The expansion of the Wildland-Urban Interface (WUI) highlights the critical need for precise mapping to improve wildfire risk management. A key challenge, however, is the scarcity of high-resolution, nationwide building footprint data. To bridge this gap, we developed a semi-automated, multi-criteria filtering framework designed to enhance the quality of open-source global building datasets—specifically Microsoft’s Global Building Footprints (MSB)—for mainland Portugal.

Our methodology combines regional adaptability with spatial analysis techniques, including area-based thresholds and proximity rules, using Portugal’s official Building Geographic Location Database (BGE) as a reference. To optimize residential representation, the framework iteratively removes non-residential outliers (e.g., industrial facilities, solar farms, transmission infrastructure) through dynamically adjusted thresholds applied across administrative levels (municipalities and NUTS-2 regions). As a result, the filtering process reduced the original dataset from approximately 5.6 million to 3.0 million building footprints.

This dataset provides WUI maps for Mainland Portugal, generated using Microsoft’s Global Building Footprints. The geodatabase include WUI maps, original building footprints, and filtered versions for analysis.

Our WUI maps are composed of 11 classes:

Classification of WUI types:

1 - Intermix

2 - Interface

Classification of building density in non-WUI areas:

3 - Very Low

4 - Low

5 - Medium

6 - High

Classification of Land Cover:

200 - Agriculture

300 - Forest

400 - Shrubland

500 - Without Vegetation

600 - Water

Microsoft Bing Maps consisting of Satellite images, Road Maps, and Address Queries

The global mapping software market is experiencing robust growth, driven by increasing demand across various sectors. While precise figures for market size and CAGR are absent from the provided data, a reasonable estimation can be made based on industry trends. Considering the presence of major players like Adobe, Autodesk, and Microsoft, and the consistent advancements in GIS technology and location-based services, a conservative estimate places the 2025 market size at approximately $15 billion USD. Assuming a steady growth trajectory influenced by factors like increasing adoption of cloud-based solutions, the integration of AI and machine learning for enhanced mapping capabilities, and the growing need for precise location data in logistics, urban planning, and environmental monitoring, a Compound Annual Growth Rate (CAGR) of 8-10% over the forecast period (2025-2033) seems plausible. This would project market values significantly higher by 2033. This growth is fueled by several key trends. The increasing availability of high-resolution satellite imagery and other geospatial data provides richer inputs for mapping applications. Furthermore, the rising adoption of mobile devices equipped with GPS technology and the proliferation of location-based services (LBS) are expanding the market's addressable user base. However, challenges remain, such as the high cost of advanced mapping software and the complexities associated with data integration and management. Nevertheless, the overall market outlook remains positive, with continued expansion anticipated across various segments and geographic regions. The competitive landscape is marked by a mix of established players and emerging startups, leading to innovation and the continuous improvement of mapping technologies.

These data provide an accurate high-resolution shoreline compiled from imagery of GULFPORT, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

Competition Link: https://www.drivendata.org/competitions/81/detect-flood-water/page/385/

Training images

Sentinel-1 radar images stored as GeoTIFFs. Each geographic area includes two microwave frequency readings: VV (vertical transmit, vertical receive) and VH (vertical transmit, horizontal receive).

Training metadata

Metadata for the training images.

Training labels

512 x 512 pixel masks indicating which pixels in a scene contain water. Each set of two polarization bands (VV and VH) corresponds with a single label mask.

These data provide an accurate high-resolution shoreline compiled from imagery of Ship Island, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

This python dictionary maps Microsoft's Census_OSVersion (str) to a timestamp (datetime.datetime). Use this to add the time when a user's OS was last updated.

The Microsoft Malware dataset has 579 unique Census_OSVersion's. This dictionary contains dates for 324 of them which constitutes 99.85% of the Microsoft data.

These timestamps were downloaded from https://support.microsoft.com/en-us/help/4043454/windows-10-windows-server-update-history and https://changewindows.org/build/17134

For a dictionary of timestamps for AvSigVersion, go here https://www.kaggle.com/cdeotte/malware-timestamps

This contains the building damage data described in the manuscript 'A Bayesian Approach for Earthquake Impact Modelling' (available at: https://arxiv.org/abs/2412.15791).The code used to generate the R objects are contained in https://github.com/hamishwp/ODDRIN. It compiles data from several sources including:Global Data Lab: J. Smits and I. Permanyer. The Subnational Human Development Database. Scientific data, 6(1):1–15, 2019.Vs30: D. C. Heath, D. J. Wald, C. B. Worden, E. M. Thompson, and G. M. Smoczyk. A global hybrid VS 30 map with a topographic slope–based default and regional map insets. Earthquake Spectra, 36(3):1570–1584, 2020.Earthquake frequency: K. Johnson, M. Villani, K. Bayliss, C. Brooks, S. Chandrasekhar, T. Chartier, Y. Chen, J. Garcia-Pelaez, R. Gee, R. Styron, A. Rood, M. Simionato, and M. Pagani. Global Earthquake Model (GEM) seismic hazard map (version 2023.1 - June 2023). GEM https://doi.org/10.5281/zenodo.8409647, 2023.Income Inequality: F. Alvaredo, A. B. Atkinson, T. Piketty, and E. Saez. World Inequality Database, 2022. URL http://wid.world/data.Copernicus Building Damage Footprints: Copernicus Emergency Management Service. Copernicus emergency management service - mapping, 2012. URL https://emergency.copernicus.eu/mapping. The European Commission.UNITAR/UNOSAT Building Damage Footprints: UNITAR/UNOSAT. UNITAR’s Operational Satellite Applications Programme – UNOSAT, 2023. URL https://unosat.org/products/.WorldPop Population: A. J. Tatem. WorldPop, open data for spatial demography. Scientific Data, 4(1):1–4, 2017. doi: 10.1038/sdata.2017.4.Bing Building Footprints: Microsoft. Global ML Building Footprints, 2022. URL https://github.com/microsoft/GlobalMLBuildingFootprints. Accessed:2024-06-17.Shakemap: D. J. Wald, B. C. Worden, V. Quitoriano, and K. L. Pankow. ShakeMap manual: Technical manual, user’s guide, and software guide. Technical Report 12-A1, United States Geological Survey, 2005.

These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF PASCAGOULA, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribu...

Discover the booming Customer Journey Mapping Tools market! Our in-depth analysis reveals a $2.5B market in 2025, projected to reach $8B by 2033, fueled by digital transformation and customer-centric strategies. Explore market trends, key players (Microsoft, Gliffy, etc.), and regional insights.

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These data provide an accurate high-resolution shoreline compiled from imagery of PORT OF BILOXI, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute S...

Discover the booming market for Customer Experience (CX) Journey Mapping Tools! Learn about market size, growth projections (CAGR 15%), key drivers, leading companies (Microsoft, IBM, Gliffy, and more), and regional trends shaping this dynamic sector. Explore the future of CX mapping and its impact on customer satisfaction.

The global Business Mapping Software market is experiencing robust growth, driven by the increasing adoption of cloud-based solutions and the expanding need for data visualization across diverse industries. Our analysis projects a market size of $15 billion in 2025, expanding at a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This significant growth is fueled by several key factors. The rise of digital transformation initiatives across sectors like healthcare, finance, and manufacturing is creating a heightened demand for efficient data visualization tools. Businesses are increasingly relying on business mapping software to understand geographical patterns, optimize supply chains, analyze market trends, and improve operational efficiency. Furthermore, advancements in Artificial Intelligence (AI) and Machine Learning (ML) are enhancing the capabilities of these platforms, making them more insightful and user-friendly. The prevalence of cloud-based solutions offers scalability, accessibility, and cost-effectiveness, contributing significantly to market expansion. While data security concerns and the need for specialized training can act as restraints, the overall market outlook remains highly positive. The market segmentation highlights the strong demand across various application sectors. Healthcare is a particularly lucrative segment, leveraging the software for efficient resource allocation, patient management, and epidemiological studies. The automotive industry uses it for supply chain optimization and logistics management. Similarly, banking, financial services, and manufacturing benefit from improved risk assessment, market analysis, and operational optimization. The competitive landscape is dynamic, featuring both established tech giants like Microsoft and IBM, and specialized providers like Caliper and eSpatial. Geographic expansion, particularly in rapidly developing economies in Asia-Pacific, presents significant growth opportunities. This suggests the market will continue its upward trajectory, driven by technological advancements, increasing digitalization across industries, and a global demand for enhanced data visualization and analysis capabilities.

These data provide an accurate high-resolution shoreline compiled from imagery of ST LOUIS BAY, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

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Microsoft Excel-based computer module for continuous item responses. (XLSM 2705 kb)

The Digital Flood Insurance Rate Map (DFIRM) Database depicts flood risk information and supporting data used to develop the risk data. The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual- chance flood event, and areas of minimal flood risk. The DFIRM Database is derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data, where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). In addition to the preceding, required text, the Abstract should also describe the projection and coordinate system as well as a general statement about horizontal accuracy.

The geographic data are built from the Technical Information Management System (TIMS). TIMS consists of two separate databases: an attribute database and a spatial database. The attribute information for offshore activities is stored in the TIMS database. The spatial database is a combination of the ARC/INFO and FINDER databases and contains all the coordinates and topology information for geographic features. The attribute and spatial databases are interconnected through the use of common data elements in both databases, thereby creating the spatial datasets. The data in the mapping files are made up of straight-line segments. If an arc existed in the original data, it has been replaced with a series of straight lines that approximate the arc. The Gulf of America OCS Region stores all its mapping data in longitude and latitude format. All coordinates are in NAD 27. Data can be obtained in three types of digital formats: INTERACTIVE MAP: The ArcGIS web maps are an interactive display of geographic information, containing a basemap, a set of data layers (many of which include interactive pop-up windows with information about the data), an extent, navigation tools to pan and zoom, and additional tools for geospatial analysis. SHP: A Shapefile is a digital vector (non-topological) storage format for storing geometric location and associated attribute information. Shapefiles can support point, line, and area features with attributes held in a dBASE format file. GEODATABASE: An ArcGIS geodatabase is a collection of geographic datasets of various types held in a common file system folder, a Microsoft Access database, or a multiuser relational DBMS (such as Oracle, Microsoft SQL Server, PostgreSQL, Informix, or IBM DB2). The geodatabase is the native data structure for ArcGIS and is the primary data format used for editing and data management.

These data provide an accurate high-resolution shoreline compiled from imagery of Mississippi Sound, Biloxi Bay to Pascagoula, MS . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808