Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding

• URL: http://arxiv.org/abs/2007.09547v1
• Date: Sun, 19 Jul 2020 01:04:19 GMT
• Title: Sat2Graph: Road Graph Extraction through Graph-Tensor Encoding
• Authors: Songtao He, Favyen Bastani, Satvat Jagwani, Mohammad Alizadeh, Hari Balakrishnan, Sanjay Chawla, Mohamed M. Elshrif, Samuel Madden, Amin Sadeghi
• Abstract summary: In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation. We find that Sat2Graph surpasses prior methods on two widely used metrics, TOPO and APLS.
• Score: 25.55895733077606
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inferring road graphs from satellite imagery is a challenging computer vision task. Prior solutions fall into two categories: (1) pixel-wise segmentation-based approaches, which predict whether each pixel is on a road, and (2) graph-based approaches, which predict the road graph iteratively. We find that these two approaches have complementary strengths while suffering from their own inherent limitations. In this paper, we propose a new method, Sat2Graph, which combines the advantages of the two prior categories into a unified framework. The key idea in Sat2Graph is a novel encoding scheme, graph-tensor encoding (GTE), which encodes the road graph into a tensor representation. GTE makes it possible to train a simple, non-recurrent, supervised model to predict a rich set of features that capture the graph structure directly from an image. We evaluate Sat2Graph using two large datasets. We find that Sat2Graph surpasses prior methods on two widely used metrics, TOPO and APLS. Furthermore, whereas prior work only infers planar road graphs, our approach is capable of inferring stacked roads (e.g., overpasses), and does so robustly.

Related papers

• LineGraph2Road: Structural Graph Reasoning on Line Graphs for Road Network Extraction [0.0]
  LineGraph2Road is a framework that improves connectedness prediction by formulating it as binary classification over edges in a constructed global but sparse Euclidean graph.<n>We evaluate it on three benchmarks: City-scale, SpaceNet, and Global-scale, and show that it achieves state-of-the-art results on two key metrics, TOPO-F1 and APLS.
  arXiv  Detail & Related papers  (2026-02-26T18:02:44Z)
• InstructG2I: Synthesizing Images from Multimodal Attributed Graphs [50.852150521561676]
  We propose a graph context-conditioned diffusion model called InstructG2I. InstructG2I first exploits the graph structure and multimodal information to conduct informative neighbor sampling. A Graph-QFormer encoder adaptively encodes the graph nodes into an auxiliary set of graph prompts to guide the denoising process.
  arXiv  Detail & Related papers  (2024-10-09T17:56:15Z)
• LaneGraph2Seq: Lane Topology Extraction with Language Model via Vertex-Edge Encoding and Connectivity Enhancement [34.017743757153866]
  Intricate road structures are often depicted using lane graphs, which include centerline curves and connections forming a Directed Acyclic Graph (DAG) We introduce LaneGraph2Seq, a novel approach for lane graph extraction. Our method demonstrates superior performance compared to state-of-the-art techniques in lane graph extraction.
  arXiv  Detail & Related papers  (2024-01-31T05:44:01Z)
• Semi-Supervised Hierarchical Graph Classification [54.25165160435073]
  We study the node classification problem in the hierarchical graph where a 'node' is a graph instance. We propose the Hierarchical Graph Mutual Information (HGMI) and present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
  arXiv  Detail & Related papers  (2022-06-11T04:05:29Z)
• Schema-Guided Event Graph Completion [103.32326979796596]
  We tackle a new task, event graph completion, which aims to predict missing event nodes for event graphs. Existing prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph.
  arXiv  Detail & Related papers  (2022-06-06T21:51:10Z)
• Road Extraction from Overhead Images with Graph Neural Networks [18.649284163019516]
  We propose a method that directly infers the final road graph in a single pass. The key idea consists in combining a Fully Convolutional Network in charge of locating points of interest and a Graph Neural Network which predicts links between these points. We evaluate our method against existing works on the popular RoadTracer dataset and achieve competitive results.
  arXiv  Detail & Related papers  (2021-12-09T21:10:27Z)
• Graphon based Clustering and Testing of Networks: Algorithms and Theory [11.3700474413248]
  Network-valued data are encountered in a wide range of applications and pose challenges in learning. We present two clustering algorithms that achieve state-of-the-art results. We further study the applicability of the proposed distance for graph two-sample testing problems.
  arXiv  Detail & Related papers  (2021-10-06T13:14:44Z)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• Second-Order Pooling for Graph Neural Networks [62.13156203025818]
  We propose to use second-order pooling as graph pooling, which naturally solves the above challenges. We show that direct use of second-order pooling with graph neural networks leads to practical problems. We propose two novel global graph pooling methods based on second-order pooling; namely, bilinear mapping and attentional second-order pooling.
  arXiv  Detail & Related papers  (2020-07-20T20:52:36Z)
• Unsupervised Graph Representation by Periphery and Hierarchical Information Maximization [18.7475578342125]
  Invent of graph neural networks has improved the state-of-the-art for both node and the entire graph representation in a vector space. For the entire graph representation, most of existing graph neural networks are trained on a graph classification loss in a supervised way. We propose an unsupervised graph neural network to generate a vector representation of an entire graph in this paper.
  arXiv  Detail & Related papers  (2020-06-08T15:50:40Z)
• Wasserstein-based Graph Alignment [56.84964475441094]
  We cast a new formulation for the one-to-many graph alignment problem, which aims at matching a node in the smaller graph with one or more nodes in the larger graph. We show that our method leads to significant improvements with respect to the state-of-the-art algorithms for each of these tasks.
  arXiv  Detail & Related papers  (2020-03-12T22:31:59Z)

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.