Probabilistic road classification in historical maps using synthetic data and deep learning

• URL: http://arxiv.org/abs/2410.02250v1
• Date: Thu, 3 Oct 2024 06:43:09 GMT
• Title: Probabilistic road classification in historical maps using synthetic data and deep learning
• Authors: Dominik J. Mühlematter, Sebastian Schweizer, Chenjing Jiao, Xue Xia, Magnus Heitzler, Lorenz Hurni,
• Abstract summary: We introduce a novel framework that integrates deep learning with geoinformation, computer-based painting, and image processing methodologies. This framework enables the extraction and classification of roads from historical maps using only road geometries. Our method achieved completeness and correctness scores of over 94% and 92%, respectively, for road class 2.
• Score: 3.3755652248305004
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Historical maps are invaluable for analyzing long-term changes in transportation and spatial development, offering a rich source of data for evolutionary studies. However, digitizing and classifying road networks from these maps is often expensive and time-consuming, limiting their widespread use. Recent advancements in deep learning have made automatic road extraction from historical maps feasible, yet these methods typically require large amounts of labeled training data. To address this challenge, we introduce a novel framework that integrates deep learning with geoinformation, computer-based painting, and image processing methodologies. This framework enables the extraction and classification of roads from historical maps using only road geometries without needing road class labels for training. The process begins with training of a binary segmentation model to extract road geometries, followed by morphological operations, skeletonization, vectorization, and filtering algorithms. Synthetic training data is then generated by a painting function that artificially re-paints road segments using predefined symbology for road classes. Using this synthetic data, a deep ensemble is trained to generate pixel-wise probabilities for road classes to mitigate distribution shift. These predictions are then discretized along the extracted road geometries. Subsequently, further processing is employed to classify entire roads, enabling the identification of potential changes in road classes and resulting in a labeled road class dataset. Our method achieved completeness and correctness scores of over 94% and 92%, respectively, for road class 2, the most prevalent class in the two Siegfried Map sheets from Switzerland used for testing. This research offers a powerful tool for urban planning and transportation decision-making by efficiently extracting and classifying roads from historical maps.

Related papers

• 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)
• Semi-supervised Road Updating Network (SRUNet): A Deep Learning Method for Road Updating from Remote Sensing Imagery and Historical Vector Maps [3.350048575501172]
  We propose a road detection method based on semi-supervised learning (SRUNet) specifically for road-updating applications. The proposed SRUNet can provide stable, up-to-date, and reliable prediction results for a wide range of road renewal tasks.
  arXiv  Detail & Related papers  (2023-04-28T16:51:35Z)
• Haul Road Mapping from GPS Traces [0.0]
  This paper investigates the possibility of automatically deriving an accurate representation of the road network using GPS data available from haul trucks operating on site. Based on shortcomings seen in all tested algorithms, a post-processing step is developed which geometrically analyses the created road map for artefacts typical of free-drive areas on mine sites.
  arXiv  Detail & Related papers  (2022-06-27T04:35:06Z)
• 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)
• 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)
• PathBench: A Benchmarking Platform for Classical and Learned Path Planning Algorithms [59.3879573040863]
  Path planning is a key component in mobile robotics. Few attempts have been made to benchmark the algorithms holistically or unify their interface. This paper presents PathBench, a platform for developing, visualizing, training, testing, and benchmarking of existing and future path planning algorithms.
  arXiv  Detail & Related papers  (2021-05-04T21:48:18Z)
• Fusion of neural networks, for LIDAR-based evidential road mapping [3.065376455397363]
  We introduce RoadSeg, a new convolutional architecture that is optimized for road detection in LIDAR scans. RoadSeg is used to classify individual LIDAR points as either belonging to the road, or not. We thus secondly present an evidential road mapping algorithm, that fuses consecutive road detection results.
  arXiv  Detail & Related papers  (2021-02-05T18:14:36Z)
• BoMuDANet: Unsupervised Adaptation for Visual Scene Understanding in Unstructured Driving Environments [54.22535063244038]
  We present an unsupervised adaptation approach for visual scene understanding in unstructured traffic environments. Our method is designed for unstructured real-world scenarios with dense and heterogeneous traffic consisting of cars, trucks, two-and three-wheelers, and pedestrians.
  arXiv  Detail & Related papers  (2020-09-22T08:25:44Z)
• Automatic extraction of road intersection points from USGS historical map series using deep convolutional neural networks [0.0]
  Road intersections data have been used across different geospatial applications and analysis. We employed the standard paradigm of using deep convolutional neural network for object detection task named region-based CNN. Also, compared to the majority of traditional computer vision algorithms RCNN provides more accurate extraction.
  arXiv  Detail & Related papers  (2020-07-14T23:51:15Z)
• Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
  In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA) It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors. We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
  arXiv  Detail & Related papers  (2020-06-24T04:45:14Z)

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.