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).

Registered charity 1126727; registered company limited by guarantee 6611408 (England and Wales)

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.

Registered charity 1126727; registered company limited by guarantee 6611408 (England and Wales)

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 67.06 percent, this approach shows strong potential for generating crop type maps of current year in September.

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.

Proposed Bus Rapid Transit (BRT) System Lines.

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.

This resource links to the Hurricane Maria Story Map https://arcg.is/00f1ij This story map provides access to a number of Hurricane Maria datasets not hosted on hydroshare.org. Maps with FEMA damage, USGS landslide, forest disturbance, power outages, and health data are browsable here. Additional photos from the event and links to other resources are also presented. Other resources include datasets from NASA, NOAA, FEMA, USGS, as well as other organizations.

UNOSAT code: CE20220223UKR This map illustrates a satellite imagery based Rapid Damage Building Assessment (RDBA) in Sumy City, Ukraine. The RDBA divides the city into 500m x 500m cells, each of which is analyzed to determine whether or not there are damaged buildings inside the cell.

Based on imagery collected on 20 and 22 March 2022, analysts found that 5 cells out of 1,111 cells sustained visible damage. This represents approximately 0.4% of the cells over the city.

This analysis is based on structures visibly damaged as of 20 and 22 March 2022 as seen in marginally degraded satellite imagery. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

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.Due to data collection issues, the following dates and/or lines are missing from this data set:March 23, 2020 Mattapan High Speed Line. Data Dictionary:

Name
Description
Data Type
Example


service_date
Date for which headways should be returned. 
Date
2020-12-31


route_id
GTFS-compatible route for which headways should be returned. 
String
Orange


direction_id
GTFS-compatible direction for which headways should be returned. 
Integer
0


stop_id
GTFS-compatible stop for which headways should be returned.
String
70154


start_time_sec
Property of “Headway”. Expressed in "seconds after midnight." The time associated with the departure event of previous vehicle.
Integer
45763


end_time_sec
Property of “Headway”. Expressed in "seconds after midnight." The time associated with the departure event of current vehicle.
Integer
46411


headway_time_sec
Property of “Headway”. Difference between start_time_sec and end_time_sec. The actual headway between two trains with the same destination, 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.
Integer
648


destination
Property of “Headway”. Intended destination for the vehicle.
String
Forest Hills

MassDOT/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.

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 CBI-4 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).

Background: Given the worldwide spread of the 2019 Novel Coronavirus (COVID-19), there is an urgent need to identify risk and protective factors and expose areas of insufficient understanding. Emerging tools, such as the Rapid Evidence Map (rEM), are being developed to systematically characterize large collections of scientific literature. We sought to generate an rEM of risk and protective factors to comprehensively inform areas that impact COVID-19 outcomes for different sub-populations in order to better protect the public.Methods: We developed a protocol that includes a study goal, study questions, a PECO statement, and a process for screening literature by combining semi-automated machine learning with the expertise of our review team. We applied this protocol to reports within the COVID-19 Open Research Dataset (CORD-19) that were published in early 2020. SWIFT-Active Screener was used to prioritize records according to pre-defined inclusion criteria. Relevant studies were categorized by risk and protective status; susceptibility category (Behavioral, Physiological, Demographic, and Environmental); and affected sub-populations. Using tagged studies, we created an rEM for COVID-19 susceptibility that reveals: (1) current lines of evidence; (2) knowledge gaps; and (3) areas that may benefit from systematic review.Results: We imported 4,330 titles and abstracts from CORD-19. After screening 3,521 of these to achieve 99% estimated recall, 217 relevant studies were identified. Most included studies concerned the impact of underlying comorbidities (Physiological); age and gender (Demographic); and social factors (Environmental) on COVID-19 outcomes. Among the relevant studies, older males with comorbidities were commonly reported to have the poorest outcomes. We noted a paucity of COVID-19 studies among children and susceptible sub-groups, including pregnant women, racial minorities, refugees/migrants, and healthcare workers, with few studies examining protective factors.Conclusion: Using rEM analysis, we synthesized the recent body of evidence related to COVID-19 risk and protective factors. The results provide a comprehensive tool for rapidly elucidating COVID-19 susceptibility patterns and identifying resource-rich/resource-poor areas of research that may benefit from future investigation as the pandemic evolves.

The Rapid Carbon Assessment (RaCA) was initiated by the USDA-NRCS Soil Science Division in 2010 with the following objectives:

To develop statistically reliable quantitative estimates of amounts and distribution of carbon stocks for U.S. soils under various land covers and to the extent possible, differing agricultural management. To provide data to support model simulations of soil carbon change related to land use change, agricultural management, conservation practices, and climate change. To provide a scientifically and statistically defensible inventory of soil carbon stocks for the U.S.

To accomplish these objectives, 144,833 samples were collected from the upper 1 meter of 32,084 soil profiles at 6,017 randomly selected locations for measurement of organic and inorganic carbon by visible and near infrared (VNIR) spectroscopy and bulk density by traditional methods. NRI sites were used as the basis for random selection of sample sites stratified by soil group within RaCA Region and land use/land cover (LULC) within soil group. Soil morphology and landscape characteristics were described at each site and limited vegetation and agricultural management information was collected from each location. Sample collection and analysis involved more than 300 soil scientists and assistance from 24 universities. Dowloadable Data Tables:

RaCA samples (CSV; 41.1 MB) RaCA general location (CSV; 145 KB) RaCA SOC pedons (CSV; 796 KB) RaCA data columns (CSV; 7 KB) RaCA download (ZIP; 336 MB)

Maps - Soil Organic Carbon Stocks:

Rapid Carbon Assessment Values Using SSURGO and NLCD Grids (PDF; 7.72 MB) Geometric Means for Each RaCA Region (PDF; 2.82 MB)

Values for Each RaCA Site (PDF; 5.11 MB) Resources in this dataset:Resource Title: Website Pointer to Rapid Carbon Assessment (RaCA). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/research/?cid=nrcs142p2_054164 Includes Introduction, Methodology and Sampling, Data Tables, Maps, Cooperators, Related Data and Information.

Legacy product - no abstract available

An area of broken, fast flowing water in a stream, where the slope of the bed increases (but without a prominent break of slope which might result in a cascade or waterfall), or where a gently dipping bar of harder rock outcrops Data Dictionary for rapid_poly: https://docs.topo.linz.govt.nz/data-dictionary/tdd-class-rapid_poly.html This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

The data in this file represent the order in which stations are placed along a route. The sequence of stops differs by direction and origin-destination. The orders are not necessarily equivalent to GTFS attribute stop_sequence.

Name
Description
Data Type
Example


route_id
GTFS-compatible route of the heavy or light rail line. 
String
Red


direction_id
GTFS-compatible direction. Binary identifier.
Integer
1


origin
Terminal station from which the vehicle begins the trip. 
String
Bowdoin


destination
Terminal station from which the vehicle ends the trip. 
String
Wonderland


stop_order
Order in which the specified stop takes place on the specified route, direction, and origin/destination pair.
Integer
5


stop_id
GTFS-compatible parent station identifier. 
String
place-mvbcl


stop_name
GTFS-compatible parent station name.
String
Maverick

MassDOT/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.

UNOSAT code: CE20220223UKR This map illustrates a satellite imagerybased Rapid Damage Building Assessment (RDBA) in the North of Kharkiv, Ukraine. The RDBA divides the area of interest into 500m x 500m cells, each of which is analyzed to determine whether or not there are damaged buildings inside the cell.

Based on imagery collected on 21, 22 and 23 March 2022, analysts found that 72 cells out of 1,866 cells in the North of Kharkiv sustained visible damage. This represents approximately 4% of the cells over the city.

This analysis is based on structures visibly damaged as of 21, 22 and 23 March 2022 as seen in marginally degraded satellite imagery affected by precipitation, seasonality, and other limiting factors. This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

An area of broken, fast flowing water in a stream, where the slope of the bed increases (but without a prominent break of slope which might result in a cascade or waterfall), or where a gently dipping bar of harder rock outcrops Data Dictionary for rapid_cl: http://apps.linz.govt.nz/topo-data-dictionary/index.aspx?page=class-rapid_cl This layer is a component of the Topo50 map series. The Topo50 map series provides topographic mapping for the New Zealand mainland, Chatham and New Zealand's offshore islands, at 1:50,000 scale. Further information on Topo50: http://www.linz.govt.nz/topography/topo-maps/topo50

Identifying the Transit Spine Network of the Regional Official Plan. ROP Consolidation September 3, 2024.