Toronto Datasets Research Articles

Multi-Feature-Filtering-Based Road Curb Extraction from Unordered Point Clouds

Road curb extraction is a critical component of road environment perception, being essential for calculating road geometry parameters and ensuring the safe navigation of autonomous vehicles. The existing research primarily focuses on extracting curbs from ordered point clouds, which are constrained by their structure of point cloud organization, making it difficult to apply them to unordered point cloud data and making them susceptible to interference from obstacles. To overcome these limitations, a multi-feature-filtering-based method for curb extraction from unordered point clouds is proposed. This method integrates several techniques, including the grid height difference, normal vectors, clustering, an alpha-shape algorithm based on point cloud density, and the MSAC (M-Estimate Sample Consensus) algorithm for multi-frame fitting. The multi-frame fitting approach addresses the limitations of traditional single-frame methods by fitting the curb contour every five frames, ensuring more accurate contour extraction while preserving local curb features. Based on our self-developed dataset and the Toronto dataset, these methods are integrated to create a robust filter capable of accurately identifying curbs in various complex scenarios. Optimal threshold values were determined through sensitivity analysis and applied to enhance curb extraction performance under diverse conditions. Experimental results demonstrate that the proposed method accurately and comprehensively extracts curb points in different road environments, proving its effectiveness and robustness. Specifically, the average curb segmentation precision, recall, and F1 score values across scenarios A, B (intersections), C (straight road), and scenarios D and E (curved roads and ghosting) are 0.9365, 0.782, and 0.8523, respectively.

MapGen-Diff: An End-to-End Remote Sensing Image to Map Generator via Denoising Diffusion Bridge Model

Online maps are of great importance in modern life, especially in commuting, traveling and urban planning. The accessibility of remote sensing (RS) images has contributed to the widespread practice of generating online maps based on RS images. The previous works leverage an idea of domain mapping to achieve end-to-end remote sensing image-to-map translation (RSMT). Although existing methods are effective and efficient for online map generation, generated online maps still suffer from ground features distortion and boundary inaccuracy to a certain extent. Recently, the emergence of diffusion models has signaled a significant advance in high-fidelity image synthesis. Based on rigorous mathematical theories, denoising diffusion models can offer controllable generation in sampling process, which are very suitable for end-to-end RSMT. Therefore, we design a novel end-to-end diffusion model to generate online maps directly from remote sensing images, called MapGen-Diff. We leverage a strategy inspired by Brownian motion to make a trade-off between the diversity and the accuracy of generation process. Meanwhile, an image compression module is proposed to map the raw images into the latent space for capturing more perception features. In order to enhance the geometric accuracy of ground features, a consistency regularization is designed, which allows the model to generate maps with clearer boundaries and colorization. Compared to several state-of-the-art methods, the proposed MapGen-Diff achieves outstanding performance, especially a 5% RMSE and 7% SSIM improvement on Los Angeles and Toronto datasets. The visualization results also demonstrate more accurate local details and higher quality.

Application of Heuristic Combinations in Hyper-Heuristic Framework for Exam Scheduling Problems

Examination Timetabling Problem is one of the optimization and combinatorial problems. It is proved to be a non-deterministic polynomial (NP)-hard problem. On a large scale of data, the examination timetabling problem becomes a complex problem and takes time if it solved manually. Therefore, heuristics exist to provide reasonable enough solutions and meet the constraints of the problem. In this study, a real-world dataset of Examination Timetabling (Toronto dataset) is solved using a Hill-Climbing and Tabu Search algorithm. Different from the approach in the literature, Tabu Search is a meta-heuristic method, but we implemented a Tabu Search within the hyper-heuristic framework. The main objective of this study is to provide a better understanding of the application of Hill-Climbing and Tabu Search in hyper-heuristics to solve timetabling problems. The results of the experiments show that Hill-Climbing and Tabu Search succeeded in automating the timetabling process by reducing the penalty 18-65% from the initial solution. Besides, we tested the algorithms within 10,000-100,000 iterations, and the results were compared with a previous study. Most of the solutions generated from this experiment are better compared to the previous study that also used Tabu Search algorithm.

A new hierarchical method for automatic road centerline extraction in urban areas using LIDAR data

Road detection and road extraction are important and challenging issues in the fields of photogrammetry and remote sensing. Researchers have conducted wide research in this regard based on multispectral images and achieved relatively useful results. Image-data driven methods have some shortcomings such as shadows, eliminating small and long vehicles, geometric distortions, and occlusions. In recent years, in order to overcome the above limitations and complexities, several attempts have been done based on LIDAR data. The present paper proposes an automatic hierarchical road detection and extraction method. The main goal of this research is to increase the level of continuity of the road detection and extraction processes. This method includes the preprocessing of intensity data using a local minima filter, applying the Mean Shift segmentation to the refined intensity data, and finally integrating it with various nDSM-based Products. The proposed method involves not only considering both small and long vehicles as road features but also neglecting some parts of large parking lots based on the nearby neighborhood of parked vehicles as much as possible as non-road features. The next step was the process of road centerline extraction by adopting a Voronoi-diagram based approach and then removing dangle lines in several iterations. The proposed method was applied to the Vaihingen and Toronto datasets (ISPRS). The completeness of the two datasets is 95.85% and 88%, and the correctness of these datasets are 83.68% and 72.2%, respectively. The results were indicative of the great potential of the proposed method for effective road centerline extraction in urban areas.

Topologically Aware Building Rooftop Reconstruction From Airborne Laser Scanning Point Clouds

This paper presents a novel topologically aware 2.5-D building modeling methodology from airborne laser scanning point clouds. The building reconstruction process consists of three main steps: primitive clustering, boundary representation, and geometric modeling. In primitive clustering, we propose an enhanced probability density clustering algorithm to cluster the rooftop primitives by taking into account the topological consistency among primitives. In the second step, we employ a novel Voronoi subgraph-based algorithm to seamlessly trace the primitive boundaries. This algorithm guarantees the production of geometric models without crack defects among adjacent primitives. The primitive boundaries are further divided into multiple linear segments, from which the key points are generated. These key points help to form a hybrid representation of the boundary by combining the projected points with part of the original boundary points. The model representation by the hybrid key points is flexible and well captures the rooftop details to generate lightweight and highly regular building models. Finally, we assemble the primitive boundaries to form the topologically correct entities, which are regarded as the basic units for primitive triangulation. The reconstructed models not only have accurate geometry and correct topology but more importantly have abundant semantics, by which five levels of building models can be generated in real time. The proposed reconstruction method has been comprehensively evaluated on Toronto data set in terms of model compactness, multilevel model representation, and geometric accuracy.

Application of the Hyper-Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes.

The hyper-Poisson distribution can handle both over- and underdispersion, and its generalized linear model formulation allows the dispersion of the distribution to be observation-specific and dependent on model covariates. This study's objective is to examine the potential applicability of a newly proposed generalized linear model framework for the hyper-Poisson distribution in analyzing motor vehicle crash count data. The hyper-Poisson generalized linear model was first fitted to intersection crash data from Toronto, characterized by overdispersion, and then to crash data from railway-highway crossings in Korea, characterized by underdispersion. The results of this study are promising. When fitted to the Toronto data set, the goodness-of-fit measures indicated that the hyper-Poisson model with a variable dispersion parameter provided a statistical fit as good as the traditional negative binomial model. The hyper-Poisson model was also successful in handling the underdispersed data from Korea; the model performed as well as the gamma probability model and the Conway-Maxwell-Poisson model previously developed for the same data set. The advantages of the hyper-Poisson model studied in this article are noteworthy. Unlike the negative binomial model, which has difficulties in handling underdispersed data, the hyper-Poisson model can handle both over- and underdispersed crash data. Although not a major issue for the Conway-Maxwell-Poisson model, the effect of each variable on the expected mean of crashes is easily interpretable in the case of this new model.

Road Centerline Extraction in Complex Urban Scenes From LiDAR Data Based on Multiple Features

Automatic extraction of roads from images of complex urban areas is a very difficult task due to the occlusions and shadows of contextual objects, and complicated road structures. As light detection and ranging (LiDAR) data explicitly contain direct 3-D information of the urban scene and are less affected by occlusions and shadows, they are a good data source for road detection. This paper proposes to use multiple features to detect road centerlines from the remaining ground points after filtering. The main idea of our method is to effectively detect smooth geometric primitives of potential road centerlines and to separate the connected nonroad features (parking lots and bare grounds) from the roads. The method consists of three major steps, i.e., spatial clustering based on multiple features using an adaptive mean shift to detect the center points of roads, stick tensor voting to enhance the salient linear features, and a weighted Hough transform to extract the arc primitives of the road centerlines. In short, we denote our method as Mean shift, Tensor voting, Hough transform (MTH). We evaluated the method using the Vaihingen and Toronto data sets from the International Society for Photogrammetry and Remote Sensing Test Project on Urban Classification and 3-D Building Reconstruction. The completeness of the extracted road network on the Vaihingen data and the Toronto data are 81.7% and 72.3%, respectively, and the correctness are 88.4% and 89.2%, respectively, yielding the best performance compared with template matching and phase-coded disk methods.

Solving the Examination Timetabling Problem in GPUs

The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. The GPU computational capabilities allow the use of very large population sizes, leading to a more thorough exploration of the problem solution space. The GPU implementation, depending on the size of the problem, is up to twenty six times faster than the identical single-threaded CPU implementation of the algorithm. The algorithm is evaluated with the well known Toronto datasets and compares well with the best results found in the bibliography. Moreover, the selection of the encoding of the chromosomes and the tournament selection size as the population grows are examined and optimized. The compressed sparse row format is used for the conflict matrix and was proven essential to the process, since most of the datasets have a small conflict density, which translates into an extremely sparse matrix.

Domain transformation approach to deterministic optimization of examination timetables

In this paper we introduce a new optimization method for the examinations scheduling problem. Rather than attempting direct optimization of assignments of exams to specific time-slots, we perform permutations of slots and reassignments of exams upon the feasible (but not optimal) schedules obtained by the standard graph colouring method with Largest Degree ordering. The proposed optimization methods have been evaluated on the University of Toronto, University of Nottingham and International Timetabling Competition (ITC2007) datasets. It is shown that the proposed method delivers competitive results compared to other constructive methods in the timetabling literature on both the Nottingham and Toronto datasets, and it maintains the same optimization pattern of the solution improvement on the ITC2007 dataset. A deterministic pattern obtained for all benchmark datasets, makes the proposed method more understandable to the users.

On the Economic Value of Probability of Precipitation Forecasts in Canada

Abstract Canadian weather offices have recently begun to receive probability of precipitation information estimated from numerical weather prediction guidance, but this information is not given in the public forecast. This paper uses a well-known procedure to estimate the increased economic value of these probability forecasts over equivalent categorical forecasts for Toronto. The cost-loss matrix given by Epstein (1969) is generalized and applied in economic value calculations of a Probability of Precipitation Amount (POPA) procedure for the same Toronto data set. The increased economic value of these probability forecasts is a substantial fraction of the value of categorical forecasts and appears to justify the expense involved in a marketing/education program necessary for the public to understand these forecasts.