Multi-Point Proximity Encoding For Vector-Mode Geospatial Machine Learning

• URL: http://arxiv.org/abs/2506.05016v1
• Date: Thu, 05 Jun 2025 13:22:47 GMT
• Title: Multi-Point Proximity Encoding For Vector-Mode Geospatial Machine Learning
• Authors: John Collins,
• Abstract summary: This paper presents an encoding method based on scaled distances from a shape to a set of reference points within a region of interest.<n>The method, MultiPoint (MPP) encoding, can be applied to any type of shape, enabling the parameterization of machine learning models with encoded representations of vector-mode features.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vector-mode geospatial data -- points, lines, and polygons -- must be encoded into an appropriate form in order to be used with traditional machine learning and artificial intelligence models. Encoding methods attempt to represent a given shape as a vector that captures its essential geometric properties. This paper presents an encoding method based on scaled distances from a shape to a set of reference points within a region of interest. The method, MultiPoint Proximity (MPP) encoding, can be applied to any type of shape, enabling the parameterization of machine learning models with encoded representations of vector-mode geospatial features. We show that MPP encoding possesses the desirable properties of shape-centricity and continuity, can be used to differentiate spatial objects based on their geometric features, and can capture pairwise spatial relationships with high precision. In all cases, MPP encoding is shown to perform better than an alternative method based on rasterization.

Related papers

• Geometry Distributions [51.4061133324376]
  We propose a novel geometric data representation that models geometry as distributions. Our approach uses diffusion models with a novel network architecture to learn surface point distributions. We evaluate our representation qualitatively and quantitatively across various object types, demonstrating its effectiveness in achieving high geometric fidelity.
  arXiv  Detail & Related papers  (2024-11-25T04:06:48Z)
• GeoCoder: Solving Geometry Problems by Generating Modular Code through Vision-Language Models [10.443672399225983]
  Vision-parametric models (VLMs) have made significant progress in various multimodal tasks. They still struggle with geometry problems and are significantly limited by their inability to perform mathematical operations not seen during pre-training. We present GeoCoder, which leverages modular code-finetuning to generate and execute code using a predefined geometry function library.
  arXiv  Detail & Related papers  (2024-10-17T12:56:52Z)
• Poly2Vec: Polymorphic Fourier-Based Encoding of Geospatial Objects for GeoAI Applications [6.1981153537308336]
  Poly2Vec is a polymorphic Fourier-based encoding approach that unifies the representation of geospatial objects.<n>We show that Poly2Vec consistently outperforms object-specific baselines in preserving three key spatial relationships.
  arXiv  Detail & Related papers  (2024-08-27T06:28:35Z)
• Learning Geometric Invariant Features for Classification of Vector Polygons with Graph Message-passing Neural Network [3.804240190982697]
  We propose a novel graph message-passing neural network (PolyMP) to learn the geometric-invariant features for shape classification of polygons. We show that the proposed graph-based PolyMP network enables the learning of expressive geometric features invariant to geometric transformations of polygons.
  arXiv  Detail & Related papers  (2024-07-05T08:19:36Z)
• Surf-D: Generating High-Quality Surfaces of Arbitrary Topologies Using Diffusion Models [83.35835521670955]
  Surf-D is a novel method for generating high-quality 3D shapes as Surfaces with arbitrary topologies. We use the Unsigned Distance Field (UDF) as our surface representation to accommodate arbitrary topologies. We also propose a new pipeline that employs a point-based AutoEncoder to learn a compact and continuous latent space for accurately encoding UDF.
  arXiv  Detail & Related papers  (2023-11-28T18:56:01Z)
• Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers [8.781861951759948]
  This paper presents Geo-SIC, the first deep learning model to learn deformable shapes in a deformation space for an improved performance of image classification. We introduce a newly designed framework that (i) simultaneously derives features from both image and latent shape spaces with large intra-class variations. We develop a boosted classification network, equipped with an unsupervised learning of geometric shape representations.
  arXiv  Detail & Related papers  (2022-10-25T01:55:17Z)
• Learning Implicit Feature Alignment Function for Semantic Segmentation [51.36809814890326]
  Implicit Feature Alignment function (IFA) is inspired by the rapidly expanding topic of implicit neural representations. We show that IFA implicitly aligns the feature maps at different levels and is capable of producing segmentation maps in arbitrary resolutions. Our method can be combined with improvement on various architectures, and it achieves state-of-the-art accuracy trade-off on common benchmarks.
  arXiv  Detail & Related papers  (2022-06-17T09:40:14Z)
• SEG-MAT: 3D Shape Segmentation Using Medial Axis Transform [49.51977253452456]
  We present an efficient method for 3D shape segmentation based on the medial axis transform (MAT) of the input shape. Specifically, with the rich geometrical and structural information encoded in the MAT, we are able to identify the various types of junctions between different parts of a 3D shape. Our method outperforms the state-of-the-art methods in terms of segmentation quality and is also one order of magnitude faster.
  arXiv  Detail & Related papers  (2020-10-22T07:15:23Z)
• DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation [98.96086261213578]
  We introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes. This supports a range of novel shape generation applications with disentangled control, such as of structure (geometry) while keeping geometry (structure) unchanged. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-12T17:06:51Z)
• TextRay: Contour-based Geometric Modeling for Arbitrary-shaped Scene Text Detection [20.34326396800748]
  We propose an arbitrary-shaped text detection method, namely TextRay, which conducts top-down contour-based geometric modeling and geometric parameter learning. Experiments on several benchmark datasets demonstrate the effectiveness of the proposed approach.
  arXiv  Detail & Related papers  (2020-08-11T16:52:10Z)
• MetaSDF: Meta-learning Signed Distance Functions [85.81290552559817]
  Generalizing across shapes with neural implicit representations amounts to learning priors over the respective function space. We formalize learning of a shape space as a meta-learning problem and leverage gradient-based meta-learning algorithms to solve this task.
  arXiv  Detail & Related papers  (2020-06-17T05:14:53Z)
• Instant recovery of shape from spectrum via latent space connections [33.83258865005668]
  We introduce the first learning-based method for recovering shapes from Laplacian spectra. Given an auto-encoder, our model takes the form of a cycle-consistent module to map latent vectors to sequences of eigenvalues. Our data-driven approach replaces the need for ad-hoc regularizers required by prior methods, while providing more accurate results at a fraction of the computational cost.
  arXiv  Detail & Related papers  (2020-03-14T00:48:34Z)

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.