existing multimodal data augmentation is only a brief reference to single-modal work

If you use this dataset, please cite this paper: Puertas, E.; De-Las-Heras, G.; Sánchez-Soriano, J.; Fernández-Andrés, J. Dataset: Variable Message Signal Annotated Images for Object Detection. Data 2022, 7, 41. https://doi.org/10.3390/data7040041

This dataset consists of Spanish road images taken from inside a vehicle, as well as annotations in XML files in PASCAL VOC format that indicate the location of Variable Message Signals within them. Also, a CSV file is attached with information regarding the geographic position, the folder where the image is located, and the text in Spanish. This can be used to train supervised learning computer vision algorithms, such as convolutional neural networks. Throughout this work, the process followed to obtain the dataset, image acquisition, and labeling, and its specifications are detailed. The dataset is constituted of 1216 instances, 888 positives, and 328 negatives, in 1152 jpg images with a resolution of 1280x720 pixels. These are divided into 576 real images and 576 images created from the data-augmentation technique. The purpose of this dataset is to help in road computer vision research since there is not one specifically for VMSs.

The folder structure of the dataset is as follows:

• vms_dataset/
  • data.csv
  • real_images/
    • imgs/
    • annotations/
  • data-augmentation/
    • imgs/
    • annotations/

In which:

• data.csv: Each row contains the following information separated by commas (,): image_name, x_min, y_min, x_max, y_max, class_name, lat, long, folder, text.
• real_images: Images extracted directly from the videos.
• data-augmentation: Images created using data-augmentation
• imgs: Image files in .jpg format.
• annotations: Annotation files in .xml format.

The Artificial Intelligence (AI) Synthetic Data Service market is experiencing rapid growth, driven by the increasing need for high-quality data to train and validate AI models, especially in sectors with data scarcity or privacy concerns. The market, estimated at $2 billion in 2025, is projected to expand significantly over the next decade, achieving a Compound Annual Growth Rate (CAGR) of approximately 30% from 2025 to 2033. This robust growth is fueled by several key factors: the escalating adoption of AI across various industries, the rising demand for robust and unbiased AI models, and the growing awareness of data privacy regulations like GDPR, which restrict the use of real-world data. Furthermore, advancements in synthetic data generation techniques, enabling the creation of more realistic and diverse datasets, are accelerating market expansion. Major players like Synthesis, Datagen, Rendered, Parallel Domain, Anyverse, and Cognata are actively shaping the market landscape through innovative solutions and strategic partnerships. The market is segmented by data type (image, text, time-series, etc.), application (autonomous driving, healthcare, finance, etc.), and deployment model (cloud, on-premise). Despite the significant growth potential, certain restraints exist. The high cost of developing and deploying synthetic data generation solutions can be a barrier to entry for smaller companies. Additionally, ensuring the quality and realism of synthetic data remains a crucial challenge, requiring continuous improvement in algorithms and validation techniques. Overcoming these limitations and fostering wider adoption will be key to unlocking the full potential of the AI Synthetic Data Service market. The historical period (2019-2024) likely saw a lower CAGR due to initial market development and technology maturation, before experiencing the accelerated growth projected for the forecast period (2025-2033). Future growth will heavily depend on further technological advancements, decreasing costs, and increasing industry awareness of the benefits of synthetic data.

Object detection in challenging environments, such as low-light, cluttered, or dynamic conditions, remains a critical issue in computer vision. Deep Convolutional Neural Networks (DCNNs) have emerged as powerful tools for addressing these challenges due to their ability to learn hierarchical feature representations. This paper explores the optimization of object detection in such environments by leveraging advanced DCNN architectures, data augmentation techniques, and domain-specific pre-training. We propose an enhanced detection framework that integrates multi-scale feature extraction, transfer learning, and regularization methods to improve robustness against noise, occlusion, and lighting variations. Experimental results demonstrate significant improvements in detection accuracy across various challenging datasets, outperforming traditional methods. This study highlights the potential of DCNNs in real-world applications, such as autonomous driving, surveillance, and robotics, where object detection in difficult conditions is crucial.

This publication presents a dataset of Spanish roundabouts aerial images taken from an UAV, along with annotations in PASCAL VOC XML files that indicate the position of vehicles within them. Additionally, a CSV file is attached containing information related to the location and characteristics of the captured roundabouts. This work details the process followed to obtain them: image capture, processing and labeling. The dataset consists of 985,260 total instances: 947,400 cars, 19,596 cycles, 2,262 trucks, 7,008 buses and 2,208 empty roundabouts, in 61,896 1920x1080px JPG images. These are divided into 15,474 extracted images from 8 roundabouts with different traffic flow and 46,422 images created using data augmentation techniques. The purpose of this dataset is to help research on computer vision on the road, as such labeled images are not abundant. It can be used to train supervised learning models, such as convolutional neural networks, which are very popular in object detection.

Roundabout (scenes)	Frames	Car	Truck	Cycle	Bus	Empty
1 (00001)	1,996	34,558	0	4229	0	0
2 (00002)	514	743	0	0	0	157
3 (00003-00017)	1,795	4822	58	0	0	0
4 (00018-00033)	1,027	6615	0	0	0	0
5 (00034-00049)	1,261	2248	0	550	0	81
6 (00050-00052)	5,501	180,342	1420	120	1376	0
7 (00053)	2,036	5,789	562	0	226	92
8 (00054)	1,344	1,733	222	0	150	222
Total	15,474	236,850	2,262	4,899	1,752	552
Data augmentation	x4	x4	x4	x4	x4	x4
Total	61,896	947,400	9048	19,596	7,008	2,208

The global Satellite Signal Augmentation Systems (SSAS) market, valued at $1610 million in 2025, is projected to experience robust growth, driven by a Compound Annual Growth Rate (CAGR) of 4.5% from 2025 to 2033. This expansion is fueled by several key factors. The increasing demand for precise positioning and navigation across various sectors, including aerospace, ground transportation, agriculture, and maritime, is a primary driver. Advanced applications such as precision farming, autonomous vehicles, and enhanced air traffic management are significantly contributing to market growth. Furthermore, the rising adoption of both Regional Augmentation Systems (RAS) and Global Augmentation Systems (GAS) reflects the growing need for reliable and accurate positioning data in diverse geographical locations and operational contexts. Technological advancements leading to improved accuracy, reliability, and cost-effectiveness of SSAS are also propelling market expansion. The incorporation of SSAS into existing infrastructure, such as existing GPS networks and communication systems, presents opportunities for streamlined integration and operational efficiencies. However, challenges remain. The high initial investment cost associated with deploying and maintaining SSAS infrastructure can be a barrier to entry for smaller players. Additionally, regulatory hurdles and interoperability concerns across different systems might hinder widespread adoption. Despite these restraints, the long-term outlook for the SSAS market remains positive, with significant potential for growth driven by continued technological innovation and the increasing reliance on accurate positioning data in a wide range of applications. The market's geographic distribution is expected to remain diverse, with North America and Europe maintaining strong market share due to established infrastructure and technological advancements. However, regions like Asia-Pacific are poised for significant growth, driven by increasing infrastructure development and economic expansion.

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SELF-DRIVING CAR USING UDACITY’S CAR SIMULATOR ENVIRONMENT AND TRAINED BY DEEP NEURAL NETWORKS COMPLETE GUIDE

Table of Contents

Introduction

• Problem Definition
• Solution Approach
• Technologies Used
• Convolutional Neural Networks (CNN)
• Time-Distributed Layers

Udacity Simulator and Dataset

The Training Process

Augmentation and image pre-processing

Experimental configurations

Network architectures

Results

• Value loss or Accuracy
• Why We Use ELU Over RELU

The Connection Part

Files

Overview

References

Introduction

Self-drivi cars has become a trending subject with a significant improvement in the technologies in the last decade. The project purpose is to train a neural network to drive an autonomous car agent on the tracks of Udacity’s Car Simulator environment. Udacity has released the simulator as an open source software and enthusiasts have hosted a competition (challenge) to teach a car how to drive using only camera images and deep learning. Driving a car in an autonomous manner requires learning to control steering angle, throttle and brakes. Behavioral cloning technique is used to mimic human driving behavior in the training mode on the track. That means a dataset is generated in the simulator by user driven car in training mode, and the deep neural network model then drives the car in autonomous mode. Ultimately, the car was able to run on Track 1 generalizing well. The project aims at reaching the same accuracy on real time data in the future.https://user-images.githubusercontent.com/91852182/147298831-225740f9-6903-4570-8336-0c9f16676456.png" alt="6">

Problem Definition

Udacity released an open source simulator for self-driving cars to depict a real-time environment. The challenge is to mimic the driving behavior of a human on the simulator with the help of a model trained by deep neural networks. The concept is called Behavioral Cloning, to mimic how a human drives. The simulator contains two tracks and two modes, namely, training mode and autonomous mode. The dataset is generated from the simulator by the user, driving the car in training mode. This dataset is also known as the “good” driving data. This is followed by testing on the track, seeing how the deep learning model performs after being trained by that user data.

Solution Approach

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The problem is solved in the following steps:

• The simulator can be used to collect data by driving the car in the training mode using a joystick or keyboard, providing the so called “good-driving” behavior input data in form of a driving_log (.csv file) and a set of images. The simulator acts as a server and pipes these images and data log to the Python client.
• The client (Python program) is the machine learning model built using Deep Neural Networks. These models are developed on Keras (a high-level API over Tensorflow). Keras provides sequential models to build a linear stack of network layers. Such models are used in the project to train over the datasets as the second step. Detailed description of CNN models experimented and used can be referred to in the chapter on network architectures.
• Once the model is trained, it provides steering angles and throttle to drive in an autonomous mode to the server (simulator).
• These modules, or inputs, are piped back to the server and are used to drive the car autonomously in the simulator and keep it from falling off the track.

Technologies Used

Technologies that are used in the implementation of this project and the motivation behind using these are described in this section.

TensorFlow: This an open-source library for dataflow programming. It is widely used for machine learning applications. It is also used as both a math library and for large computation. For this project Keras, a high-level API that uses TensorFlow as the backend is used. Keras facilitate in building the models easily as it more user friendly.

Different libraries are available in Python that helps in machine learning projects. Several of those libraries have improved the performance of this project. Few of them are mentioned in this section. First, “Numpy” that provides with high-level math function collection to support multi-dimensional metrices and arrays. This is used for faster computations over the weights (gradients) in neural networks. Second, “scikit-learn” is a machine learning library for Python which features different algorithms and Machine Learning function packages. Another one is OpenCV (Open Source Computer Vision Library) which is designed for computational efficiency with focus on real-time applications. In this project, OpenCV is used for image preprocessing and augmentation techniques.

The project makes use of Conda Environment which is an open source distribution for Python which simplifies package management and deployment. It is best for large scale data processing. The machine on which this project was built, is a personal computer.

Convolutional Neural Networks (CNN)

CNN is a type of feed-forward neural network computing system that can be used to learn from input data. Learning is accomplished by determining a set of weights or filter values that allow the network to model the behavior according to the training data. The desired output and the output generated by CNN initialized with random weights will be different. This difference (generated error) is backpropagated through the layers of CNN to adjust the weights of the neurons, which in turn reduces the error and allows us produce output closer to the desired one.

CNN is good at capturing hierarchical and spatial data from images. It utilizes filters that look at regions of an input image with a defined window size and map it to some output. It then slides the window by some defined stride to other regions, covering the whole image. Each convolution filter layer thus captures the properties of this input image hierarchically in a series of subsequent layers, capturing the details like lines in image, then shapes, then whole objects in later layers. CNN can be a good fit to feed the images of a dataset and classify them into their respective classes.

Time-Distributed Layers

Another type of layers sometimes used in deep learning networks is a Time- distributed layer. Time-Distributed layers are provided in Keras as wrapper layers. Every temporal slice of an input is applied with this wrapper layer. The requirement for input is that to be at least three-dimensional, first index can be considered as temporal dimension. These Time-Distributed can be applied to a dense layer to each of the timesteps, independently or even used with Convolutional Layers. The way they can be written is also simple in Keras as shown in Figure 1 and Figure 2.

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Fig. 1: TimeDistributed Dense layer

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Fig. 2: TimeDistributed Convolution layer

Udacity Simulator and Dataset

We will first download the simulator to start our behavioural training process. Udacity has built a simulator for self-driving cars and made it open source for the enthusiasts, so they can work on something close to a real-time environment. It is built on Unity, the video game development platform. The simulator consists of a configurable resolution and controls setting and is very user friendly. The graphics and input configurations can be changed according to user preference and machine configuration as shown in Figure 3. The user pushes the “Play!” button to enter the simulator user interface. You can enter the Controls tab to explore the keyboard controls, quite similar to a racing game which can be seen in Figure 4.

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Fig. 3: Configuration screen

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Fig. 4: Controls Configuration

The first actual screen of the simulator can be seen in Figure 5 and its components are discussed below. The simulator involves two tracks. One of them can be considered as simple and another one as complex that can be evident in the screenshots attached in Figure 6 and Figure 7. The word “simple” here just means that it has fewer curvy tracks and is easier to drive on, refer Figure 6. The “complex” track has steep elevations, sharp turns, shadowed environment, and is tough to drive on, even by a user doing it manually. Please refer Figure 6. There are two modes for driving the car in the simulator: (1) Training mode and (2) Autonomous mode. The training mode gives you the option of recording your run and capturing the training dataset. The small red sign at the top right of the screen in the Figure 6 and 7 depicts the car is being driven in training mode. The autonomous mode can be used to test the models to see if it can drive on the track without human intervention. Also, if you try to press the controls to get the car back on track, it will immediately notify you that it shifted to manual controls. The mode screenshot can be as seen in Figure 8. Once we have mastered how the car driven controls in simulator using keyboard keys, then we get started with record button to collect data. We will save the data from it in a specified folder as you can see

This document contains the final project report for the SAFER-SIM project titled “Physics-Based Sensor Models for Virtual Simulation of Connected and Autonomous Vehicles.” The report includes discussion of sensors models for simulation autonomous vehicles, and overviews the simulation framework developed in accordance with the project. The framework, called Chrono::Sensor is developed as a module alongside Project Chrono to augment the open-source multi-physics engine with the capability to simulation sensor data from within its virtual environment. Chrono::Sensor provides support for the modeling and simulation of camera, lidar, GPS, and IMU. It also provides a framework to implement custom sensors that can leverage existing sensor generation functionality. Chrono::Sensor generates data using ray-tracing algorithms that mimic the data acquisition process of cameras and lidars. In addition to data collection, the sensors support further data augmentation including the addition of sensor-specific noise. Results from each sensor implementation are included as part of the corresponding sensor discussion, with the report concluding with two demonstrations showing the use of Chrono::Sensor, in combination with Chrono, to simulate autonomous vehicles. The dataset for this project is a C++ library for multi-physics simulation. It is available at the following URL: https://github.com/projectchrono/chrono

The automotive high-precision positioning market is experiencing robust growth, driven by the increasing demand for advanced driver-assistance systems (ADAS) and autonomous vehicles. The market's precision requirements are pushing technological advancements in GNSS augmentation techniques and sensor fusion, leading to more reliable and accurate positioning data even in challenging environments. This demand is further fueled by stricter safety regulations and the ongoing development of Level 3, 4, and 5 autonomous vehicles, which rely heavily on precise localization for safe and efficient operation. Considering a plausible market size of $2.5 billion in 2025 and a conservative Compound Annual Growth Rate (CAGR) of 18% based on industry trends, the market is projected to reach approximately $7.8 billion by 2033. This growth trajectory indicates significant investment opportunities for companies involved in developing and deploying high-precision positioning technologies for the automotive sector. Key players like Hi-Target, BroadGNSS, and Septentrio are at the forefront of this expansion, continually innovating to meet the evolving needs of the industry. Factors such as increasing infrastructure investments in high-precision positioning systems, including RTK networks and satellite-based augmentation systems, are further contributing to market expansion. However, challenges remain, including the high cost of implementation, cybersecurity concerns related to connected vehicle systems, and the need for reliable infrastructure coverage, especially in remote areas. The market segmentation is likely to diversify as specific solutions tailored for various applications within the automotive sector, including passenger cars, commercial vehicles, and robotics, continue to emerge. This dynamic landscape demands constant adaptation and innovation from companies in the sector to maintain a competitive edge.

Synthetic Training Data Market Outlook

According to our latest research, the global synthetic training data market size in 2024 is valued at USD 1.45 billion, demonstrating robust momentum as organizations increasingly adopt artificial intelligence and machine learning solutions. The market is projected to grow at a remarkable CAGR of 38.7% from 2025 to 2033, reaching an estimated USD 22.46 billion by 2033. This exponential growth is primarily driven by the rising demand for high-quality, diverse, and privacy-compliant datasets that fuel advanced AI models, as well as the escalating need for scalable data solutions across various industries.

One of the primary growth factors propelling the synthetic training data market is the escalating complexity and diversity of AI and machine learning applications. As organizations strive to develop more accurate and robust AI models, the need for vast amounts of annotated and high-quality training data has surged. Traditional data collection methods are often hampered by privacy concerns, high costs, and time-consuming processes. Synthetic training data, generated through advanced algorithms and simulation tools, offers a compelling alternative by providing scalable, customizable, and bias-mitigated datasets. This enables organizations to accelerate model development, improve performance, and comply with evolving data privacy regulations such as GDPR and CCPA, thus driving widespread adoption across sectors like healthcare, finance, autonomous vehicles, and robotics.

Another significant driver is the increasing adoption of synthetic data for data augmentation and rare event simulation. In sectors such as autonomous vehicles, manufacturing, and robotics, real-world data for edge-case scenarios or rare events is often scarce or difficult to capture. Synthetic training data allows for the generation of these critical scenarios at scale, enabling AI systems to learn and adapt to complex, unpredictable environments. This not only enhances model robustness but also reduces the risk associated with deploying AI in safety-critical applications. The flexibility to generate diverse data types, including images, text, audio, video, and tabular data, further expands the applicability of synthetic data solutions, making them indispensable tools for innovation and competitive advantage.

The synthetic training data market is also experiencing rapid growth due to the heightened focus on data privacy and regulatory compliance. As data protection regulations become more stringent worldwide, organizations face increasing challenges in accessing and utilizing real-world data for AI training without violating user privacy. Synthetic data addresses this challenge by creating realistic yet entirely artificial datasets that preserve the statistical properties of original data without exposing sensitive information. This capability is particularly valuable for industries such as BFSI, healthcare, and government, where data sensitivity and compliance requirements are paramount. As a result, the adoption of synthetic training data is expected to accelerate further as organizations seek to balance innovation with ethical and legal responsibilities.

From a regional perspective, North America currently leads the synthetic training data market, driven by the presence of major technology companies, robust R&D investments, and early adoption of AI technologies. However, the Asia Pacific region is anticipated to witness the highest growth rate during the forecast period, fueled by expanding AI initiatives, government support, and the rapid digital transformation of industries. Europe is also emerging as a key market, particularly in sectors where data privacy and regulatory compliance are critical. Latin America and the Middle East & Africa are gradually increasing their market share as awareness and adoption of synthetic data solutions grow. Overall, the global landscape is characterized by dynamic regional trends, with each region contributing uniquely to the market’s expansion.

Component

The synthetic data tool market is experiencing rapid growth, driven by the increasing need for high-quality data to train machine learning models, especially in sectors grappling with data privacy regulations and data scarcity. The market, currently estimated at $2 billion in 2025, is projected to experience a robust Compound Annual Growth Rate (CAGR) of 25% through 2033, reaching an estimated $12 billion. This expansion is fueled by several key trends: the rising adoption of AI and machine learning across industries, the growing concerns around data privacy (GDPR, CCPA, etc.), and the increasing complexity of data annotation requirements. Companies are increasingly turning to synthetic data to overcome the limitations of real-world datasets, creating more robust and ethically sound AI solutions. The market is segmented based on various factors including data type (image, text, tabular), application (healthcare, finance, autonomous vehicles), and deployment (cloud, on-premise). While challenges remain, including the complexity of generating high-fidelity synthetic data and ensuring its representativeness of real-world data, these hurdles are being addressed through ongoing innovations in generative models and data augmentation techniques. The competitive landscape is dynamic, with numerous players ranging from established technology companies to emerging startups. Key players like Datagen, Parallel Domain, and Synthesis AI are leading the charge with their innovative solutions, while smaller players are focusing on niche applications and specific data types. The market's geographical distribution is expected to be heavily concentrated in North America and Europe initially, due to the higher adoption of AI and stricter data privacy regulations. However, growth in Asia-Pacific and other regions is anticipated as AI adoption expands globally and the value proposition of synthetic data becomes more widely understood. The historical period (2019-2024) showcased a steady incline in market adoption, paving the way for the significant growth predicted in the forecast period (2025-2033). Further segmentation based on the aforementioned factors will reveal specific opportunities and areas for future market expansion.

Satellite Based Augmentation Systems Market Outlook

The satellite-based augmentation systems (SBAS) market is poised for significant growth, with a projected CAGR of 8.9% from 2024 to 2032. The market size, valued at approximately $5.2 billion in 2023, is expected to reach around $11.3 billion by 2032. The expansion of this market is driven by the increasing demand for precise navigation and positioning services across various sectors, including aviation, maritime, and road transportation. The adoption of these systems is crucial to enhancing the accuracy and reliability of existing GPS signals, thereby facilitating a wide range of applications that require high precision.

One of the primary growth factors of the SBAS market is the rapid development of the aviation sector, which demands highly accurate navigation and positioning services for both commercial and defense purposes. The increase in air travel and the subsequent need for efficient air traffic management have pushed the aviation industry to seek advanced technologies that ensure safety and efficiency. SBAS plays a crucial role in this context by providing real-time corrections and integrity monitoring of GPS signals, which are critical for aircraft navigation. Furthermore, the modernization of air navigation infrastructure, coupled with regulatory mandates for the adoption of SBAS in various regions, is anticipated to drive market growth.

Another significant contributor to the market's expansion is the maritime industry's increasing reliance on precise navigation systems for safe and efficient operations. Maritime navigation requires accurate and reliable position data to ensure the safety of vessels, particularly in congested or sensitive areas. SBAS technology enhances the accuracy of GPS signals, providing critical support for navigational tasks such as docking, maneuvering, and collision avoidance. Additionally, the growing trend of autonomous ships and the need for enhanced navigational systems further fuel the demand for SBAS in the maritime sector.

Apart from aviation and maritime, the road and rail sectors are also embracing SBAS technologies to enhance safety and efficiency. The integration of SBAS in road transportation aids in the implementation of advanced driver assistance systems (ADAS) and supports the development of autonomous vehicles by improving positioning accuracy. Similarly, the rail industry benefits from SBAS through improved track monitoring and train positioning systems, which contribute to safer and more efficient rail operations. As these industries continue to prioritize safety and technological advancements, the demand for SBAS is likely to witness substantial growth.

Regionally, the North American market is expected to dominate the SBAS market due to the early adoption of advanced navigation systems and the presence of key players such as the United States and Canada. These countries have been at the forefront of implementing SBAS in various sectors, particularly aviation. In Asia Pacific, the market is projected to witness significant growth, driven by rapid infrastructure development and increasing investments in aviation and transportation sectors in countries like China, Japan, and India. Europe is also a major market for SBAS, with the European Geostationary Navigation Overlay Service (EGNOS) playing a pivotal role in enhancing navigation systems across the continent.

Component Analysis

The satellite-based augmentation systems market can be segmented by component into ground infrastructure, space segment, and user segment. The ground infrastructure component comprises control centers, reference stations, and data processing facilities that are essential for the functioning of SBAS. This segment is critical as it processes the data received from satellites and generates correction and integrity messages that enhance the accuracy of GPS signals. The demand for advanced ground infrastructure is growing as more regions adopt SBAS technology, necessitating the development of robust and reliable ground facilities to support these systems. Additionally, investments in upgrading existing infrastructure to accommodate new technologies and standards are further driving growth in this segment.

The space segment includes the satellites and payloads used to transmit correction signals to users. This component is vital for the dissemination of augmentation data to end-users, ensuring that the corrected and enhanced navigation signals are available across wide geographical areas. The expansion of satellite constellations dedicated to SBAS and advancements in satellite tech

The dataset consists of more than 2300 trajectories of pedestrians and 1000 trajectories of cyclists recorded by a research vehicle of the University of Applied Sciences Aschaffenburg (Kooperative Automatisierte Verkehrssysteme) in urban traffic. In addition to the actual trajectory, the data set contains 3D poses, a representation of the body posture in three-dimensional space, and semantic maps describing the surrounding of the respective vulnerable road user (VRU). The trajectories were sampled using a sliding window approach and split into a training, validation, and test dataset. Each sample contains the trajectory, 3D poses and semantic maps of the past second, as well as the sought future trajectory and semantic maps for the future 2.52 s. In addition, each pattern is assigned to a current type of motion. The motion types were annotated manually. For a more detailed description of the dataset, please refer to the following publication: Viktor Kress, Fabian Jeske, Stefan Zernetsch, Konrad Doll, Bernhard Sick: Pose and Semantic Map Based Probabilistic Forecast of Vulnerable Road Users' Trajectories. 2021, arXiv: 2106.02598, https://arxiv.org/abs/2106.02598 We provide files for the training/validation dataset and the test dataset for pedestrians and cyclists, respectively. To read the provided data, unzip the files first. Each file contains a zarr directory. Zarr is a format for the storage of chunked, compressed, N-dimensional arrays (https://zarr.readthedocs.io). To read the data: import zarr data = zarr.open(

The global Satellite Based Augmentation Systems (SBAS) market is experiencing robust growth, driven by increasing demand for enhanced precision in navigation and positioning across various sectors. The market, estimated at $5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 8% from 2025 to 2033, reaching approximately $9 billion by 2033. This growth is fueled by several key factors, including the expanding adoption of SBAS in aviation for safer and more efficient air traffic management, the increasing integration of SBAS into precision agriculture for optimized farming practices, and the rising demand for precise location services in autonomous vehicles and robotics. Furthermore, continuous technological advancements leading to improved accuracy, reliability, and coverage of SBAS are further boosting market expansion. Key players like Raytheon, Mitsubishi, Thales, Airbus, SES, and Space Systems Loral are actively contributing to this growth through innovation and strategic partnerships. The market segmentation includes various applications (aviation, maritime, land transport), technology types, and service models, with each segment showing varying growth trajectories. Despite the positive outlook, certain restraints hinder the market's full potential. High initial investment costs associated with infrastructure development and system maintenance can pose a significant barrier to entry for smaller companies and developing nations. Regulatory hurdles and interoperability challenges across different SBAS systems also contribute to slower growth in certain regions. However, ongoing efforts to standardize technologies and regulatory frameworks, coupled with the increasing affordability of SBAS solutions, are expected to mitigate these challenges in the coming years. The market’s regional distribution is expected to be heavily influenced by the existing aviation and infrastructure development across the globe, with North America and Europe likely to hold dominant market shares initially, followed by a gradual increase in adoption across Asia-Pacific and other regions.

The global Satellite Signal Augmentation Systems (SSAS) market is poised for robust growth, projected to reach $1.61 billion in 2025 and expand at a compound annual growth rate (CAGR) of 4.5% from 2025 to 2033. This growth is fueled by increasing demand for precise positioning across diverse sectors. The aerospace industry, driven by the need for enhanced navigation and safety features in aircraft, constitutes a significant market segment. Similarly, the ground transportation sector, particularly autonomous vehicles and advanced driver-assistance systems (ADAS), relies heavily on accurate positioning data provided by SSAS. The agricultural sector's adoption of precision farming techniques, requiring accurate GPS data for optimized planting and harvesting, further contributes to market expansion. Maritime applications, including improved ship navigation and safety, and the growing use of unmanned maritime systems are also key drivers. Technological advancements in both Regional Augmentation Systems (RAS) and Global Augmentation Systems (GAS), leading to improved accuracy, reliability, and cost-effectiveness, are also propelling market growth. The market segmentation reveals a dynamic landscape. While the aerospace and ground transportation segments currently dominate, the agricultural and maritime sectors exhibit strong growth potential. Regional variations reflect the varying levels of technological adoption and infrastructure development. North America and Europe, with their advanced technological infrastructure and robust aerospace and automotive industries, currently hold the largest market share. However, the Asia-Pacific region, driven by rapid urbanization, infrastructure development, and increasing adoption of precision agriculture, is projected to witness significant growth in the coming years. Competitive landscape analysis reveals a mix of established players like Honeywell, Garmin, and Thales Group, alongside specialized companies like NovAtel and Topcon Positioning Systems. These companies are focusing on technological innovation, strategic partnerships, and geographic expansion to maintain a competitive edge in this expanding market.

The global Satellite-Based Augmentation Systems (SBAS) market, valued at $715.9 million in 2025, is poised for significant growth driven by increasing demand for precise positioning and navigation across various sectors. The aviation industry, a primary driver, relies heavily on SBAS for enhanced safety and efficiency during takeoff, landing, and en-route operations. Furthermore, the burgeoning autonomous vehicle market is fueling demand for highly accurate positioning data, making SBAS a critical component. Other key applications, such as precision agriculture, maritime navigation, and surveying, are also contributing to market expansion. Technological advancements leading to improved accuracy, reliability, and coverage are further bolstering market growth. While initial infrastructure investments can be substantial, the long-term benefits in terms of safety, efficiency, and operational cost savings outweigh the initial outlay. The market is segmented by hardware (receivers, antennas), software (processing algorithms, data management), and applications (Automatic Vehicle Location (AVL), tracking systems, navigation, precise point positioning (PPP), and others). Key players include Garmin, Raytheon, Rockwell Collins, Thales, and others, actively competing through innovation and strategic partnerships. Geographic growth is expected across all regions, with North America and Europe maintaining a significant market share due to early adoption and robust infrastructure. However, the Asia-Pacific region is projected to experience the fastest growth rate due to rapid infrastructure development and increasing demand from emerging economies. The forecast period (2025-2033) anticipates a sustained growth trajectory for the SBAS market. Factors such as government initiatives promoting the use of SBAS for improved infrastructure management, coupled with rising consumer adoption of location-based services, will continue to stimulate market expansion. While competitive pressures and technological disruptions are potential restraints, the overall outlook remains positive, driven by the undeniable need for enhanced positioning accuracy and reliability across a multitude of industries. The market is characterized by a dynamic interplay between technological innovation, regulatory frameworks, and market demand, resulting in a continuously evolving landscape with opportunities for both established players and new entrants. A conservative estimate, assuming a moderate CAGR of 8% based on industry trends, projects substantial market expansion over the forecast period.

Satellite-Based Augmentation System (SBAS) Market Outlook

According to the latest research, the global Satellite-Based Augmentation System (SBAS) market size in 2024 stands at USD 730 million, reflecting robust activity across multiple sectors reliant on precise navigation and positioning. The market is projected to grow at a CAGR of 7.1% from 2025 to 2033, reaching a forecasted value of USD 1.35 billion by 2033. This remarkable expansion is primarily attributed to increasing demand for enhanced navigation accuracy, safety, and reliability across aviation, maritime, agriculture, and other critical industries. As per our latest research, the integration of SBAS into next-generation transportation and geospatial solutions is fueling both adoption and innovation within the market.

One of the core growth factors propelling the SBAS market is the rapidly rising need for safety and efficiency in global aviation. Regulatory mandates from organizations such as the International Civil Aviation Organization (ICAO) require the use of augmentation systems to improve the accuracy, integrity, and availability of GPS signals, which are vital for flight navigation and landing procedures. As air traffic continues to surge, especially in developing regions, airlines and airport authorities are increasingly adopting SBAS to minimize delays, reduce fuel consumption, and enhance operational safety. This widespread regulatory push, combined with the aviation sector’s focus on modernization, is a significant contributor to the market’s upward trajectory.

Another key driver is the expanding use of SBAS technology in non-aviation applications, including maritime navigation, precision agriculture, road and rail transportation, and land surveying. In agriculture, for instance, SBAS enables the deployment of precision farming techniques, optimizing resource usage and crop yields. Similarly, in the maritime sector, SBAS supports safer navigation in congested waterways and ports. The growing adoption of autonomous vehicles and smart transportation systems further amplifies demand for highly accurate and reliable positioning data, which SBAS provides. This cross-industry adoption not only broadens the addressable market but also encourages continuous innovation and investment in SBAS infrastructure and services.

Technological advancements and international collaborations are also playing a pivotal role in the market’s growth. The development of new SBAS constellations, such as the European Geostationary Navigation Overlay Service (EGNOS), India’s GAGAN, and Japan’s MSAS, is enhancing global coverage and interoperability. Public-private partnerships are fostering the deployment of ground and space segment infrastructure, while ongoing research is driving improvements in signal processing, data security, and system resilience. As emerging economies invest in their own SBAS capabilities, the market is witnessing increased competition and a proliferation of tailored solutions designed to meet regional and sector-specific needs.

From a regional perspective, North America currently dominates the SBAS market, owing to early adoption, advanced infrastructure, and strong regulatory frameworks. However, significant growth is anticipated in the Asia Pacific region, where countries are rapidly developing their own SBAS systems to support burgeoning aviation and transportation sectors. Europe, with its mature EGNOS program, continues to invest in system upgrades and expansion. Meanwhile, Latin America and the Middle East & Africa are gradually increasing their adoption rates, driven by international partnerships and the need for improved navigation services in critical industries. This diverse regional landscape ensures that the SBAS market remains dynamic and poised for sustained global growth.

Component Analysis

The SBAS market by component is segmented into Ground Equipment, Space Segment, and Service, each playing a critical role in ensuring the seamless operation and delivery of augmentation services. Ground Equipment includes reference stat

The key point detection market is experiencing robust growth, driven by the increasing adoption of computer vision across diverse sectors. The market's expansion is fueled by advancements in deep learning algorithms, the availability of large annotated datasets, and the declining cost of processing power. Applications span various industries, including automotive (autonomous driving and advanced driver-assistance systems), healthcare (medical image analysis and diagnostics), robotics (object manipulation and navigation), and security (surveillance and facial recognition). The rising demand for automation and efficiency across these sectors is a significant contributing factor to the market's expansion. We estimate the 2025 market size to be approximately $2.5 billion, based on observed growth in related computer vision segments and considering the technological advancements driving adoption. A conservative Compound Annual Growth Rate (CAGR) of 15% is projected for the forecast period (2025-2033), leading to significant market expansion by 2033. While significant growth is anticipated, challenges remain. The need for high-quality data for training accurate models, the computational intensity of some algorithms, and the ethical implications of certain applications (like facial recognition) pose hurdles to market penetration. However, ongoing research in efficient algorithms, data augmentation techniques, and responsible AI development is expected to mitigate these limitations. Key players such as ULUCU, Roboflow, Oosto, and MathWorks are actively contributing to market development through innovative software and hardware solutions. The market segmentation is likely to see growth in specific application niches, such as those requiring real-time processing or those focusing on specialized hardware for improved performance. The competitive landscape will likely evolve with mergers and acquisitions, as companies compete to offer the most efficient and accurate solutions.

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The hypergeolocation service market is experiencing robust growth, driven by increasing demand for precise location data across various sectors. The market's expansion is fueled by advancements in technologies like GNSS augmentation, precise point positioning (PPP), and sensor fusion, which enhance location accuracy and reliability beyond the capabilities of traditional GPS. Key application areas include autonomous vehicles, precision agriculture, surveying and mapping, maritime navigation, and drone operations. These industries require highly accurate and dependable geolocation information for optimal functionality and safety, stimulating market demand. While the provided CAGR is missing, a conservative estimate considering the rapid technological advancements and expanding applications places the annual growth rate between 15% and 20% for the forecast period. This translates to a substantial increase in market value over the next decade. The competitive landscape includes established players like Hexagon, Fugro, and Trimble, alongside emerging innovative companies. This competitive dynamic fosters innovation and drives the continuous improvement of hypergeolocation services. Significant market restraints include the high cost of implementation and maintenance of sophisticated infrastructure, as well as the challenges associated with signal interference and multipath effects in complex environments. However, ongoing technological advancements, such as the integration of artificial intelligence and machine learning for error correction and enhanced data processing, are mitigating these challenges. Further growth will also be driven by increasing government regulations mandating higher accuracy levels for location-based applications, particularly within safety-critical sectors. Segmentation within the market includes various service types (e.g., real-time kinematic, post-processed kinematic), deployment models (e.g., cloud-based, on-premise), and vertical market applications (e.g., autonomous driving, surveying). The market is geographically diverse, with North America and Europe currently dominating, but significant growth is expected from Asia-Pacific regions as adoption accelerates.