Cities In Motion 2 Maps Download
Lane Frisch
Those are all static maps of long-term features, however. Alternately, there is a more dynamic way to map the city: use digital technologies to show the city in motion, charting pollution, traffic, pedestrian flow, crowds, commuting patterns, and other elements of our daily urban experience.
Argoverse 2 datasets share a common HD map format that is richer than the HD maps in Argoverse 1. Argoverse 2 datasets also share a common API, which allows users to easily access and visualize the data and maps.
We created Argoverse to support advancements in 3D tracking, motion forecasting, and other perception tasks for self-driving vehicles. We offer it free of charge under a creative commons share-alike license. Please visit our Terms of Use for details on licenses and all applicable terms and conditions.
The Argoverse 2 datasets are described in our publications, Argoverse 2 and Trust, but Verify, in the NeurIPS 2021 dataset track. When referencing these datasets or any of the materials we provide, please use the following citations
The data in Argoverse 2 comes from six U.S. cities with complex, unique driving environments: Miami, Austin, Washington DC, Pittsburgh, Palo Alto, and Detroit. We include recorded logs of sensor data, or "scenarios," across different seasons, weather conditions, and times of day.
We collected all of our data using a fleet of identical Ford Fusion Hybrids, fully integrated with Argo AI self-driving technology. We include data from two lidar sensors, seven ring cameras, and two front-facing stereo cameras. All sensors are roof-mounted:
Our semantic vector map contains 3D lane-level details, such as lane boundaries, lane marking types, traffic direction, crosswalks, driveable area polygons, and intersection annotations. These map attributes are powerful priors for perception and forecasting. For example, vehicle heading tends to follow lane direction, drivers are more likely to make lane changes where there are dashed lane boundaries, and pedestrians are more likely to cross the street at designated crosswalks.
The Argoverse 2 Lidar Dataset is a collection of 20,000 scenarios with lidar sensor data, HD maps, and ego-vehicle pose. It does not include imagery or 3D annotations. The dataset is designed to support research into self-supervised learning in the lidar domain, as well as point cloud forecasting.
All Argoverse datasets contain lidar data from two out-of-phase 32 beam sensors rotating at 10 Hz. While this can be aggregated into 64 beam frames at 10 Hz, it is also reasonable to think of this as 32 beam frames at 20 Hz. Furthermore, all Argoverse datasets contain raw lidar returns with per-point timestamps, so the data does not need to be interpreted in quantized frames.
The Lidar Dataset contains 6 million lidar frames, one of the largest open-source collections in the autonomous driving industry to date. Those frames are sampled at high temporal resolution to support learning about scene dynamics.
The Argoverse 2 Motion Forecasting Dataset is a curated collection of 250,000 scenarios for training and validation. Each scenario is 11 seconds long and contains the 2D, birds-eye-view centroid and heading of each tracked object sampled at 10 Hz.
Spanning 2,000+ km over six geographically diverse cities, Argoverse 2 covers a large geographic area. Argoverse 2 also contains a large object taxonomy with 10 non-overlapping classes that encompass a broad range of actors, both static and dynamic. In comparison to the Argoverse 1 Motion Forecasting Dataset, the scenarios in this dataset are approximately twice as long and more diverse.
The Argoverse 2 Map Change Dataset is a collection of 1,000 scenarios with ring camera imagery, lidar, and HD maps. Two hundred of the scenarios include changes in the real-world environment that are not yet reflected in the HD map, such as new crosswalks or repainted lanes. By sharing a map dataset that labels the instances in which there are discrepancies with sensor data, we encourage the development of novel methods for detecting out-of-date map regions.
The Map Change Dataset does not include 3D object annotations (which is a point of differentiation from the Argoverse 2 Sensor Dataset). Instead, it includes temporal annotations that indicate whether there is a map change within 30 meters of the autonomous vehicle at a particular timestamp. Additionally, the scenarios tend to be longer than the scenarios in the Sensor Dataset. To avoid making the dataset excessively large, the bitrate of the imagery is reduced.
The Map Change Dataset is uniquely designed so that the train and validation sets do not contain map changes. We preserve scenarios with map changes for the test set so that we can more reliably evaluate map change detection algorithms, given the rarity of map changes. The 200 test scenarios contain thousands of frames with and without map changes, so the test set is roughly balanced.
Argoverse 2 is provided free of charge under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public license. Argoverse code and APIs are provided under the MIT license. Before downloading, please view our full Terms of Use.
Because of the large file size of the Map Change Dataset (1 TB), we recommend downloading it according to these instructions. However, we do offer direct links to the dataset below. This script can be used to decompress the files.
The researchers analyzed satellite data and ground-based GPS sensors to map the vertical and horizontal motion of coastal land from New England to Florida. In a study published in PNAS Nexus, the team reported that more than half of infrastructure in major cities such as New York, Baltimore, and Norfolk is built on land that sank, or subsided, by 1 to 2 millimeters per year between 2007 and 2020. Land in several counties in Delaware, Maryland, South Carolina, and Georgia sank at double or triple that rate. At least 867,000 properties and critical infrastructure including several highways, railways, airports, dams, and levees were all subsiding, the researchers found.
These natural isostatic adjustments take place relatively deep underground, occur over long periods of time, affect broad areas, and are responsible for about half of the vertical land motion that satellites observed along the East Coast, Shirzaei said. However, shorter-lived, human-caused processes happening closer to the surface can also have a strong influence in certain areas.
The rapid subsidence in some parts of the Eastern Shore in Maryland and parts of Virginia near areas of uplift is likely partly a product of groundwater withdrawals and intentional pumping of water back into aquifers to minimize the effects of saltwater intrusion, explained Ohenhen. Likewise, the high rates of subsidence in coastal Georgia, South Carolina, and North Carolina are likely influenced by the presence of dams that block sediment that would otherwise travel down several key rivers and replenish coastal lands, and the draining and compaction of peat soils.
Charleston, South Carolina, is among the cities scrambling to react to subsidence and rising seas. This city of 800,000 people is one of the fastest sinking cities (about 4 millimeters per year) in the eastern U.S., with a portion of that thought to be the result of human activities, including groundwater pumping. With much of the downtown built at an elevation less than 3 meters (10 feet) above sea level, the frequency of tidal flooding has increased sharply in recent decades, and the city is considering building an 8-mile seawall around the Charleston peninsula to protect its downtown from storm surges.
The goal of the game is to implement and improve the public transport system in various cities. This can be achieved by building lines for buses, trams, metro trains, waterbuses, buses, and helicopters.
It was released for Microsoft Windows in 2011. Paradox Interactive released the Mac version of Cities in Motion on May 20, 2011.[2] A port of Cities in Motion to Linux was announced by Paradox Interactive in 2013, with it eventually arriving via Steam on January 9, 2014.
The main objective of the game is to create a profitable transport network that provides residents access to places of work, leisure, shopping centers and residential areas in various cities. The player acts as the head of a company providing public transportation, building new transit networks and completing from city residents or the mayor. 4 European cities are available in the base game: Amsterdam, Berlin, Helsinki and Vienna, but other cities have been released with DLCs and more can be added with addons and the map editor.
Public transport lines in the game are closed loops of stops along which transit vehicles move. There are five types of transport in the game: bus, tram, metro, water bus and helicopter. Depending on the type of transit, structures required for the operation of the route differ. A bus route needs a few stops installed along an already built street, trams need rails and stops along them. Metro needs large stations connected with metro tracks. Water bus needs two water bus stops on water, and a helicopter needs two helipads.
Management in the game involves regulating the salaries of workers and setting fares. Increasing fares increases revenue from routes, but may reduce the number of potential passengers, who are divided in the game by social cliques. At the same time, as employee salaries increase, costs increase and the condition of vehicles improve.
There are two game modes available for the game: campaign and sandbox. The campaign mode consists of scenarios depicting historical stages of public transit development in various cities throughout the 20th century. In each scenario the player needs to complete all tasks provided by the city mayor or citizens. The player is given a certain amount of money, and can take out loans from different banks with different interest rates and earn money from the transit system. Loan payments are made monthly until the entire amount is returned to the bank. At the same time, the number of available loans is limited, so if the budget is spent inefficiently the player might go bankrupt. Sandbox mode is a free game mode; when starting the mode, the player can select the city, the starting year from 1920 to 2020 and the starting amount of money. In the sandbox mode, the player is not limited to completing scenario tasks and can build the city's transport network at the player's own discretion.
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