This dataset contains projections of shoreline change and uncertainty bands for future scenarios of sea-level rise (SLR). Scenarios include 25, 50, 75, 100, 150, 200, and 300 centimeters (cm) of SLR by the year 2100. Output for SLR of 0 cm is also included, reflective of conditions in 2005, in accordance with recent SLR projections and guidance from the National Oceanic and Atmospheric Administration (NOAA; see process steps).Projections were made using the Coastal Storm Modeling System - Coastal One-line Assimilated Simulation Tool (CoSMoS-COAST), a numerical model (described in Vitousek and others, 2017; 2021; 2023) run in an ensemble forced with global-to-local nested wave models and assimilated with satellite-derived shoreline (SDS) observations. Shoreline positions from models are generated at pre-determined cross-shore transects and output includes different cases covering important model behaviors (cases are described in process steps of metadata; see citations listed in the Cross References section for more details on the methodology and supporting information). This model shows change in shoreline positions along transects, considering sea level, wave conditions, along-shore/cross-shore sediment transport, long-term trends due to sediment supply, and estimated variability due to unresolved processes (as described in Vitousek and others, 2021). Variability associated with complex coastal processes (for example, beach cusps/undulations and shore-attached sandbars) are included via a noise parameter in a model, which is tuned using observations of shoreline change at each transect and run in an ensemble of 200 simulations; this approach allows for a representation of statistical variability in a model that is assimilated with sequences of noisy observations. The model synthesizes and improves upon numerous, well-established shoreline models in the scientific literature; processes and methods are described in this metadata (see lineage and process steps), but also described in more detail in Vitousek and others 2017, 2021, and 2023. KMZ data are readily viewable in Google Earth. For best display of results, it is recommended to turn off any 3D features or terrain. For technical users and researchers, shapefile and KMZ data can be ingested into geographic information system (GIS) software such as Global Mapper or QGIS.

The United States geospatial analytics market is experiencing robust growth, projected to reach a significant size within the forecast period (2025-2033). The market's Compound Annual Growth Rate (CAGR) of 10.04% from 2019-2033 indicates a consistently expanding demand for geospatial data analysis across diverse sectors. Key drivers include the increasing availability of high-resolution satellite imagery, advancements in data processing capabilities (cloud computing, AI), and the growing need for data-driven decision-making in various industries. Specific sectors like agriculture, utilizing geospatial analytics for precision farming, and the defense and intelligence sectors, leveraging it for surveillance and strategic planning, are major contributors to market growth. Further fueling expansion are trends like the rising adoption of Internet of Things (IoT) devices generating location-based data, and the increasing sophistication of geospatial analytics software, incorporating advanced visualization and predictive modeling techniques. While data security concerns and the high cost of implementation pose some restraints, the overall market outlook remains positive, driven by the substantial benefits offered by geospatial analytics in improving efficiency, optimizing resource allocation, and enhancing situational awareness across a wide spectrum of applications. The market segmentation reveals significant opportunities across different types of geospatial analytics (surface analysis, network analysis, and geovisualization) and end-user verticals. While the provided data indicates a significant presence of companies like Harris Corporation, Bentley Systems Inc., and ESRI Inc., the market's competitive landscape is dynamic, with both established players and emerging technology companies vying for market share. The United States' dominance in geospatial technology and data infrastructure further supports the market's projected growth trajectory. The substantial investments in R&D and the prevalence of skilled professionals in the country further contribute to the market's expansion. Looking ahead, the integration of geospatial analytics with other technologies like blockchain and big data is expected to unlock new possibilities, further driving market growth and innovation in the coming years. Recent developments include: May 2023 : CAPE Analytics, a player in AI-powered geospatial property intelligence, has extended its partnership with The Hanover Insurance Group, which provides independent agents with the best insurance coverage and prices. Integrating geospatial analytics and inspection and rating models into Hanover's underwriting procedure is the central component of the partnership expansion. The company's rating plans will benefit from this strategic move, which will improve workflows, new and renewal underwriting outcomes, and pricing segmentation., March 2023 : Carahsoft Technology Corp., The Trusted Government IT Solutions Provider, and Orbital Insight, a player in geospatial intelligence, announced a partnership. By the terms of the agreement, Carahsoft will act as Orbital Insight's Master Government Aggregator, making the leading AI-powered geospatial data analytics available to the public sector through Carahsoft's reseller partners and contracts for Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO) ValuePoint, National Cooperative Purchasing.. Key drivers for this market are: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Potential restraints include: Increasing in Demand for Location Intelligence, Advancements of Big Data Analytics. Notable trends are: Network Analysis is Expected to Hold Significant Share of the Market.

The US geospatial imagery analytics market is experiencing robust growth, fueled by increasing adoption across diverse sectors. The global market's substantial size of $5.38 billion in 2025 and a Compound Annual Growth Rate (CAGR) of 24.14% project significant expansion through 2033. While precise figures for the US market segment are unavailable, a reasonable estimation, considering the US's significant technological advancement and market dominance in related fields, would place its 2025 market size at approximately $2.0 billion. This substantial value is driven by several key factors. The rising demand for precise location intelligence across various sectors such as insurance (risk assessment and fraud detection), agriculture (precision farming and yield optimization), defense and security (surveillance and intelligence gathering), and environmental monitoring (disaster management and climate change analysis) are primary growth catalysts. Technological advancements like improved sensor technologies, enhanced image processing algorithms, and the proliferation of cloud-based solutions further accelerate market expansion. The increasing availability of high-resolution satellite imagery and the development of sophisticated analytics platforms are also contributing to the market's growth trajectory. However, the market faces certain restraints. High initial investment costs for implementing geospatial imagery analytics solutions, especially for SMEs, can pose a barrier to entry. Moreover, concerns regarding data privacy and security, along with the complexity of data analysis and interpretation, can hinder wider adoption. Despite these challenges, the long-term outlook remains positive. The continuous development of user-friendly software, the decreasing cost of data storage and processing, and growing government initiatives promoting the use of geospatial technologies are expected to mitigate these limitations and propel the market toward sustained growth. The market segmentation by deployment (on-premise and cloud), organization size (SMEs and large enterprises), and vertical industries presents diverse opportunities for growth and specialization within the US market. The competitive landscape is characterized by a mix of established technology giants and specialized geospatial analytics providers, each vying for a share of this rapidly expanding market. Recent developments include: May 2023: CAPE Analytics, a player in AI-powered geospatial property intelligence, has extended its partnership with The Hanover Insurance Group, which provides independent agents with the best insurance coverage and prices. Integrating geospatial analytics and inspection and rating models into Hanover's underwriting procedure is the central component of the partnership expansion. The company's rating plans will benefit from this strategic move, improving workflows, new and renewal underwriting outcomes, and pricing segmentation., March 2023 : Carahsoft Technology Corp., The Trusted Government IT Solutions Provider, and Orbital Insight, a player in geospatial intelligence, announced a partnership. By the terms of the agreement, Carahsoft will act as Orbital Insight's Master Government Aggregator, making the leading AI-powered geospatial data analytics available to the public sector through Carahsoft's reseller partners and contracts for Information Technology Enterprise Solutions - Software 2 (ITES-SW2), NASA Solutions for Enterprise-Wide Procurement (SEWP) V, National Association of State Procurement Officials (NASPO) ValuePoint, National Cooperative Purchasing.. Key drivers for this market are: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Potential restraints include: Increasing demand for Location based services, Technological innovations in geospatial imagery services. Notable trends are: Small Satellities will Boost Market Growth.

The data illustrates the “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2010 census. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)

FIELD NAME

DESCRIPTION

Name

Short name of the municipality (Lansing)

Label

The municipalities full name (City of Lansing)

Type

The type of municipality (city, township, or village)

SQMILEArea of the shape in Square Miles

ACRES

Area of the shape in Acres

Published in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

This dataset contains summary data regarding historical (1930s-40s) land use and land-use change between 1930s and 2007 according to broad land-use categories. Data provided are summary values at the 10-km grid square 'hectad' level of the British National Grid, specifying the proportion and proportion of change in broad land-use categories.

Historical data are based on the first Land Utilisation Survey of Great Britain (Stamp 1931). For England and Wales, digitisation of the historical maps contains information supplied by Natural England, based on methods developed by Baily et al. (2011). For Scotland, map images were digitised using the R package HistMapR (Auffret et al. 2017). Both methods involve processing and classifying images based on the colour of the historical land-use map categories. Classified maps were then resampled to the 25m resolution of the modern UK Land Cover Map 2007 (Morton et al. 2011), and both historical and modern land-use categories were adjusted to produce broad categories of equivalent land use: Arable, Grassland, Urban, Woodland, Agriculturally-Improved Grassland and Surface Water. In Scotland, surface water from a modern map is used for the historical time period due to issues in classifying this category. Pixels within a 75m buffer of the modern road network were removed due to the disproportionate size of roads shown in the historical maps, and pixels falling into some coastal land-use categories in the modern maps were removed due to a lack of equivalent in the historical maps. The proportions of remaining pixels within each hectad, and the change in the proportion over time was then calculated. Full details of data creation and processing can be found in Suggitt et al. (2023), and more information on the data files can be found in the readme.

The extent of the data files: GB_LandUseChange_Data.csv - table containing summary data, 2802 rows and 15 columns GB_LandUseChange_LowlandGrasslandChange.csv - table containing data on lowland grassland change, 2802 rows and 10 columns

The file GB_LandUseChange_Raster.tif is a GeoTIFF file primarily intended to be used with the R script. It can also be opened using other GIS software.

If R is installed with required packages (see sessionInfo.txt), the file Rplots.pdf can be generated running: Rscript GB_LandUseChange_Code.R

References:

Auffret, A.G., Kimberley, A., Plue, J., Skånes, H., Jakobsson, S., Waldén, E., Wennbom, M., Wood, H., Bullock, J.M., Cousins, S.A.O., Gartz, M., Hooftman, D.A.P., Tränk, L., 2017, HistMapR: Rapid digitization of historical land-use maps in R, Methods in Ecology and Evolution 8: 1453-1457. https://doi.org/10.1111/2041-210X.12788

Baily, B., Riley, M., Aucott, P. & Southall, H., 2011, Extracting digital data from the First Land Utilisation Survey of Great Britain – Methods, issues and potential, Applied Geography 31: 959-968. https://doi.org/10.1016/j.apgeog.2010.12.007

Morton, D., Rowland, C., Wood, C., Meek, L., Marston, C., Smith, G., Wadsworth, R., Simpson, I.C., 2011, Final Report for LCM2007 – the new UK Land Cover Map, Centre for Ecology & Hydrology, Wallingford, UK. http://nora.nerc.ac.uk/id/eprint/14854

Stamp, D.L., 1931, The Land Utilisation Survey of Britain. Geographical Journal 78: 40-47. https://doi.org/10.2307/1784994

Suggitt, A.J., Wheatley, C.J., Aucott, P., Beale, C.M., Fox, R., Hill, J.K., Isaac, N.J.B., Martay, B., Southall, H., Thomas, C.D., Walker, K.J., Auffret, A.G., 2023, Linking climate warming and land conversion to species’ range changes across Great Britain, Nature Communications, https://doi.org/10.1038/s41467-023-42475-0

The data illustrates the expanded “Urbanized Area” for the Municipal Separate Storm Sewer System (MS4) program from the 2020 census data. "Urbanized area" means a place and the adjacent densely populated territory that together have a minimum population of 50,000 people, as defined by the United States bureau of the census and as determined by the latest available decennial census. The data is provided to the Michigan Department of Environment, Great Lakes, and Energy (EGLE) by the United States Environmental Protection Agency. The urbanized area is the regulated area for municipalities that are regulated under the MS4 program, including but not limited to cities, township, and villages."2020 Census Populations of 50K or more" and "Automatically Designated Areas" was provided by US EPA in July 2023 and combined with Michigan Open GIS Data (Minor Civil Divisions: Cities, Townships and Villages) using ESRI's ArcGIS Pro Software. Tools used include Pairwise Intersect, Merge, Pairwise Erase, and manual editing to combine the two layers.Please contact the individuals below with any questions.Christe Alwin: ALWINC@michigan.gov (point of contact)Patrick Klein: kleinp3@michigan.gov (creator)FIELD NAMEDESCRIPTIONNameShort name of the municipality (Lansing)LabelThe municipalities full name (City of Lansing)TypeThe type of municipality (city, township, or village)SQMILEArea of the shape in Square MilesACRESArea of the shape in AcresPublished in June 2024. Learn more about EGLE's Municipal Storm Water Program.Additional information describing Part 21 Wastewater Discharge Permits.

A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included. The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. http://creativecommons.org/licenses/by/4.0 Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland. This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset. Open Data Bulk Data Downloads (version date: 23/08/2023) The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. ERSI Shapefiles of the SMR points and SMRZone polygons are also available The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. GIS Web Service APIs (live views): For users with access to GIS software please note that the Archaeological Survey of Ireland data is also available spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below: SMR; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMROpenData/FeatureServer SMRZone; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMRZoneOpenData/FeatureServer Historic Environment Viewer - Query Tool The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf

This dataset contains the polygons of automatic ( PL) and manually (ML) based shallow landslides related to two extreme rainfall events. In KML format, the dataset can be visualized on GIS software or Google Earth. With more details, it is possible to find:

AOI_2016: The study area of the extreme rainfall of November 2016, Tanerello and Arroscia Valleys NW Italy. The 2016_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in November 2016 The 2016_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in November 2016 AOI_2019_large: The study area of the extreme rainfall of October 2019 NW Italy. AOI_2019: The testing area of the extreme rainfall of October 2019, Gavi Area NW Italy. The 2019_PL_all: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (whole Study area) The 2019_PL: The inventory of potential shallow landslides semi-automatically mapped on the base of Sentinel-2 images related to extreme rainfall events that hit NW Italy in October 2019 (Gavi test area) The 2019_ML: The inventory of shallow landslides manually mapped on high-resolution images of Google Earth, related to extreme rainfall events that hit NW Italy in October 2019 GEE_Script: A list of codes used in Google Earth Engine to produce NDVI time series or averaged NDVI on some sample studied areas are reported in the attached PDF. The code may be pasted and copied to the Google Earth Engine console. The codes (if an account on Google Earth Engine is active) may be reached directly from the following URLs: Script 1. NDVI time series of some sampled areas to select the best pair of images for the PL creation (Tanarello and Arroscia Valley and GAVI AOIs; Fig. 16 of the paper). Link to GEE: https://code.earthengine.google.com/998af951fcb74519589bf8e722bb30b0?noload=true Script 2. sampled NDVI time series from different intersection cases for the Tanarello and Arroscia Valley study area (2016 Event). Link to GEE: https://code.earthengine.google.com/b622cb64f90771ced78ef73bad9cc50f?noload=true Script 3. Sampled NDVI time series from different land-use cases for the Gavi study area (2019 Event). Link to GEE: https://code.earthengine.google.com/f686c60b78a3dee0b2a2c94a259ccff2?noload=true Script 4. Multi-temporal-averaged NDVIvar Link to GEE Script: https://code.earthengine.google.com/bfc2e570bb675372c4c482eef682be4a?noload=true for the whole Gavi study area (2019 flood) and https://code.earthengine.google.com/a3390b262cef1b5f42837c88d8791b5b?noload=true for the entire Arroscia-Tanarello study area The full description of the methodology can be found in the paper of Notti et al., 2023 Notti, D., Cignetti, M., Godone, D., and Giordan, D.: Semi-automatic mapping of shallow landslides using free Sentinel-2 images and Google Earth Engine, Nat. Hazards Earth Syst. Sci., 23, 2625–2648, https://doi.org/10.5194/nhess-23-2625-2023, 2023