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ROAD is designed to test an autonomous vehicle's ability to detect road events, defined as triplets composed by an active agent, the action(s) it performs and the corresponding scene locations. ROAD comprises videos originally from the Oxford RobotCar Dataset, annotated with bounding boxes showing the location in the image plane of each road event.
This dataset contains images of unusual dangers which can be encountered by a vehicle on the road – animals, rocks, traffic cones and other obstacles. Its purpose is testing autonomous driving perception algorithms in rare but safety-critical circumstances.
KITTI Road is road and lane estimation benchmark that consists of 289 training and 290 test images. It contains three different categories of road scenes: * uu - urban unmarked (98/100) * um - urban marked (95/96) * umm - urban multiple marked lanes (96/94) * urban - combination of the three above Ground truth has been generated by manual annotation of the images and is available for two different road terrain types: road - the road area, i.e, the composition of all lanes, and lane - the ego-lane, i.e., the lane the vehicle is currently driving on (only available for category "um"). Ground truth is provided for training images only. Source: http://www.cvlibs.net/datasets/kitti/eval_road.php Image Source: http://www.cvlibs.net/datasets/kitti/eval_road.php
RoadTracer is a dataset for extraction of road networks from aerial images. It consists of a large corpus of high-resolution satellite imagery and ground truth road network graphs covering the urban core of forty cities across six countries. For each city, the dataset covers a region of approximately 24 sq km around the city center. The satellite imagery is obtained from Google at 60 cm/pixel resolution, and the road network from OSM. The dataset is split into a training set with 25 cities and a test set with 15 other cities. Source: RoadTracer: Automatic Extraction of Road Networks from Aerial Images Image Source: Bastani et al
RoadAnomaly21 is a dataset for anomaly segmentation, the task of identify the image regions containing objects that have never been seen during training. It consists of an evaluation dataset of 100 images with pixel-level annotations. Each image contains at least one anomalous object, e.g. animals or unknown vehicles. The anomalies can appear anywhere in the image and widely differ in size, covering from 0.5% to 40% of the image
This paper introduces the Broad Twitter Corpus (BTC), which is not only significantly bigger, but sampled across different regions, temporal periods, and types of Twitter users. The gold-standard named entity annotations are made by a combination of NLP experts and crowd workers, which enables us to harness crowd recall while maintaining high quality. We also measure the entity drift observed in our dataset (i.e. how entity representation varies over time), and compare to newswire.
The LandCover.ai (Land Cover from Aerial Imagery) dataset is a dataset for automatic mapping of buildings, woodlands, water and roads from aerial images. ### Dataset features * land cover from Poland, Central Europe * three spectral bands - RGB * 33 orthophotos with 25 cm per pixel resolution (~9000x9500 px) * 8 orthophotos with 50 cm per pixel resolution (~4200x4700 px) * total area of 216.27 sq. km ### Dataset format * rasters are three-channel GeoTiffs with EPSG:2180 spatial reference system * masks are single-channel GeoTiffs with EPSG:2180 spatial reference system
The datasets introduced in Chapter 6 of my PhD thesis are below. See the thesis for more details. If you use any of these datasets for research purposes you should use the following citation in any resulting publications: @phdthesis{MnihThesis, author = {Volodymyr Mnih}, title = {Machine Learning for Aerial Image Labeling}, school = {University of Toronto}, year = {2013} }
The first real-world, large-scale Roadside Cooperative Perception Dataset, RCooper, is released to bloom research on roadside cooperative perception for practical applications. More than 50k images and 30k point clouds manually annotated with 3D bounding boxes and trajectories for ten semantic classes are provided.
A dataset for text in driving videos. The dataset is 20 times larger than the existing largest dataset for text in videos. The dataset comprises 1000 video clips of driving without any bias towards text and with annotations for text bounding boxes and transcriptions in every frame. Source: RoadText-1K: Text Detection & Recognition Dataset for Driving Videos
The Road Damage Dataset 2020 (RDD-2020) Secondly is a large-scale heterogeneous dataset comprising 26620 images collected from multiple countries using smartphones. The images are collected from roads in India, Japan and the Czech Republic. Source: Transfer Learning-based Road Damage Detection for Multiple Countries
Text and signs around roads provide crucial information for drivers, vital for safe navigation and situational awareness. Scene text recognition in motion is a challenging problem, while textual cues typically appear for a short time span, and early detection at a distance is necessary. Systems that exploit such information to assist the driver should not only extract and incorporate visual and textual cues from the video stream but also reason over time. To address this issue, we introduce RoadTextVQA, a new dataset for the task of video question answering (VideoQA) in the context of driver assistance. RoadTextVQA consists of 3,222 driving videos collected from multiple countries, annotated with 10,500 questions, all based on text or road signs present in the driving videos. We assess the performance of state-of-the-art video question answering models on our RoadTextVQA dataset, highlighting the significant potential for improvement in this domain and the usefulness of the dataset in advancing research on in-vehicle support systems and text-aware multimodal question answering.
Open Broadcast Media Audio from TV (OpenBMAT) is an open, annotated dataset for the task of music detection that contains over 27 hours of TV broadcast audio from 4 countries distributed over 1647 one-minute long excerpts. It is designed to encompass several essential features for any music detection dataset and is the first one to include annotations about the loudness of music in relation to other simultaneous non-music sounds. OpenBMAT has been cross-annotated by 3 annotators obtaining high inter-annotator agreement percentages, which validates the annotation methodology and ensures the annotations reliability. Source: Open Broadcast Media Audio from TV (OpenBMAT)
This dataset for the semantic segmentation of potholes and cracks on the road surface was assembled from 5 other datasets already publicly available, plus a very small addition of segmented images on our part. To speed up the labeling operations, we started working with depth cameras to try to automate, to some extent, this extremely time-consuming phase. The main dataset is composed of 4340 (image,mask) pairs at different resolutions divided into training/validation/test sets with a proportion of 3340/496/504 images equal to 77/11/12 percent. This is the dataset used in the SHREC2022 competition and it is the dataset that allowed us to train the neural networks for semantic segmentation capable of obtaining the nice images and videos that you have probably already seen. Along the main dataset we also release a set of RGB-D videos consisting of 797 RGB clips and as many clips with their disparity maps, captured with the excellent OAK-D cameras we won for being finalists at the OpenCV AI Competition 2021. In an effort to achieve (semi-)automatic labeling for these clips, we postprocessed the disparity maps using classic CV algorithms and managed to obtain 359 binary mask clips. Obviously these masks are not perfect (they cannot be by definition, otherwise the problem of automatic road damage detection would not exist), but nonetheless we believe they are an excellent starting point to create, for example, new data augmentations (creating potholes on "intact road images" belonging to other standard road datasets) or to be used as textures in the creation of 3D scenes from which to extract large amounts of images/masks for the training of neural networks. You can have a preview of what you will find in these clips by watching this video showing the overlay of images and binary masks: http://deeplearning.ge.imati.cnr.it/genova-5G/video/pothole-mix-videos/pothole-mix-rgb-d-overlay-videos-concat.html Please take a look at the readme file inside the main dataset zipfile to have some more details about the single sub-datasets and their sources.
A special scene-graph for intelligent vehicles. Different to classical data representation, this graph provides not only object proposals but also their pair-wise relationships. By organizing them in a topological graph, these data are explainable, fully-connected, and could be easily processed by GCNs (Graph Convolutional Networks). Source: Road Scene Graph: A Semantic Graph-Based Scene Representation Dataset for Intelligent Vehicles
The Toulouse Road Network dataset describes patches of road maps from the city of Toulouse, represented both as spatial graphs G = (A, X) and as grayscale segmentation images. The TRN dataset contains 111,034 data points (map tiles), of which: 80,357 are in the training set (around 72.4%), 11,679 are in the validation set (around 10.5%), 18,998 are in the test set (around 17.1%). Each tile represents a squared region of side 0.001 degrees of latitude and longitude on the map, which corresponds to a square of around 110 meters. The semantic segmentation of each patch is represented as a 64 × 64 grayscale image. The dataset is generated starting from publicly available data from OpenStreetMap. More details on the dataset characteristic and generation methods are available in our blogpost. Source: https://github.com/davide-belli/toulouse-road-network-dataset Image Source: https://arxiv.org/pdf/1910.14388.pdf
A novel remote sensing dataset for evaluating a geospatial machine learning model's ability to learn long range dependencies and spatial context understanding. We create a task to use as a proxy for this by training models to extract roads which have been broken into disjoint pieces due to tree canopy occluding large portions of the road. The dataset consists of 30,000 RGBN NAIP images and land cover annotations from the Chesapeake Conservacy containing significant amounts of the Tree Canopy Over Road category.
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INDRA is a dataset capturing videos of Indian roads from the pedestrian point-of-view. INDRA contains 104 videos comprising of 26k 1080p frames, each annotated with a binary road crossing safety label and vehicle bounding boxes. Source: A Dataset and Model for Crossing Indian Roads Image Source: https://arxiv.org/pdf/2211.07916v1.pdf
The LWIR DoFP Dataset of Road Scene (LDDRS) is a road detection dataset with 2,113 annotated images. It contains both day and night scenes, with multiple cars and pedestrians per image. Source: https://www.ecva.net/papers/eccv_2020/papers_ECCV/papers/123700460.pdf
The whole UCF-Crime dataset consists of real-world 240 × 320 RGB videos with 13 realistic anomaly types such as explosion, road accident, burglary, etc., and normal examples. The CPD specific requires a change in data distribution. We suppose that explosions and road accidents correspond to such a scenario, while most other types correspond to point anomalies. For example, data, obviously, com from a normal regime before the explosion. After it, we can see fire and smoke, which last for some time. Thus, the first moment when an explosion appears is a change point. Along with a volunteer, the authors carefully labelled chosen anomaly types. Their opinions were averaged. We provide the obtained markup, so other researchers can use it to validate their CPD algorithm for video.
DIT4BEARs Internship Project (at UiT-The Arctic University of Norway) Dataset The dataset contains data of 5 months including weather conditions, friction coefficient, distance traveled, wind speed, surface temperature, air temperature, etc. This dataset was provided by DIT4BEARs for the Smart Road Internship Project at UiT-The Arctic University of Norway It can be used for weather forecasting, road friction forecasting, smart road proposition, minimization of accident rate in Barren Euro-Arctic regions.
The Oxford Road Boundaries is a dataset designed for training and testing machine-learning-based road-boundary detection and inference approaches. The authors have hand-annotated two of the 10 km-long forays from the Oxford Robotcar Dataset and generated from other forays several thousand further examples with semi-annotated road-boundary masks. To boost the number of training samples in this way, the authors used a vision-based localiser to project labels from the annotated datasets to other traversals at different times and weather conditions. As a result, the dataset consists of 62,605 labelled samples, of which 47,639 samples are curated. Each of these samples contains both raw and classified masks for left and right lenses. Our data contains images from a diverse set of scenarios such as straight roads, parked cars, junctions, etc.
This data contains throughput, RTT, power consumption and speed data measured with Starlink during mobility setups.
The StudyAbroadGPT-Dataset is a collection of conversational data focused on university application requirements for various programs, including MBA, MS in Computer Science, Data Science, and Bachelor of Medicine. The dataset includes interactions between humans asking questions about application processes (e.g., "How do I write a strong SOP for MS in Data Science at MIT?") and an assistant providing detailed responses. Covering prestigious institutions such as MIT, Oxford, Cambridge, and Stanford, this dataset serves as a valuable resource for understanding the informational needs of prospective students applying to study abroad. ## Dataset Structure The dataset is organized as a list of JSON objects, where each object represents a single conversation. Each conversation contains an array of turns, structured as follows: - "from": Specifies the speaker, either "human" or "assistant". - "value": Contains the text of the query or response. ### Example json { "conversations": [ {"from": "human", "value": "What documents do I need for applying to MBA?"}, {"from": "assistant", "value": "## Introduction\nTo embark on your MBA journey, it's crucial to gather the necessary documents..."} ] } ## Data Collection This dataset was synthetically generated to simulate realistic conversations about study abroad applications. It is designed to reflect common queries and provide detailed, informative responses related to university application requirements. ## Preprocessing The dataset is provided in its raw form, consisting of unprocessed conversational text. Depending on their specific use case, such as natural language processing (NLP) tasks, users may need to perform additional preprocessing steps like tokenization or stopword removal. ## Usage You can load the dataset using the Hugging Face Datasets library with the following code: python from datasets import load_dataset dataset = load_dataset("millat/StudyAbroadGPT-Dataset") ### Accessing Conversations To access and iterate through the conversations, use this example: python for conversation in dataset["train"]: for turn in conversation["conversations"]: print(f"{turn['from']}: {turn['value']}") ## Examples Here’s a sample conversation excerpt from the dataset: json { "conversations": [ {"from": "human", "value": "What are the GMAT requirements for Oxford?"}, {"from": "assistant", "value": "## Introduction\nOxford University, one of the world's most prestigious institutions, requires..."} ] } ## Use Cases - Training Conversational Agents: Build chatbots to assist with university application queries. - Analyzing Trends: Study application requirements across different programs and institutions. - NLP Development: Create natural language understanding models tailored to educational domains. ## License This dataset is licensed under the MIT License. ## Citation If you use this dataset in your research, please cite it as follows: bibtex @misc{StudyAbroadGPT-Dataset, author = {MD MILLAT HOSEN}, title = {StudyAbroadGPT-Dataset}, year = {2025}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/datasets/millat/StudyAbroadGPT-Dataset}} }
The U.S. Broadband Coverage data set is a publicly available dataset that reports broadband coverage percentages at a zip code-level. The authors have used differential privacy to guarantee that the privacy of individual households is preserved. The data set also contains error ranges estimates, providing information on the expected error introduced by differential privacy per zip code.
Connectivity is a main driver for the ongoing megatrend of automated mobility: future Cooperative Intelligent Transport Systems (C-ITS) will connect road vehicles, traffic signals, roadside infrastructure, and even vulnerable road users, sharing data and compute for safer, more efficient, and more comfortable mobility. In terms of communication technology for realizing such vehicle-to-everything (V2X) communication, the WLAN-based peer-to-peer approach (IEEE 802.11p, ITS-G5 in Europe) competes with C-V2X based on cellular technologies (4G and beyond). Irrespective of the underlying communication standard, common message interfaces are crucial for a common understanding between vehicles, especially from different manufacturers. Targeting this issue, the European Telecommunications Standards Institute (ETSI) has been standardizing V2X message formats such as the Cooperative Awareness Message (CAM). In this work, we present V2AIX, a multi-modal real-world dataset of ETSI ITS messages gathered in public road traffic, the first of its kind. Collected in measurement drives and with stationary infrastructure, we have recorded more than 230000 V2X messages from more than 1800 vehicles and roadside units in public road traffic. Alongside a first analysis of the dataset, we present a way of integrating ETSI ITS V2X messages into the Robot Operating System (ROS). This enables researchers to not only thoroughly analyze real-world V2X data, but to also study and implement standardized V2X messages in ROS-based automated driving applications.
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Description: <a href="https://gts.ai/dataset-download/lego-road-image-for-drone/" target="_blank">👉 Download the dataset here</a> This folder contains three distinct types of images representing various movements: forward, left, and right. Our task involves reading these images into a digital array, which will enable us to analyze and predict the next action of the drone. Download Dataset Here’s a detailed breakdown of the process: Image Categorization: Forward Movement: These images depict scenarios where the drone needs to move straight ahead. Left Movement: These images illustrate situations where the drone needs to turn or move to the left. Right Movement: These images show instances where the drone should turn or move to the right. Image Preprocessing: Reading Images: Use an appropriate library to read and load the images from the folder into a digital format. Resizing and Normalizing: Standardize the size of the images and normalize the pixel values to ensure consistent input for the analysis model. Labeling: Assign labels to each image based on its movement category (forward, left, right). Data Storage: Store the preprocessed images in a digital array, with each image associated with its respective label. This array will serve as the dataset for further analysis. Analysis and Prediction: Feature Extraction: Extract relevant features from the images, such as edges, shapes, and textures, which are indicative of the movement type. Model Training: Use machine learning algorithms to train a predictive model on the labeled dataset. This model will learn to recognize patterns corresponding to each movement type. Prediction: Utilize the trained model to analyze new images and predict the drone’s next action based on the detected patterns. Applications: Real-Time Navigation: Implement the predictive model in the drone’s navigation system to enable real-time decision-making based on visual inputs. Autonomous Operations: Enhance the drone’s autonomous capabilities by integrating the model with other sensors and control systems for seamless movement. This dataset is sourced from Kaggle.
One of the most important aspects of robot scene understanding is semantic segmentation of external environments. Urban environment semantic segmentation has been extensively investigated by researchers and many real-world and synthetic datasets have been utilised to develop highly accurate segmentation results. However, the number of off-road datasets available for robot navigation research remains limited. To address this, we introduce a novel framework [1] to generate varied photorealistic synthetic off-road datasets capable of supporting multiple sensor modalities. Using this approach, a synthetic multimodal dataset for off-road ground robot navigation in typical Western Australian outback conditions has been created. The robot simulations for synthetic dataset generation were conducted using the NVIDIA Isaac Sim robotics simulator platform and the camera, LiDAR, and IMU sensor data was collected using four synthetic off-road environment scenarios. After revising some of the semantic classes introduced in our DICTA 2024 conference paper, we published our OUTBACK dataset, which consists of a total of 7,817 image and LiDAR frames with respective annotations covering 16 semantic classes.
The preview of the road surface states is essential for improving the safety and the ride comfort of autonomous vehicles. This dataset consists of 1 million (240 x 360 pixels) road surface images captured under a wide range of road and weather conditions in China. The original pictures are acquired with a vehicle-mounted camera and then the patches containing only the road surface area are cropped. The images are classified into 27 categories, containing both the friction level, material, and unevenness properties. The dataset is divided into train-set(~960k samples), validation-set(~20k samples), test-set(~50k samples) . This large-scale dataset is useful for developing vision-based road sensing modules to improve the performance of the driving assistance systems. More details, please visit Github: https://github.com/ztsrxh/RSCD-Road_Surface_Classification_Dataset
Description: <a href="https://gts.ai/dataset-download/road-anomaly-dataset/" target="_blank">👉 Download the dataset here</a> The Road Anomaly Dataset (RAD) is meticulously designed to tackle the urgent need for real-time detection and response to dangerous and potentially life-threatening road anomalies through surveillance footage. This dataset captures a wide spectrum of hazardous road-related activities, including but not limited to vehicular accidents, car fires, violent altercations, and armed thefts. The RAD dataset serves as a vital resource for improving public safety by equipping machine learning models to detect such anomalies with precision and speed, ensuring a faster and more effective emergency response. Motivation With the proliferation of Closed-Circuit Television (CCTV) cameras in urban and rural areas, there is an increasing demand for intelligent surveillance systems capable of detecting abnormal activities in real-world environments. Detecting road anomalies in real-time is particularly crucial, as it can significantly reduce response times to dangerous situations, preventing injuries, fatalities, and property damage. Traditional surveillance systems, however, often fail to recognize these anomalies quickly enough, leading to delayed emergency responses. RAD aims to address this challenge by providing a comprehensive dataset that can train advanced machine learning models to identify, classify, and respond to road anomalies effectively. Download Dataset Dataset Description The RAD dataset comprises an extensive collection of high-quality images and videos, systematically annotated to reflect a variety of road anomalies. The dataset focuses on four primary categories, each representing a critical risk to human safety and property: Accidents: Includes footage of car crashes, multi-vehicle collisions, and other traffic-related incidents that pose immediate dangers. Car Fires: Captures instances of vehicles engulfed in flames, emphasizing the importance of quick detection to prevent further casualties and property damage. Fighting: Contains videos of physical altercations occurring on streets or roads, which could escalate into severe violence or endanger bystanders. Armed Robberies (Snatching at Gunpoint): Features videos of robberies and thefts involving firearms, where immediate response is essential for protecting lives and reducing the impact of criminal activities. Each category in the dataset is annotated with detailed labels to ensure the accurate identification and classification of these anomalies. The annotations include temporal and spatial metadata, allowing for precise localization of the anomalies within the footage. This dataset is primarily aimed at training and testing machine learning models, particularly those using Convolutional Neural Networks (CNNs) and other deep learning architectures for high-accuracy anomaly detection. Additional Categories and Data Enrichment To further enhance the dataset's utility, additional categories of road anomalies are being considered, including: Illegal Parking: Captures instances of vehicles parked in unauthorized or hazardous locations, potentially obstructing traffic or emergency services. Obstructions on Roadways: Identifies obstacles such as fallen trees, debris, or stalled vehicles, which can disrupt traffic flow and pose risks to motorists. Pedestrian Accidents: Includes footage of collisions involving pedestrians, which are often more severe due to the vulnerability of those involved. Moreover, the RAD dataset has been enriched with contextually relevant metadata, such as environmental conditions (e.g., weather, lighting) and road types (e.g., urban streets, highways), further refining the data for more nuanced training applications. Applications The RAD dataset is ideal for developers and researchers working in public safety, smart cities, and autonomous vehicle technologies. Its primary applications include: Surveillance Systems: To improve the accuracy and responsiveness of Al-powered surveillance systems by detecting hazardous road activities. Traffic Management: To help authorities monitor and manage traffic flow more efficiently, identifying potential threats in real-time. Autonomous Vehicles: For the training of autonomous vehicle systems, equipping them to recognize and respond to road anomalies during navigation. This dataset is sourced from Kaggle.
Description: <a href="https://gts.ai/dataset-download/road-lane-instance-segmentation/" target="_blank">👉 Download the dataset here</a> The Road Lane Instance Segmentation dataset consists of a collection of images captured from vehicle dashcams, providing detailed segmentation labels for various types of road lane markings. This dataset is specifically designed to support the development of advanced driver assistance systems (ADAS), autonomous driving research, and lane detection algorithms. It provides high-quality, annotated data for training machine learning models capable of accurately recognizing and distinguishing between different types of lane markings on diverse road surfaces. Dataset Composition: Each image in the dataset contains comprehensive instance segmentation labels for different road lane markings, allowing precise identification of lane boundaries and other road-related lines. The annotations are pixel-perfect, ensuring high accuracy for tasks like semantic segmentation, lane boundary detection, and instance-level classification. Download Dataset Classes: The dataset categorizes road lane markings into the following classes: Divider Line: Solid lines used to separate traffic moving in opposite directions, typically found in the center of the road. Dotted Line: Broken lines used for lane division or indicating areas where lane changes are permitted. Double Line: A pair of solid lines, often indicating areas where overtaking is prohibited. Random Line: Miscellaneous markings such as temporary construction lines, which may not follow standard lane marking patterns. Road Sign Line: Specialized markings indicating directions, such as turn arrows or crosswalks. Solid Line: Continuous lines used to define the edge of a lane or boundary where lane changes are not allowed. Data Annotations: Instance Segmentation: Each lane marking is segmented with a unique instance mask, allowing algorithms to distinguish between individual lanes and classify them accordingly. Bounding Boxes: In addition to instance segmentation masks, the dataset also provides bounding box annotations for lane markings. Lane Curvature and Width: Some annotations include additional metadata relate to the curvature and width of lanes, useful for training models on highways, city streets, and rural roads. Applications: This dataset is suitable for a wide range of computer vision tasks, including: Autonomous Driving: The dataset is ideal for developing systems capable of lane detection and navigation in autonomous vehicles, enabling safe lane-keeping and transition maneuvers. Advanced Driver Assistance Systems (ADAS): Useful for real-time lane detection and warning systems in smart vehicles. Road Condition Monitoring: Can be use by transportation authorities to identify worn-out or missing lane markings that need maintenance. Traffic Management Systems: Enables smart city infrastructure by integrating road lane data for efficient traffic flow management. Dataset Features: Diverse Road Conditions: The images cover a wide variety of road conditions, including urban, suburban, and highway environments, with varying lighting conditions (day/night) and weather situations (rain, snow, fog). Resolution and Quality: All images are high-resolution, ensuring clear visualization of lane details, even in complex or challenging scenes. Scalable and Customizable: The dataset is expandable with additional road classes and can be customize according to specific user requirements or research goals. Platform Compatibility: The dataset can be seamlessly integrate into machine learning platforms like TensorFlow, PyTorch, and others, with annotations provide in commonly use formats such as COCO and Pascal VOC. This dataset is sourced from Kaggle.
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