Sidewalk Measurements from Satellite Images: Preliminary Findings

• URL: http://arxiv.org/abs/2112.06120v1
• Date: Sun, 12 Dec 2021 02:22:46 GMT
• Title: Sidewalk Measurements from Satellite Images: Preliminary Findings
• Authors: Maryam Hosseini, Iago B. Araujo, Hamed Yazdanpanah, Eric K. Tokuda, Fabio Miranda, Claudio T. Silva, Roberto M. Cesar Jr
• Abstract summary: We train a computer vision model to detect sidewalks, roads, and buildings from remote-sensing imagery. We apply shape analysis techniques to study different attributes of the extracted sidewalks.
• Score: 10.870041943009722
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large-scale analysis of pedestrian infrastructures, particularly sidewalks, is critical to human-centric urban planning and design. Benefiting from the rich data set of planimetric features and high-resolution orthoimages provided through the New York City Open Data portal, we train a computer vision model to detect sidewalks, roads, and buildings from remote-sensing imagery and achieve 83% mIoU over held-out test set. We apply shape analysis techniques to study different attributes of the extracted sidewalks. More specifically, we do a tile-wise analysis of the width, angle, and curvature of sidewalks, which aside from their general impacts on walkability and accessibility of urban areas, are known to have significant roles in the mobility of wheelchair users. The preliminary results are promising, glimpsing the potential of the proposed approach to be adopted in different cities, enabling researchers and practitioners to have a more vivid picture of the pedestrian realm.

Related papers

• StreetSurfGS: Scalable Urban Street Surface Reconstruction with Planar-based Gaussian Splatting [85.67616000086232]
  StreetSurfGS is first method to employ Gaussian Splatting specifically tailored for scalable urban street scene surface reconstruction. StreetSurfGS utilizes a planar-based octree representation and segmented training to reduce memory costs, accommodate unique camera characteristics, and ensure scalability. To address sparse views and multi-scale challenges, we use a dual-step matching strategy that leverages adjacent and long-term information.
  arXiv  Detail & Related papers  (2024-10-06T04:21:59Z)
• MetaUrban: An Embodied AI Simulation Platform for Urban Micromobility [52.0930915607703]
  Recent advances in Robotics and Embodied AI make public urban spaces no longer exclusive to humans. Micromobility enabled by AI for short-distance travel in public urban spaces plays a crucial component in the future transportation system. We present MetaUrban, a compositional simulation platform for the AI-driven urban micromobility research.
  arXiv  Detail & Related papers  (2024-07-11T17:56:49Z)
• Robust Pedestrian Detection via Constructing Versatile Pedestrian Knowledge Bank [51.66174565170112]
  We propose a novel approach to construct versatile pedestrian knowledge bank. We extract pedestrian knowledge from a large-scale pretrained model. We then curate them by quantizing most representative features and guiding them to be distinguishable from background scenes.
  arXiv  Detail & Related papers  (2024-04-30T07:01:05Z)
• A shape-based heuristic for the detection of urban block artifacts in street networks [0.0]
  Street networks are ubiquitous components of cities, guiding their development and enabling movement from place to place. Their graph representation is often designed primarily for transportation purposes. This representation is less suitable for other use cases where transportation networks need to be simplified.
  arXiv  Detail & Related papers  (2023-09-01T13:11:35Z)
• Pedestrian Crossing Action Recognition and Trajectory Prediction with 3D Human Keypoints [25.550524178542833]
  We propose a novel multi-task learning framework for pedestrian crossing action recognition and trajectory prediction. We use 3D human keypoints extracted from raw sensor data to capture rich information on human pose and activity. We show that our approach achieves state-of-the-art performance on a wide range of evaluation metrics.
  arXiv  Detail & Related papers  (2023-06-01T18:27:48Z)
• UrbanBIS: a Large-scale Benchmark for Fine-grained Urban Building Instance Segmentation [50.52615875873055]
  UrbanBIS comprises six real urban scenes, with 2.5 billion points, covering a vast area of 10.78 square kilometers. UrbanBIS provides semantic-level annotations on a rich set of urban objects, including buildings, vehicles, vegetation, roads, and bridges. UrbanBIS is the first 3D dataset that introduces fine-grained building sub-categories.
  arXiv  Detail & Related papers  (2023-05-04T08:01:38Z)
• Towards Mapping and Assessing Sidewalk Accessibility Across Sociocultural and Geographic Contexts [10.55901957962347]
  There is a lack of high-quality datasets and corresponding analyses on sidewalk existence and condition. Our work explores a twofold vision: first, to develop scalable mechanisms to locate and assess sidewalks in cities across the world, and second, to use this data to support new urban analyses and mobility tools.
  arXiv  Detail & Related papers  (2022-07-27T16:38:01Z)
• Towards Global-Scale Crowd+AI Techniques to Map and Assess Sidewalks for People with Disabilities [10.096568479976725]
  There is a lack of data on the location, condition, and accessibility of sidewalks across the world. In this paper, we describe initial work in semi-automatically building a sidewalk network topology from satellite imagery. We close with a call to create a database of labeled satellite and streetscape scenes for sidewalks and sidewalk accessibility issues along with standardized benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T01:05:08Z)
• CitySurfaces: City-Scale Semantic Segmentation of Sidewalk Materials [6.573006589628846]
  Most cities lack a spatial catalog of their surfaces due to the cost-prohibitive and time-consuming nature of data collection. Recent advancements in computer vision, together with the availability of street-level images, provide new opportunities for cities to extract large-scale built environment data. We propose CitySurfaces, an active learning-based framework that leverages computer vision techniques for classifying sidewalk materials.
  arXiv  Detail & Related papers  (2022-01-06T21:58:37Z)
• Learning Perceptual Locomotion on Uneven Terrains using Sparse Visual Observations [75.60524561611008]
  This work aims to exploit the use of sparse visual observations to achieve perceptual locomotion over a range of commonly seen bumps, ramps, and stairs in human-centred environments. We first formulate the selection of minimal visual input that can represent the uneven surfaces of interest, and propose a learning framework that integrates such exteroceptive and proprioceptive data. We validate the learned policy in tasks that require omnidirectional walking over flat ground and forward locomotion over terrains with obstacles, showing a high success rate.
  arXiv  Detail & Related papers  (2021-09-28T20:25:10Z)
• End-to-End Deep Structured Models for Drawing Crosswalks [98.9901717499058]
  We project both inputs onto the ground surface to produce a top down view of the scene. We then leverage convolutional neural networks to extract semantic cues about the location of the crosswalks. Experiments over crosswalks in a large city area show that 96.6% automation can be achieved.
  arXiv  Detail & Related papers  (2020-12-21T18:59:08Z)

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.