an attribution method for deep neural network models offers simple implementability. It suffers from noisiness of explanations which affects the ease of interpretability. The SmoothGrad technique is proposed to solve the noisiness issue and smoothen the attribution maps of any gradient-based attribution method.
  arXiv  Detail & Related papers  (2020-04-22T10:43:19Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.