The map dataset involved in the paper (Towards Secure and Efficient Crowdsourced Vector Map Updating on Cloud Platform)Description： (1) Folder database_dataset: Corresponds to the task publisher's original map database.(2) Folder vehicle_dataset: Crowdsourced vehicle collection trajectories, containing trajectories of 15 IDs.The specific coordinate information of the dataset is in Table 5 of the paper.Specifically, considering the confidentiality of vector map data, a geometric accuracy reduction method of has been applied to process the datasets, allowing for safer public release of the datasets while ensuring that the data remains usable.

Purpose: This is the 2019 Hurricanes Crowdsourced Photos Public Feature Layer View. This is a live publicly accessible layer for the Crowdsource Story Map accessible here: This layer cannot be edited, it is view only. ShareHidden Field: 0 = Needs Review, 1 = Already Reviewed, 2 = Hidden (not available in this public view).Audience: GIS Staff and Technologists who would like to add this layer to their own web maps and apps. If you need access to this layer in other formats, see the Open Data link. Please send us an email at triage@publicsafetygis.org to tell us if you are going to use this layer and if you have any questions or need assistance with this layer.Need to download the photos? See this technical support article.

This template is in Mature Support. Esri offers several other crowdsourcing and data collection apps. Story Map Crowdsource is a configurable application that lets you set up a Story Map that anyone can contribute to. Use it to engage a specific or general audience and collect their pictures and captions on any topic that interests you. Participants can log in with their social media account or ArcGIS account. When you configure a Crowdsource story, an interactive builder makes it easy to create your story and optionally review and approve contributions before they appear on the map.Use CasesStory Map Crowdsource can be used to create a crowdsourced map of photos related to any topic, event, or cause. The submissions can be all from a single neighborhood or from all over the world. Here are some examples:National Park MemoriesEsri 2016 User ConferenceGIS DayHonoring our VeteransUrban Food MovementConfigurable OptionsThe following aspects of a Story Map Crowdsource app can be configured using the Builder:Title, cover image, cover message, header logo and click-through link, button labels, social sharing options, and home map viewAuthentication services participants can use to sign inWhether new contributions are being acceptedWhether new contributions appear on the map immediately or only after the author approves themSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsStory Map Crowdsource does not require you to provide any geographic content, but a web map and feature service are created for your story in your account when the Builder is launched. An ArcGIS account with Publisher permissions is required to create a Crowdsource story.Get Started This application can be created in the following ways:Click the Create a Web App button on this page (sign in required)Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.For more information, see this FAQ and these blog posts..

The spatial distribution of cropland is an important input to many applications including food security monitoring and economic land use modeling. Global land cover maps derived from remote sensing are one source of cropland but they are currently not accurate enough in the cropland domain to meet the needs of the user community. Moreover, when compared with one another, these land cover products show large areas of spatial disagreement, which makes the choice very difficult regarding which land cover product to use. This paper takes an entirely different approach to mapping cropland, using crowdsourcing of Google Earth imagery via tools in Geo-Wiki. Using sample data generated by a crowdsourcing campaign for the collection of the degree of cultivation and settlement in Ethiopia, a cropland map was created using simple inverse distance weighted interpolation. The map was validated using data from the GOFC-GOLD validation portal and an independent crowdsourced dataset from Geo-Wiki. The results show that the crowdsourced cropland map for Ethiopia has a higher overall accuracy than the individual global land cover products for this country. Such an approach has great potential for mapping cropland in other countries where such data do not currently exist. Not only is the approach inexpensive but the data can be collected over a very short period of time using an existing network of volunteers.

Crowdsourced data from OpenStreetMap is used to automate the classification of satellite images into different land cover classes (impervious, farm, forest, grass, orchard, water).

There is increasing evidence that smallholder farms contribute substantially to food production globally yet spatially explicit data on agricultural field sizes are currently lacking. Automated field size delineation using remote sensing or the estimation of average farm size at subnational level using census data are two approaches that have been used but both have limitations, e.g. limited geographical coverage by remote sensing or coarse spatial resolution when using census data. This paper demonstrates another approach to quantifying and mapping field size globally using crowdsourcing. A campaign was run in June 2017 where participants were asked to visually interpret very high resolution satellite imagery from Google Maps and Bing using the Geo-Wiki application. During the campaign, participants collected field size data for 130K unique locations around the globe. Using this sample, we have produced an improved global field size map (over the previous version) and estimated the percentage of different field sizes, ranging from very small to very large, in agricultural areas at global, continental and national levels. The results show that smallholder farms occupy no more than 40% of agricultural areas, which means that, potentially, there are much more smallholder farms in comparison with the current global estimate of 12%. The global field size map and the crowdsourced data set are openly available and can be used for integrated assessment modelling, comparative studies of agricultural dynamics across different contexts and contribute to SDG 2, among many others.

The dataset (global field sizes.zip) contains: - map of dominant field sizes (dominant_field_size_categories.tif) and description of legend items (legend_items.txt) - table with all submissions by the participant (those who completed more than 10 classifications) and table description - table with quality score of all the participants and table description - table with estimated dominant field sizes at each location and table description

Three maps used in the crowdsourcing experiment - Batavia- Ambarawa en Salatiga en Omstreken- Sourabaya

A global reference dataset on cropland was collected through a crowdsourcing campaign implemented using Geo-Wiki. This reference dataset is based on a systematic sample at latitude and longitude intersections, enhanced in locations where the cropland probability varies between 25-75% for a better representation of cropland globally. Over a three week period, around 36K samples of cropland were collected. For the purpose of quality assessment, additional datasets are provided. One is a control dataset of 1793 sample locations that have been validated by students trained in image interpretation. This dataset was used to assess the quality of the crowd validations as the campaign progressed. Another set of data contains 60 expert or gold standard validations for additional evaluation of the quality of the participants. These three datasets have two parts, one showing cropland only and one where it is compiled per location and user. This reference dataset will be used to validate and compare medium and high resolution cropland maps that have been generated using remote sensing. The dataset can also be used to train classification algorithms in developing new maps of land cover and cropland extent.

Photovoltaic (PV) energy generation plays a crucial role in the energy transition. Small-scale PV installations are deployed at an unprecedented pace, and their integration into the grid can be challenging since stakeholders often lack quality data about these installations. Overhead imagery is increasingly used to improve the knowledge of distributed PV installations with machine learning models capable of automatically mapping these installations. However, these models cannot be easily transferred from one region or data source to another due to differences in image acquisition. To address this issue known as domain shift and foster the development of PV array mapping pipelines, we propose a dataset containing aerial images, annotations, and segmentation masks. We provide installation metadata for more than 28,000 installations. We provide ground truth segmentation masks for 13,000 installations, including 7,000 with annotations for two different image providers. Finally, we provide ground truth annotations and associated installation metadata for more than 8,000 installations. Dataset applications include end-to-end PV registry construction, robust PV installations mapping, and analysis of crowdsourced datasets.

This dataset contains the complete records associated with the article "A crowdsourced dataset of aerial images of solar panels, their segmentation masks, and characteristics", currently under review. These complete records consist of RGB overhead imagery, segmentation masks, and characteristics of PV installations. The data records are organized as follows:

• bdappv/ Root data folder
  • google / ign: One folder for each campaign
    • img/: Folder containing all the images presented to the users. This folder contains 28807 images for Google and 17325 images for IGN.
    • mask/: Folder containing all segmentations masks generated from the polygon annotations of the users. This folder contains 13303 masks for Google and 7686 masks for IGN.
• metadata.csv The .csv file with the characteristics of the installations.

Crowdsource Polling is a configurable app template that can be used for collecting feedback and assessing public sentiment for a series of proposals, plans, or events. Users are presented with a map and list of features containing the details of each proposal, plan, or event including any attached documents. These users can then submit their feedback in the form of votes and comments. Crowdsource Polling can be accessed anonymously and by authenticating via Twitter.Use CasesCrowdsource Polling can be configured to present information such as:proposed land use changesenvironmental impact pollingpublic comment on capital projectspublic comment on proposed rights of way for transmission systemsevents permit reviewConfigurable OptionsConfigure Crowdsource Polling to present content from any web map and personalize the app by modifying the following options: Display a custom title and logo in the application headerUse a custom color schemeChoose which layer contains the features for which feedback is being solicitedProvide custom instruction on the use of the app, contact information, credits, etc. in a highly configurable help windowSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Crowdsource Polling requires a web map with at least one feature layer. In addition, the following requirements must be met to expose full app functionality:To enable votes, this layer must have a numeric field for storing the number of votes on each featureTo collect comments, the feature layer must have a related tableTo capture the names of authenticated users, the layer must have a text field for storing this valueGet Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.Learn MoreFor release notes and more information on configuring this app, see the Crowdsource Polling documentation.

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.

The self-driving 3D high-precision map market is experiencing significant growth, driven by the accelerating adoption of autonomous vehicles and the increasing demand for advanced driver-assistance systems (ADAS). This market is projected to reach a value of $10 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 25% from 2025 to 2033. This substantial growth is fueled by several key factors: the continuous improvement in sensor technology, the rising investments in R&D by automotive and technology companies, and stringent government regulations promoting road safety. The market is segmented by crowdsourcing model and centralized model, and by application into L1/L2+ driving automation, L3 driving automation, and others. Key players such as TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Baidu, and NVIDIA are actively shaping the market landscape through innovative map creation and update technologies. The North American market currently holds a significant share, but the Asia-Pacific region is expected to witness the fastest growth in the forecast period due to the increasing adoption of autonomous vehicles in countries like China and India. The competitive landscape is characterized by both established map providers and emerging technology companies, leading to continuous innovation and improvement in map accuracy and coverage. The restraints on market growth include the high cost of developing and maintaining high-precision maps, data security concerns, and the challenges in updating maps frequently to account for dynamic changes in the road infrastructure. However, advancements in cloud computing, artificial intelligence, and machine learning are mitigating these challenges. The shift towards more accurate and frequently updated maps is driving the adoption of both crowdsourcing and centralized mapping models. The development of highly automated vehicles (L3 and beyond) is creating a particularly strong demand for these advanced maps. This suggests a trajectory of continued expansion in this market, even as challenges related to data management and cost remain.

Crowdsource Manager is a configurable group app template that can be used for triaging crowd sourced data across multiple layers and maps as it is collected using applications such as Crowdsource Reporter or Collector. Using Crowdsource Manager, these reports can be reviewed and attributes such as assignment and status can be updated. Attachments and comments associated with each report are also accessible.Use CasesCrowdsource Manager can be configured for reviewing any crowd sourced information, including data collected through Crowdsource Reporter configurations such as these:citizen service requestshealth and safety reportscitizen science reportsdrone imagery reviewreviewing real estate property listingsConfigurable OptionsConfigure Crowdsource Manager to present a group of maps with editable layers, and personalize the app by modifying the following options: Display a custom title and logo in the application headerChoose a color schemeUse the map pop-up settings to specify which fields should be visible and which should be editableSupported DevicesThis application is responsively designed to support use in browsers on desktops and tablets..Data RequirementsCrowdsource Manager requires an ArcGIS Online group that contains at least one map with at least one editable feature layer.This web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a group and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.Learn MoreFor release notes and more information on configuring this app, see the Crowdsource Manager documentation.

The Geo-Wiki Project is a global network of volunteers who wish to help improve the quality of global land cover maps. Since large differences occur between existing global land cover maps, current ecosystem and land-use science lacks crucial accurate data (e.g. to determine the potential of additional agricultural land available to grow crops in Africa). Volunteers are asked to review hotspot maps of global land cover disagreement and determine, based on what they actually see in Google Earth and their local knowledge, if the land cover maps are correct or incorrect. Their input is recorded in a database, along with uploaded photos, to be used in the future for the creation of a new and improved global land cover map.

A map used in the Vision Zero application to enlist feedback from bikers, drivers, and pedestrians using public streets.

The self-driving 3D high-precision map market is experiencing rapid growth, driven by the increasing adoption of autonomous vehicles and advanced driver-assistance systems (ADAS). This market is projected to reach a substantial size, with a Compound Annual Growth Rate (CAGR) reflecting significant expansion over the forecast period of 2025-2033. Let's assume, based on typical growth rates observed in similar technological sectors and considering the considerable investment in autonomous vehicle technology, a market size of $5 billion in 2025 and a CAGR of 25% is reasonable. This implies a market value exceeding $20 billion by 2033. Key drivers include the rising demand for safer and more efficient transportation solutions, advancements in sensor technologies (LiDAR, radar, cameras), and the continuous development of sophisticated mapping algorithms. The market is segmented by crowdsourcing model versus centralized model and by application, encompassing levels L1/L2+ and L3 driving automation, alongside other emerging applications. Major players like TomTom, Google, Alibaba (AutoNavi), Navinfo, Mobileye, Baidu, and NVIDIA are actively shaping this landscape through innovative mapping solutions and strategic partnerships. The regional distribution shows significant concentration in North America and Asia Pacific, particularly in the United States and China, fueled by robust technological advancements and supportive government regulations. The market's growth trajectory is influenced by several trends, including the increasing availability of high-resolution satellite imagery and aerial photography for map creation, the development of real-time map updates based on vehicle data, and the integration of artificial intelligence (AI) for enhanced map accuracy and efficiency. However, challenges such as high data acquisition and processing costs, data privacy concerns, and the need for continuous map updates represent significant restraints. The future growth of the self-driving 3D high-precision map market will heavily depend on the continued progress in autonomous driving technology, the scalability of crowdsourcing solutions, and the ability to overcome regulatory and technological hurdles. The integration of 5G technology promises to further accelerate growth by enabling faster data transmission and real-time map updates, making the autonomous driving experience smoother and safer.

Successful knowledge graphs (KGs) solved the historical knowledge acquisition bottleneck by supplanting the previous expert focus with a simple, crowd-friendly one: KG nodes represent popular people, places, organizations, etc., and the graph arcs represent common sense relations like affiliations, locations, etc. Techniques for more general, categorical, KG curation do not seem to have made the same transition: the KG research community is still largely focused on logic-based methods that belie the common-sense characteristics of successful KGs. In this paper, we propose a simple yet novel three-tier crowd approach to acquiring class-level attributes that represent broad common sense associations between categories, and can be used with the classic knowledge-base default & override technique, to address the early label sparsity problem faced by machine learning systems for problems that lack data for training. We demonstrate the effectiveness of our acquisition and reasoning approach on a pair of very real industrial-scale problems: how to augment an existing KG of places and offerings (e.g. stores and products, restaurants and dishes) with associations between them indicating the availability of the offerings at those places. Label sparsity is a general problem, and not specific to these use cases, that prevents modern AI and machine learning techniques from applying to many applications for which labeled data is not readily available. As a result, the study of how to acquire the knowledge and data needed for AI to work is as much a problem today as it was in the 1970s and 80s during the advent of expert systems. Our approach was a critical part of enabling a worldwide local search capability on Google Maps, with which users can find products and dishes that are available in most places on earth.

In species richness studies, citizen-science surveys where participants make individual decisions regarding sampling strategies provide a cost-effective approach to collect a large amount of data. However, it is unclear to what extent the bias inherent to opportunistically collected samples may invalidate our inferences. Here, we compare spatial predictions of forest ground-floor bryophyte species richness in Limburg (Belgium), based on crowd- and expert-sourced data, where the latter are collected by adhering to a rigorous geographical randomisation and data collection protocol. We develop a log-Gaussian Cox process model to analyse the opportunistic sampling process of the crowd-sourced data and assess its sampling bias. We then fit two geostatistical Poisson models to both data-sets and compare the parameter estimates and species richness predictions. We find that the citizens had a higher propensity for locations that were close to their homes and environmentally more valuable. The estimated effects of ecological predictors and spatial species richness predictions differ strongly between the two geostatistical models. Unknown inconsistencies in the sampling process, such as unreported observer's effort, and the lack of a hypothesis-driven study protocol can lead to the occurrence of multiple sources of sampling bias, making it difficult, if not impossible, to provide reliable inferences.

This dataset represents point locations for various recreation sites within the state of New York symbolized by total number of Facebook likes. This dataset is used to understand the value of New York recreational sites using social media crowdsource information to be used in planning activities. Other information is available on number of Facebook likes, new posts, and rating.View Dataset on the Gateway

The global satellite navigation service market is experiencing robust growth, driven by increasing demand across diverse sectors. While precise market size figures for 2025 are unavailable, a logical estimation based on industry reports and a projected CAGR (assuming a conservative CAGR of 7% based on typical tech sector growth) indicates a substantial market value. This growth is fueled primarily by the expanding adoption of satellite navigation in the aviation and transportation sectors, where precise positioning and real-time tracking are critical. The proliferation of connected vehicles, autonomous driving initiatives, and the rise of precision agriculture are significant contributors to this expansion. Furthermore, the increasing reliance on location-based services (LBS) across various applications, from logistics and delivery to personal navigation and asset tracking, is propelling demand for reliable and accurate satellite navigation systems. Centralized mapping systems remain dominant, but the growth of crowdsourced mapping solutions offers enhanced data accuracy and cost-effectiveness, contributing to further market segmentation. However, market growth is not without its challenges. Constraints include the inherent vulnerability to signal interference and jamming, potential security risks associated with data breaches, and the high initial investment required for infrastructure development and system implementation. Moreover, regulatory complexities and the need for standardization across different satellite navigation systems present hurdles to overcome. Despite these constraints, the market's long-term outlook remains optimistic, driven by technological advancements, such as the development of more resilient and accurate satellite constellations, the integration of artificial intelligence (AI) for enhanced data processing, and the expansion of 5G and IoT infrastructure to support location-based services. The market is expected to witness further segmentation based on application and technological advancements, with niche players and established giants vying for market share. The regional breakdown is likely to see North America and Europe maintaining a leading position due to advanced infrastructure and higher adoption rates, while Asia-Pacific is anticipated to showcase significant growth driven by rapid industrialization and urbanization.