PathwayBench: Assessing Routability of Pedestrian Pathway Networks Inferred from Multi-City Imagery

• URL: http://arxiv.org/abs/2407.16875v1
• Date: Tue, 23 Jul 2024 22:47:32 GMT
• Title: PathwayBench: Assessing Routability of Pedestrian Pathway Networks Inferred from Multi-City Imagery
• Authors: Yuxiang Zhang, Bill Howe, Sachin Mehta, Nicholas-J Bolten, Anat Caspi,
• Abstract summary: Application to support pedestrian mobility in urban areas requires a complete, and routable graph representation of the built environment. Relative to road network pathways, pedestrian network pathways are narrower, more frequently disconnected, often visually and materially variable in smaller areas. Existing algorithms to extract pedestrian pathway network graphs are inconsistently evaluated and tend to ignore routability.
• Score: 15.563635571840733
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Applications to support pedestrian mobility in urban areas require a complete, and routable graph representation of the built environment. Globally available information, including aerial imagery provides a scalable source for constructing these path networks, but the associated learning problem is challenging: Relative to road network pathways, pedestrian network pathways are narrower, more frequently disconnected, often visually and materially variable in smaller areas, and their boundaries are broken up by driveway incursions, alleyways, marked or unmarked crossings through roadways. Existing algorithms to extract pedestrian pathway network graphs are inconsistently evaluated and tend to ignore routability, making it difficult to assess utility for mobility applications: Even if all path segments are available, discontinuities could dramatically and arbitrarily shift the overall path taken by a pedestrian. In this paper, we describe a first standard benchmark for the pedestrian pathway graph extraction problem, comprising the largest available dataset equipped with manually vetted ground truth annotations (covering $3,000 km^2$ land area in regions from 8 cities), and a family of evaluation metrics centering routability and downstream utility. By partitioning the data into polygons at the scale of individual intersections, we compute local routability as an efficient proxy for global routability. We consider multiple measures of polygon-level routability and compare predicted measures with ground truth to construct evaluation metrics. Using these metrics, we show that this benchmark can surface strengths and weaknesses of existing methods that are hidden by simple edge-counting metrics over single-region datasets used in prior work, representing a challenging, high-impact problem in computer vision and machine learning.

Related papers

• Reliable, Routable, and Reproducible: Collection of Pedestrian Pathways at Statewide Scale [7.346075203371274]
  This paper presents a methodology to collect, manage, serve, and maintain pedestrian path data at a statewide scale. We aim to produce routable pedestrian pathways for the entire State of Washington within approximately two years.
  arXiv  Detail & Related papers  (2024-10-12T02:31:57Z)
• Neural Semantic Map-Learning for Autonomous Vehicles [85.8425492858912]
  We present a mapping system that fuses local submaps gathered from a fleet of vehicles at a central instance to produce a coherent map of the road environment. Our method jointly aligns and merges the noisy and incomplete local submaps using a scene-specific Neural Signed Distance Field. We leverage memory-efficient sparse feature-grids to scale to large areas and introduce a confidence score to model uncertainty in scene reconstruction.
  arXiv  Detail & Related papers  (2024-10-10T10:10:03Z)
• Road Graph Generator: Mapping roads at construction sites from GPS data [0.0]
  We propose a new method for inferring roads from GPS trajectories to map construction sites. This task presents a unique challenge due to the erratic and non-standard movement patterns of construction machinery. Our proposed method first identifies intersections in the road network that serve as critical decision points, and then connects them with edges to produce a graph.
  arXiv  Detail & Related papers  (2024-02-15T12:53:25Z)
• APE: An Open and Shared Annotated Dataset for Learning Urban Pedestrian Path Networks [16.675093530600154]
  Inferring the full transportation network, including sidewalks and cycleways, is crucial for many automated systems. This work begins to address this problem at scale by introducing a novel dataset of aerial satellite imagery, map imagery, and annotations of sidewalks, crossings, and corner bulbs in urban cities. We present an end-to-end process for inferring a connected pedestrian path network map using street network information and our proposed dataset.
  arXiv  Detail & Related papers  (2023-03-04T05:08:36Z)
• GraphWalks: Efficient Shape Agnostic Geodesic Shortest Path Estimation [93.60478281489243]
  We propose a learnable network to approximate geodesic paths on 3D surfaces. The proposed method provides efficient approximations of the shortest paths and geodesic distances estimations.
  arXiv  Detail & Related papers  (2022-05-30T16:22:53Z)
• RNGDet: Road Network Graph Detection by Transformer in Aerial Images [19.141279413414082]
  Road network graphs provide critical information for autonomous vehicle applications. manually annotating road network graphs is inefficient and labor-intensive. We propose a novel approach based on transformer and imitation learning named RNGDet.
  arXiv  Detail & Related papers  (2022-02-16T01:59:41Z)
• Road Network Guided Fine-Grained Urban Traffic Flow Inference [108.64631590347352]
  Accurate inference of fine-grained traffic flow from coarse-grained one is an emerging yet crucial problem. We propose a novel Road-Aware Traffic Flow Magnifier (RATFM) that exploits the prior knowledge of road networks. Our method can generate high-quality fine-grained traffic flow maps.
  arXiv  Detail & Related papers  (2021-09-29T07:51:49Z)
• SPIN Road Mapper: Extracting Roads from Aerial Images via Spatial and Interaction Space Graph Reasoning for Autonomous Driving [64.10636296274168]
  Road extraction is an essential step in building autonomous navigation systems. Using just convolution neural networks (ConvNets) for this problem is not effective as it is inefficient at capturing distant dependencies between road segments in the image. We propose a Spatial and Interaction Space Graph Reasoning (SPIN) module which when plugged into a ConvNet performs reasoning over graphs constructed on spatial and interaction spaces projected from the feature maps.
  arXiv  Detail & Related papers  (2021-09-16T03:52:17Z)
• Detecting 32 Pedestrian Attributes for Autonomous Vehicles [103.87351701138554]
  In this paper, we address the problem of jointly detecting pedestrians and recognizing 32 pedestrian attributes. We introduce a Multi-Task Learning (MTL) model relying on a composite field framework, which achieves both goals in an efficient way. We show competitive detection and attribute recognition results, as well as a more stable MTL training.
  arXiv  Detail & Related papers  (2020-12-04T15:10:12Z)
• Open Area Path Finding to Improve Wheelchair Navigation [0.0]
  This paper proposes and implements a novel path finding algorithm for open areas with no network of pathways. The proposed algorithm creates a new graph in the open area, which can consider the obstacles and barriers and calculate the path. The implementations and tests show at least a 76.4% similarity between the proposed algorithm outputs and actual wheelchair users trajectories.
  arXiv  Detail & Related papers  (2020-11-07T21:20:32Z)
• Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting [88.5550074808201]
  We propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN) for traffic forecasting. In this work, we propose a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.
  arXiv  Detail & Related papers  (2020-04-23T03:50:46Z)

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.