dataesr/scientific-paragraphs-categorization huggingface dataset:
The prototype web-based application was developed further creating the mobility station information pillars in the Neuaubing-Westkreuz district. The Smart Data Platform in Munich was designed and implemented using the city intelligence platform (CIP) as a theoretical blueprint. The DGK developed by the city of Munich and Fraunhofer IAO Institute for Industrial Engineering (Fraunhofer-Institut für Arbeitswirtschaft und Organisation) is a blueprint that is focused on data handling, including data security and data privacy. It describes the necessary technical, legal, and structural prerequisites to handle Smart City data and Smart City data platforms in general. Just building a data platform without the understanding of the complexity behind the complete end-to-end processes was not the goal of the City of Munich.
A central aspect of ST is monitoring smart solutions. The Austrian Institute of Technology (AIT) developed a purpose-built integrated monitoring concept for the Viennese quarter. The City of Vienna adopted the "open by default" principle, opting for a FIWARE open-source data platform. The City of Vienna joined the FIWARE Foundation and is a member of OASC (Open and Agile Smart Cities) to promote open standards and their use  [21]. The central component of Vienna's FIWARE Platform is the Orion Context Broker, which aggregates data and transfers any platform into a context-aware system-turning a spreadsheet of numbers into actionable insight. Orion is open-source software designed to consider all specifications, thus saving developers' time and money. The Context Broker provides an open standard API to communicate with all other applications in the ecosystem, such as an open government database or a dashboarding application.
The API also enables data to be sent to the historical database, the Data Lake, for monitoring developments across a timeline. Both the Context Broker and the API were developed by the FIWARE Association based on global NGSI (Next Generation Service Interfaces) specifications. FIWARE's version of the NGSI interface is a REST API that uses HTTP (Hypertext Transfer Protocol) to transfer context data between applications. The API allows data to be retrieved for both one-time queries and subscriptions. Vienna chose relevant data models from FIWARE to define how entities and attributes relate to each other. This helps the Context Broker to understand how data can be aggregated from various sources, including open government data, other public sources of data, and other local data providers. The latest NGSI V2 data model is used for the API with enhanced support for linked data.
Security Concept and Legal Contexts Digital services for various stakeholders must consider three major issues: privacy policies, legal contexts, and data quality to meet stakeholder requirements while ensuring data security for accessing the same SDP. The Grand Lyon data platform is registered with the French Data protection authority. No personal data is stored. Instead, an alternative "self-data" solution, complementary but separate from the data platform, has been developed to deal with data privacy. More than ten data sharing agreements were signed between residential data providers supplying data to the Grand Lyon data platform. The data-sharing agreement between Enedis and Grand Lyon complies with the French national data privacy regulation  [17]. The IT Department of the Greater Lyon Region oversees data security.
Visualization Tools Data dashboards need to be developed to visualize results and provide services to the citizens and the municipality. The lighthouse cities have chosen to develop applications building on already existing digital infrastructures reflecting local goals and regulations. Figure  5  illustrates examples of visualizations from each city. [24] , Munich (center)  [25] , and Vienna (right)  [26]. The data platform in Lyon creates a collaborative hub for local utilities such as GRDF, Enedis, and others. Energy consumption distribution in the territory is now visible because of data exchange and sharing. La Confluence can expand capabilities to analyze energy data as the project enabled content management system development. The CMS integrates La Confluence district. Internal negotiations are underway in Grand Lyon to extend the system throughout the City of Lyon.
Munich has a three-part DMP: The Analysis Dashboard, Muenchen. de-App, and the Transparency Dashboard. All energy, mobility, and smart lighting sensor data was transferred to the internal Smart Data Platform. The Analysis Dashboard is a part of the internal Smart Data Platform and visualizes the raw data graphically. The Analysis Dashboard has password-protected access only for internal ST Munich team members. An API from the Smart Data Platform transferred selected sensor data to the server feeding the publicly accessible Muenchen. de-App. Data could be visualized as part of an integrated digital city map on smartphones with either iOS or Android operating systems. A user can click on icons of "intelligent lampposts" to view selected ST-sensor data. The visible data on the digital map was simplified for data comprehensibility and clarity.
The approach gained trust from those involved in the public discussions. Although the TD is not a direct data link, feedback given reflected understanding and acceptance of the efforts to make the whole data collection process and analysis within ST-Munich transparent and open  [20]. The FIWARE application used in Vienna offers an appealing user interface, as desirable user management already integrates numerous modules. The FIWARE platform is equipped with an interface based on the VM9 application and offers basic visualization to view data in its raw form. During the test run, the data platform operated as a cloud solution using Netzlink's Open Stack installation. The platform relocated to the City of Vienna servers after the test run.
SPL created a municipal data platform assisted by the Japanese company, Toshiba during the Franco-Japanese Lyon Smart Community Program. Lessons learned from the first-generation SDP was applied to develop the new Grand Lyon data platform and CMS. The CMS is connected to the Grand-Lyon Data Platform, which establishes the API to send data to the CMS. Within the framework of this project, SPL Lyon Confluence provided the link between all stakeholders. Since the data platform is managed by Grand Lyon, decisions relating to adapting the SDP to changes to the La Confluence quarter must be agreed by SPL and Grand Lyon. The Munich Smart Data Platform (SDP) is an adaptation of the Siemens-owned CIP that was developed to meet ST project requirements. The new platform design and implementation meet all use case requirements for data integration, processing, analysis, and exchange in the Neuaubing-Westkreuz district.
The SDP synchronizes with the Data Gatekeeper (DGK) presenting a comprehensive blueprint on data handling within a Smart City context including data privacy and data security aspects. Unlike Lyon and Munich, Vienna did not yet have its urban data platform at the citylevel, at the beginning of the project. Vienna began by analyzing different local and international digital urban solutions in detail, and by evaluating different urban applications in Amsterdam, Lyon, Munich, Barcelona, London, Milan, and Bern. Based on the analysis results, Vienna selected a FIWARE-based (future internet ware-based) open-source solution, which has been also used for implementing the City of Vienna IoT Strategy. FIWARE fulfilled the main selection criteria: independence from a specific supplier and the possibility to further develop existing modules and solutions  [16].
Digital Mobility Services for Citizens and Stakeholders In ST Lyon the NAVLY autonomous shuttle operated from 2016 until September 2019. The distances travelled, and hourly electricity consumption data were collected during this period and communicated to the data management platform enabling vehicle performance and malfunctions to be remotely diagnosed. Navya manufactured the shuttle bus, and KEOLIS, Lyon's public transport network operator managed the fleet. Bluely was a municipal e-car sharing program that operated in the Greater Lyon Region from October 2013 to September 2020. The private e-car fleet was operated by Bolloré on behalf of Grand Lyon. Bolloré operated both the electric car fleet and the charging points. The charging points in La Confluence were expanded as part of the ST project.
The distances travelled by the e-cars, and the charging point use frequency in La Confluence was transmitted to the Grand Lyon data platform to analyze the acceptance and use of e-cars by the Lyonnaise population. Figure  6  shows the different e-mobility solutions in each LHC. Power consumption of the Compagnie Nationale du Rhône (CNR) charging points has been monitored to determine the number of car trips by privately owned electric vehicles in Confluence, and the Perrache-Sainte-Blandine neighborhood. CNR deployed a network of charging points from Genève to Avignon in the South East of France. Including Lyon, there is a total of 27 charging stations with 54 charging points  [28]. Grand Lyon only has access to data from the Lyon CNR charging points in La Confluence. Open-access mobility data is available on the Grand Lyon online dashboard from Grand Lyon's partners.
JCDecaux is in charge of the "Vélo'v" bike-sharing fleet and stations that provide real-time information about the availability of bikes and e-bikes for each station across Lyon  [29]. One of the central use cases for the Smart Data platform in Munich was data integration and analysis from eight newly installed mobility stations in the project area as seen in Figure  6. The mobility stations offered data from four different sources: car sharing usage, rental bikes, rental e-pedelecs, and rental e-trikes. The data platform analyzed and visualized the car-sharing distances driven per sharing car and station, and the number of rented bikes, the rental station, and the delivery station. Based on this data, a local ranking of usage and business impact of the newly installed stations was carried out, clearly showing useful results for the mobility station planning team.
This approach also avoided dedicating resources to collect irrelevant data. In Munich, the required data quality and content in the project are first described in a use case. The stakeholders in the Munich project classified their delivered data before sending it. Depending on the classification level, the data will be threatened in the data platform. The classification level for all ST uses cases in Munich was developed and described in the Data Gatekeeper. For the SDP, this stepwise and parallel approach forces all involved stakeholders to deal with their data, taking into consideration to think about and define the "real value of the data". This creates work on various levels of complexity related to the different tasks at the same time but is a worthwhile exercise for all participants and management levels.
Climate and pollution sensors measure local climate conditions. Other sensors track traffic and manage parking. A designated fiberglass internet cable connects several lampposts for the public M-WLAN hotspots (Munich WLAN) for free internet connectivity for all. Sixty intelligent lampposts provide lighting, free public Wi-Fi, and collect local climate and air pollution data. No digital system to measure air quality has been officially used in the City of Munich. The project provided a chance to test air quality sensors in a real lab scenario. Based upon the experiences and the encouraging results, discussions have recently begun about officially using next-generation digital air quality measuring equipment instead of an analogue infrastructure. The Smart Data Platform in Munich included several use cases like energy, mobility, and smart lampposts.
Deep-thermal renovations in Munich maintained legacy meters and the city administration is strategizing how the heating and electricity meters will be incorporated into the overall energy monitoring plan. Since 2018, several new bike rental providers established urban mobility services in Munich but were not able to be integrated into the ST project. In the future, consideration of all mobility stakeholders in mobility-analysis platforms is to be included for a comprehensive overview of the real potential of various urban mobility scenarios. Due to complexity and time-critical reasons, API-integrations into the Munich MVG and STATTAUTO databases were not feasible when the SDP commenced. It was decided to provide the mobility station's raw-data quarterly and manually via e-mail and Excel tables.
Manual integration of a continuous data flow into an SDP is simple. However, the process is extremely time-consuming and contains high error potential. Therefore, continuing manual entry is not recommended for future use even though the basic elements of the different use cases could be standardized and reused. Automating data entry for standardized analysis is a goal to be developed at a future stage. Exchanging knowledge and experiences between Munich and Lyon improved dashboard development. Munich's backend services require further development. However, the user interface for viewing analysis results in the Analysis Dashboard is a crucial component of the SDP. The interface delivers added value to the ST SDP. The screen interface underwent several development iterations to design a robust interface.
Benefit Models In ST, a benefit model describes the effects of data platforms and smart services  [18]. Most datasets in Lyon are openly accessible to citizens. The data sets are also used by private companies, such as Transit, Citymapper, and architectural and engineering firms. Datasets are integrated into private companies' databases for development of new citizen services. Data is used by Grand Lyon to develop its own digital services. For example, Grand Lyon provides the Onlymoov multimodal route planner based on data from the data. grandlyon. com platform: roads, road traffic information, information on public transport, and on the self-service bicycle system  [35] [36] [37]. The Onlymoov calculator offers metropolitan residents the best mobility solutions at any given time. Public and private initiatives can benefit from the data platform to deploy digital services as shown in Figure  7.
All energy data sets feed the CMS for three energy use case scenarios: 1. Checking new buildings conformity to the energy performance specifications set by SPL, 2. Evaluating the energy performance of building renovations and building operations, 3. Tracking energy consumption and production by the Lyon-La Confluence District according to the zero-carbon objectives in the WWF Sustainable Action Plan, and 4. Tracking the level of use of electric vehicle systems. Originally, the Munich Smart Data Dashboard was only to be used during the project. Munich decided to expand the data infrastructure beyond the project incorporating the knowledge gained during the project to design and launch a next generation Digital Twin with an Urban Data Platform. The Munich DGK is one of the projects envisaged as a standardization activity.
The SDP, based on the DGK concept, is one of the replication projects. The City of Vienna and the ST project team developed possible use cases across several departments. E-mobility sharing applications were implemented including sensor data from mobility operators. Energy consumption data from a new gymnasium, static data from renovated buildings, and numerous previously available OGD supplied the data platform. Several use cases were realized on the Viennese SCW comprising energy, building, and mobility data. Three other use cases were developed for car and bike sharing, and E-Logistics. The first use case was the energy consumption monitoring of the existing Enkplatz school gymnasium prior to renovation. The energy monitoring system was extended to the City of Vienna's social housing building stock where two buildings were refurbished and monitored within the project.
The final step is implementing the City of Vienna IoT strategy within the FIWARE-based SCW platform. Vienna's strategy follows the "Open by Default" guiding principle of their Data Excellence Strategy  [38]. Open data brings innovation, transparency, and participation by citizens, research institutions, start-ups, and many business institutions, such as through applications used by other innovation projects or by data journalists. Sharing open data enables fact-based participation, objective discussion, and generates added value for all stakeholders in the data economy. Open data enables the creation of new applications to assist citizens by offering better service delivery. Data platforms are becoming an integral part of cities  [39] [40] [41]. They enable municipalities to handle Smart City data and form the basis for new digital services for citizens and local governance.
The data platform architecture should be standards-based but is also strongly dependent on the specific requirements and conditions of each locality, especially the existing IT-infrastructure. A data platform will not replace the numerous existing municipal data entities, but rather will combine existing digital infrastructure elements to a logical common analytics and visualization tool. The data platform is a system of systems that is usable by every government department, external stakeholders, and citizens with different permissions and access. The three LHCs implemented their data management platforms by either expanding existing data platforms or creating a new platform supporting monitoring processes in the ST-implemented demonstration projects. The "urban data platforms" represent a fundamental element of the integrated processes handling data collection, processing, monitoring, and visualization from each implemented project.
Within the framework of the Grand Lyon digital data platform, the partnership with the La Confluence translates into an experimental project of energy data recovery. This is the first time in Lyon that the platform has recovered energy data. The platform adapted a new format and programmed new modules to harvest the new data typology allowing for future multi-level replication. The first level collects data for an energy assessment enabling the main building energy consumers to be identified. Appropriate measures increasing energy performance are selected. France has a large real estate portfolio including multiple public building typologies, including schools, universities, and government buildings. The collection and analysis of energy data on a district level will be scaled up to improve building energy performance on a municipal scale.
Smarter Together enabled the development of the CMS integrating energy data only from the La Confluence district. The CMS uses data sent to the Grand-Lyon data platform from more than ten building complexes enabling a global understanding of the energy flows by collecting and displaying the energy data of the area to improve the urban planning process. The same data will be applied to improve the planning, design, and operation of public infrastructures for the greater Lyon Metropolis. Additionally, the CMS will be supplied with electric smart meter data from Enedis, the electricity grid operator. Negotiations are underway to extend the CMS throughout Lyon. Cooperation between Grand Lyon and the La Confluence allows for innovations to accelerate, developing digital services to measure air quality, health and well-being, and mobility.
Munich: Developing a Digital Twin The SDP provides a flexible tool to integrate data from various use cases that are stored and refined in the database layer based on the adopted standardized API. The data is processed and visualized for the platform's dashboards and provided to third-party application services. The most important use cases of the SDP are the ST intelligent lampposts with local climate sensors. In addition, the Smart Home sensors measure temperature and humidity in selected Munich dwellings. The development of the Data Gatekeeper, design and implementation of the Smart Data Platform, and collaboration between all internal and external stakeholders created end-to-end processes benefitting the City of Munich immensely. Two important developments resulted: Munich decided to define, test, and implement a digital twin based on excellent SDP experiences, overall ST project results, knowledge building, internal and external community feedback, and increasing demand for IT-managed Smart City solutions in Munich.
Vienna: Expansion Towards a Digital Twin Vienna's smart data platform will continue to be used by the City of Vienna serving future Smart City projects. It will be expanded to become Vienna's digital twin. The data platform relocated from the temporary cloud infrastructure to the City of Vienna's permanent data center. The user interface is under revision and for integration with the Austrian security portal. FIWARE is an IoT middleware and data source that can be applied to cooperate with other Austrian cities. The intent is to expand the SCW Platform for use in other Austrian cities such as Graz and Linz, building a nationwide open-source FIWARE-based platform. The more cities that choose an open-source solution, the more synergies can result from the accumulated experience. Efficient and cost-effective integration of the building stock in local energy communities remains a challenge for municipalities in general.
Data We used the USDA National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) to study the fallow land and cropland dynamics in the U. S. portion of the RGB SES between 2008 and 2018. The CDL is a satellite-derived, crop-specific land cover dataset. It classifies more than 130 land cover types and provides a detailed classification of crop types including row crops, fruits, and vegetables (USDA NASS, 2018a). A detailed list of all categories and associated codes is available in Supplementary Information Table  S1. The CDL also tracks cultivated fallow/idle cropland. According to the USDA Farm Service Agency (FSA), fallow is defined as "unplanted cropland acres which are part of a crop/fallow rotation where cultivated land that is normally planted is purposely kept out of production during a regular growing season" (USDA FSA, 2020, p.
The NASS leverages other major sources of inputs to produce the CDL. The FSA Common Land Unit (CLU) Program is a national ground truth dataset of USDA farm program crops, collected by NASS County Field Offices. The CLU is split up in two datasets: one is used to train the classifier and distinguish between different crop types, while the other is used for accuracy assessment validation of the cultivated crop classes. NASS generates its own mapping of non-agricultural land cover classes for the CDL based on data from the USGS National Land Cover Dataset (NLCD) released every five years, along with the National Elevation Dataset (NED), percent tree canopy, and percent imperviousness used as ancillary data to separate the non-agricultural areas  (Boryan et al. , 2011). The CDL also relies on ground truth data from the NASS June Agricultural Survey that served to build a regression model estimate.
Data collection and processing We acquired the CDL annual time series for the entire available period when we conducted the study, i. e. 2008-2018, as raster-based GeoTIFF files for the states of Colorado, New Mexico, and Texas from the web application CropScape  (Han et al. , 2012). We then processed these datasets using ESRI's ArcGIS (version 10. 6) functionalities and the arcpy library. First, we selected the study area extent and generated 11 gridded annual maps of land-use for the RGB SES, preserving the spatial resolution (30 m) and projection (USA Contiguous Albers Equal-Area Conic USGS) of the original datasets. Next, we derived a mask of the total agricultural land (areas that were in cropland or fallow/idle cropland at least once between 2008 and 2018) by performing a binary classification over the 11 gridded annual maps.