Near Real-time and archival data of High-resolution (10 m) flood inundation dataset over the Contiguous United States, developed based on the Sentinel-1 SAR imagery (2016-current) archive, using an automated Radar Produced Inundation Diary (RAPID) algorithm.

The USDA Forest Service Rapid Assessment of Vegetation Condition after Wildfire (RAVG) program produces geospatial data and maps of post-fire vegetation condition using standardized change detection methods based on Landsat or similar multispectral satellite imagery. RAVG data products characterize vegetation condition within a fire perimeter, and include estimates of percent change in basal area (BA), percent change in canopy cover (CC), and a standardized composite burn index (CBI). Standard thematic products include 7-class percent change in basal area (BA-7), 5-class percent change in canopy cover (CC-5), and 4-class CBI (CBI-4). Contingent upon the availability of suitable imagery, RAVG products are prepared for all wildland fires reported within the conterminous United States (CONUS) that include at least 1000 acres of forested National Forest System (NFS) land (500 acres for Regions 8 and 9 as of 2016). Data for individual fires are typically made available within 45 days after fire containment ("initial assessments"). Late-season fires, however, may be deferred until the following spring or summer ("extended assessments"). National mosaics of each thematic product are prepared annually. Mosaics of extended assessments, if any, are provided separately from initial assessment mosaics. This map service includes annual raster mosaics of published BA-7 datasets for fires that burned during calendar years 2012 through 2023, excluding 2020 extended assessments. The associated burned area perimeters are available via the Enterprise Data Warehouse (EDW, see https://data.fs.usda.gov/geodata/edw/datasets.php).

Activation time (UTC): 2018-03-08 14:20:00
Event time (UTC): 2018-03-08 00:00:00
Event type: Humanitarian (Population displacement (IDP))

Activation reason:
The Danish Emergency Management Agency (DEMA) is planning to build a coordination hub in Bangladesh in relation with Rohingya refugees. The Copernicus EMS Rapid Mapping Service has been triggered to produce Reference Maps based on recent optical satellite imagery that will be used for the initial assessment on the Areas of Interest.

Reference products: 5
Delineation products: 0
Grading products: 0

Copernicus Emergency Management Service - Mapping is a service funded by European Commission aimed at providing actors in the management of natural and man-made disasters, in particular Civil Protection Authorities and Humanitarian Aid actors, with mapping products based on satellite imagery.

Bus Rapid Transfer (BRT) station locations.

For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

Rapid Transit Zone. The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

This dataset includes a detailed example for using our method (described in paper linked to below) to digitize historical land-use maps in R. We also release all of the Swedish land-use maps that we digitized for this project. This includes the Economic Map of Sweden (Ekonomiska kartan) over Sweden's 15 southernmost counties (7069 25 km2 sheets), plus 11 sheets of the District Economic Map (Häradsekonomiska kartan - but see http://bolin.su.se/data/Cousins-2015 for more accurate manual digitization).

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively. With overall mapping accuracy for the map reaching 58.03 percent, this approach shows strong potential for generating crop type maps of current year in September.

MBTA Rapid Transit data represents the station stops on the five subway, streetcar/trolley and Silver Line bus "T" lines (Blue, Green, Orange, Red and Silver) in the Massachusetts Bay Transportation Authority's rapid transit rail network. The layers were developed by the Central Transportation Planning Staff (CTPS), with additional editing by MassGIS based on current aerial imagery and information from mbta.com. See the datalayer page for metadata and a link to free data download.Map service also available.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market's value is estimated at $2 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of location-based services, the proliferation of readily available geographic data, and the growing need for effective data visualization in business intelligence and marketing. The individual user segment currently holds a significant share, but corporate adoption is rapidly expanding, propelled by the need for sophisticated map-based analytics and internal communication. Furthermore, the paid use segment is anticipated to grow more quickly than the free use segment, reflecting the willingness of businesses and organizations to invest in advanced features and functionalities. This trend is further amplified by the increasing integration of interactive maps into various platforms, such as business intelligence dashboards and website content. Geographic expansion is also a significant growth driver. North America and Europe currently dominate the market, but the Asia-Pacific region is showing significant promise due to rapid technological advancements and increasing internet penetration. Competitive pressures remain high, with established players such as Google, Mapbox, and ArcGIS StoryMaps vying for market share alongside innovative startups offering specialized solutions. The market's restraints are primarily focused on the complexities of data integration and the technical expertise required for effective map creation. However, ongoing developments in user-friendly interfaces and readily available data integration tools are mitigating these challenges. The future of the interactive map creation tools market promises even greater innovation, fueled by developments in augmented reality (AR), virtual reality (VR), and 3D visualization technologies. We expect to see the emergence of more sophisticated tools catering to niche requirements, further driving market segmentation and specialization. Continued investment in research and development will also play a crucial role in pushing the boundaries of what's possible with interactive map creation. The market presents opportunities for companies to develop tools which combine data analytics and interactive map design.

This repository contains the flood proxy maps (FPMs) and damage proxy map (DPM) covering the Tokyo, Fukushima, Ibaraki and Nagano prefectures. Maps are derived from synthetic aperture radar data acquired by the Copernicus Sentinel-1 satellites operated by the European Space Agency and ALOS-2 satellites operated by the Japan Aerospace Exploration Agency. Analysed by the ARIA-SG team at the Earth Observatory of Singapore and the ARIA team at National Aeronautics and Space Administration - Jet Propulsion Laboratory and California Institute of Technology.

Navigation Map Market Outlook

The global navigation map market size was valued at USD 20.5 billion in 2023 and is projected to reach USD 45.8 billion by 2032, exhibiting a Compound Annual Growth Rate (CAGR) of 9.3% during the forecast period. The primary growth factor propelling this market is the increasing integration of advanced mapping technologies in automotive and mobile device industries, aimed at enhancing navigation and user experience.

One of the key drivers of the navigation map market is the rapid technological advancements in digital mapping and Geographic Information Systems (GIS). Innovations such as real-time traffic updates, augmented reality (AR) navigation, and highly detailed 3D maps are significantly enhancing the functionality and user experience of navigation systems. These advancements are crucial for the development of autonomous driving technologies, which rely heavily on precise and real-time mapping data. The proliferation of smartphones equipped with GPS capabilities has also expanded the demand for high-quality digital maps, further fueling market growth.

Another significant growth factor is the increasing demand for navigation solutions in the automotive industry. As automakers strive to enhance driver safety and convenience, the integration of advanced navigation systems has become a standard feature in modern vehicles. The advent of connected cars, which communicate with external systems for real-time traffic and route information, is further driving the need for sophisticated navigation maps. Additionally, the growing trend of ride-hailing and logistics services has necessitated the use of accurate and efficient navigation solutions to optimize routes and improve operational efficiency.

The commercial sector is also contributing to the growth of the navigation map market. Businesses are increasingly relying on advanced mapping solutions to streamline their operations, manage logistics, and enhance customer service. For instance, companies in the e-commerce and delivery services sectors use navigation maps to ensure timely and efficient deliveries. Moreover, the government and public sector are adopting navigation maps for urban planning, disaster management, and public safety applications. These diverse applications across various sectors are collectively driving the demand for navigation maps, thereby contributing to market expansion.

Regionally, North America holds a significant share of the navigation map market, driven by the presence of major technology companies and high adoption rates of advanced navigation solutions. The Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, attributed to the rapid urbanization, increasing smartphone penetration, and growing automotive industry in countries like China and India. Europe also represents a substantial market share, supported by stringent regulations on vehicle safety and the presence of leading automotive manufacturers. The Middle East & Africa and Latin America are gradually adopting advanced navigation technologies, presenting potential growth opportunities in these regions.

The evolution of High Precision Map technology is revolutionizing the navigation map market, particularly in the realm of autonomous vehicles and advanced driver-assistance systems. These maps provide an unparalleled level of detail, including lane-level accuracy and precise positioning, which are essential for the safe and efficient operation of self-driving cars. High Precision Maps are not only crucial for navigation but also for enhancing the overall driving experience by integrating real-time data and predictive analytics. This technology allows vehicles to anticipate road conditions, optimize routes, and improve fuel efficiency, thereby contributing to the broader goals of sustainability and safety in the automotive industry.

Product Type Analysis

The navigation map market is segmented into digital maps and paper maps, each catering to different user preferences and applications. Digital maps are the dominant segment, driven by the widespread use of smartphones, tablets, and in-car navigation systems. Digital maps offer real-time updates, interactive features, and the ability to integrate with other applications, making them highly popular among users. The continuous advancements in digital mapping technologies, such as 3D mapping, AR navigation, and real-time traffic information, are further enhancing the appeal and functionality of digi

A map used in the Rapid Pavement Assessments ArcGIS QuickCapture project to view road segments and rapid pavement assessment results.

The global navigation map market is experiencing robust growth, driven by increasing adoption of location-based services across various sectors. Our analysis projects a market size of $15 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant expansion is fueled by several key factors. The automotive industry's reliance on advanced driver-assistance systems (ADAS) and autonomous vehicles is a primary driver, demanding high-precision and regularly updated map data. Furthermore, the proliferation of mobile devices with integrated GPS and mapping applications continues to stimulate market growth. The burgeoning enterprise solutions segment, utilizing navigation maps for logistics, fleet management, and delivery optimization, contributes significantly to overall market value. Government and public sector initiatives promoting smart cities and infrastructure development further fuel demand. Technological advancements, such as the integration of LiDAR and improved GIS data, enhance map accuracy and functionality, attracting more users and driving market expansion. The market segmentation reveals substantial contributions from various application areas. The automotive segment is projected to maintain its dominance throughout the forecast period, followed closely by the mobile devices and enterprise solutions segments. Within the type segment, GIS data holds a significant market share due to its versatility and application across various sectors. However, LiDAR data is experiencing rapid growth, driven by its high precision and suitability for autonomous driving applications. Geographic regional analysis indicates strong market presence in North America and Europe, primarily driven by advanced technological infrastructure and high adoption rates. However, the Asia-Pacific region is poised for substantial growth, fueled by rapid urbanization, increasing smartphone penetration, and government investments in infrastructure development. Competitive landscape analysis reveals a blend of established players and emerging technology companies, signifying an increasingly dynamic and innovative market environment.

Layered GeoPDF 7.5 Minute Quadrangle Map. Layers of geospatial data include orthoimagery, roads, grids, geographic names, elevation contours, hydrography, and other selected map features.

Legacy product - no abstract available Legacy product - no abstract available

Event summary map for the June 28, 2020, Rapid City, MB tornado. Ground survey conducted June 29, 2020. Map includes ground photos, drone photos, worst damage points, and tornado centreline.

Cadastral Mapping Market Outlook

The global cadastral mapping market size was valued at approximately USD 4.2 billion in 2023 and is projected to reach around USD 7.9 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.2% during the forecast period. This market growth can be attributed to increasing urbanization, rapid advancements in geospatial technologies, and the growing need for efficient land management systems across various regions.

The expansion of urban areas and the corresponding increase in the need for effective land management infrastructure are significant growth factors driving the cadastral mapping market. As urbanization accelerates globally, local governments and planning agencies require sophisticated tools to manage and record land ownership, boundaries, and property information. Enhanced geospatial technologies, including Geographic Information Systems (GIS) and remote sensing, are pivotal in facilitating accurate and efficient cadastral mapping, thus contributing to market growth.

Another key growth factor is the rising demand for infrastructure development. As nations invest in large-scale infrastructure projects such as roads, railways, and smart cities, there is an increased need for precise land data to ensure the proper allocation of resources and to avoid legal disputes. Cadastral mapping provides the critical data needed for these projects, hence its demand is surging. Additionally, governments worldwide are increasingly adopting digital platforms to streamline land administration processes, further propelling the market.

Furthermore, the agricultural sector is also significantly contributing to the growth of the cadastral mapping market. Modern agriculture relies heavily on accurate land parcel information for planning and optimizing crop production. By integrating cadastral maps with other geospatial data, farmers can improve land use efficiency, monitor crop health, and enhance yield predictions. This integration is particularly valuable in precision farming, which is becoming more prevalent as the world's population grows and the demand for food increases.

Regionally, Asia Pacific is expected to witness the highest growth in the cadastral mapping market. Factors such as rapid urbanization, extensive infrastructure development projects, and the need for improved land management are driving the demand in this region. Moreover, governments in countries like India and China are investing heavily in creating digital land records and implementing smart city initiatives, which further boosts the market. The North American and European markets are also substantial, driven by the advanced technological infrastructure and well-established land administration systems.

Component Analysis

The cadastral mapping market can be segmented by component into software, hardware, and services. The software segment holds a significant share in this market, driven by the increasing adoption of advanced GIS and mapping software solutions. These software solutions enable accurate land parcel mapping, data analysis, and integration with other geospatial data systems, making them indispensable tools for cadastral mapping. Companies are continuously innovating to provide more intuitive and comprehensive software solutions, which is expected to fuel growth in this segment.

Hardware components, including GPS devices, drones, and other surveying equipment, are also critical to the cadastral mapping market. The hardware segment is expected to grow steadily as technological advancements improve the accuracy and efficiency of these devices. Innovations such as high-resolution aerial imaging and LIDAR technology are enhancing the capabilities of cadastral mapping hardware, allowing for more detailed and precise data collection. This segment is particularly essential for field surveying and data acquisition, forming the backbone of cadastral mapping projects.

The services segment encompasses a wide range of offerings, including consulting, implementation, and maintenance services. Professional services are vital for the successful deployment and operation of cadastral mapping systems. Governments and private sector organizations often rely on specialized service providers to implement these systems, train personnel, and ensure ongoing support. As the complexity of cadastral mapping projects increases, the demand for expert services is also expected to rise, contributing to the growth of this segment.

Integration services are another critical component within the

Spatially accurate annual crop cover maps are an important component to various planning and research applications; however, the importance of these maps varies significantly with the timing of their availability. Utilizing a previously developed crop classification model (CCM), which was used to generate historical annual crop cover maps (classifying nine major crops: corn, cotton, sorghum, soybeans, spring wheat, winter wheat, alfalfa, other hay/non alfalfa, fallow/idle cropland, and ‘other’ as one class for remaining crops), we hypothesized that such crop cover maps could be generated in near real time (NRT). The CCM was trained on 14 temporal and 15 static geospatial datasets, known as predictor variables, and the National Agricultural Statistics Service (NASS) Cropland Data Layers (CDL) was used as the dependent variable. We were able to generate a NRT crop cover map by the first day of September through a process of incrementally removing weekly and monthly data from the CCM and comparing the subsequent map results with the original maps and NASS CDLs. Initially, our NRT results revealed training error of 1.4% and test error of 8.3%, as compared to 1.0% and 7.6%, respectively for the original CCM. Through the implementation of a new ‘two-mapping model’ approach, we were able to substantially improve the results of the NRT crop cover model. We divided the NRT model into one ‘crop type model’ to handle the classification of the nine specific crops and a second, binary model to classify crops as presence or absence of the ‘other’ crop. Under the two-mapping model approach, the training errors were 0.8% and 1.5% for the crop type and binary model, respectively, while test errors were 5.5% and 6.4% for crop type and binary model, respectively.With overall mapping accuracy for the map reaching 69.88 percent, this approach shows strong potential for generating crop type maps of current year in September.

Greater Taipei Mass Rapid Transit (MRT) System Route Network GIS Map Data

It includes all heavy rail and light rail rapid transit lines. Due to track circuit or other data issues, data is not guaranteed to be complete for any stop or date. Data Dictionary:NameDescriptionData TypeExampleservice_dateDate for which headways should be returned.Date2019-12-31route_idGTFS-compatible route for which headways should be returned.StringOrangedirection_idGTFS-compatible direction for which headways should be returned.Integer0stop_idGTFS-compatible stop for which headways should be returned.String70154start_time_secProperty of “Headways”. Expressed in "seconds after midnight." The time associated with the departure event of previous vehicle.Integer45763end_time_secProperty of “Headways”. Expressed in "seconds after midnight." The time associated with the departure event of current vehicle.Integer46411headway_time_secProperty of “Headways”. Difference between start_time_sec and end_time_sec. The actual travel time between the origin stop and the destination stop, in seconds. Red line trunk stops will have two headways for the same southbound train: one dependent on the destination and one independent of the destination.Integer648destinationProperty of “Headways”. Intended destination for the vehicle.StringForest HillsMassDOT/MBTA shall not be held liable for any errors in this data. This includes errors of omission, commission, errors concerning the content of the data, and relative and positional accuracy of the data. This data cannot be construed to be a legal document. Primary sources from which this data was compiled must be consulted for verification of information contained in this data.