Achieve precision in geospatial mapping with accurate data labeling. Enhance navigation, planning, and location-based services.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. were derived from the NVC. NatureServe developed a preliminary list of potential vegetation types. These data were combined with existing plot data (Cully 2002) to derive an initial list of potential types. Additional data and information were gleaned from a field visit and incorporated into the final list of map units. Because of the park’s small size and the large amount of field data, the map units are equivalent to existing vegetation associations or local associations/descriptions (e.g., Prairie Dog Colony). In addition to vegetation type, vegetation structures were described using three attributes: height, coverage density, and coverage pattern. In addition to vegetation structure and context, a number of attributes for each polygon were stored in the associated table within the GIS database. Many of these attributes were derived from the photointerpretation; others were calculated or crosswalked from other classifications. Table 2.7.2 shows all of the attributes and their sources. Anderson Level 1 and 2 codes are also included (Anderson et al. 1976). These codes should allow for a more regional perspective on the vegetation types. Look-up tables for the names associated with the codes is included within the geodatabase and in Appendix D. The look-up tables contain all the NVC formation information as well as alliance names, unique IDs, and the ecological system codes (El_Code) for the associations. These El_Codes often represent a one-to-many relationship; that is, one association may be related to more than one ecological system. The NatureServe conservation status is included as a separate item. Finally, slope (degrees), aspect, and elevation were calculated for each polygon label point using a digital elevation model and an ArcView script. The slope figure will vary if one uses a TIN (triangulated irregular network) versus a GRID (grid-referenced information display) for the calculation (Jenness 2005). A grid was used for the slope figure in this dataset. Acres and hectares were calculated using XTools Pro for ArcGIS Desktop.

Damper Labeling and GIS Mapping Market Outlook

According to our latest research, the global damper labeling and GIS mapping market size reached USD 1.13 billion in 2024, with a robust growth trajectory driven by the increasing integration of digital solutions in building management and infrastructure development. The market is currently expanding at a CAGR of 8.2% and is forecasted to achieve a value of USD 2.22 billion by 2033. This growth is primarily attributed to the surging demand for precise asset tracking, enhanced regulatory compliance, and the adoption of advanced Geographic Information System (GIS) technologies across various industrial and commercial sectors.

One of the primary growth factors propelling the damper labeling and GIS mapping market is the intensifying focus on building automation and smart infrastructure development. As cities worldwide embrace smart building initiatives, the need for accurate damper labeling and real-time GIS mapping becomes indispensable for efficient facility management and safety compliance. Modern HVAC systems, fire safety mechanisms, and industrial ventilation systems rely heavily on precise damper identification and location tracking. This digital transformation is further supported by stringent regulatory frameworks that mandate clear asset labeling and documentation, ensuring safety and operational efficiency. The integration of IoT and AI-driven analytics within GIS mapping platforms is also enhancing operational visibility, thereby reducing maintenance costs and downtime.

Additionally, the rising adoption of cloud-based solutions is significantly influencing market dynamics. Cloud deployment offers scalability, remote accessibility, and seamless data sharing, which are crucial for large-scale commercial and industrial projects. Organizations are increasingly leveraging cloud-enabled GIS mapping to centralize asset data, streamline workflows, and facilitate real-time collaboration among stakeholders. This shift is particularly valuable in multi-site operations, where centralized control and standardized labeling protocols are essential for regulatory compliance and effective risk management. As a result, service providers are investing heavily in cloud infrastructure and cybersecurity, which is expected to further accelerate market growth.

Another compelling driver for the damper labeling and GIS mapping market is the growing emphasis on fire safety and disaster preparedness. With the escalation of fire incidents in commercial and industrial facilities, regulatory bodies are enforcing stricter codes for damper identification and maintenance. GIS mapping, when integrated with advanced labeling systems, provides a comprehensive overview of damper locations, enabling swift response during emergencies. This capability is particularly critical for large-scale facilities such as hospitals, educational institutions, and manufacturing plants, where rapid evacuation and risk mitigation are paramount. Furthermore, the ongoing trend of retrofitting aging infrastructure with modern labeling and mapping solutions is opening new avenues for market expansion, as facility managers seek to enhance safety and operational transparency.

From a regional perspective, North America continues to dominate the damper labeling and GIS mapping market, owing to its early adoption of advanced building automation technologies and stringent regulatory standards. The presence of leading technology providers, coupled with significant investments in smart city projects, is fostering innovation and market penetration. In contrast, the Asia Pacific region is witnessing the fastest growth, driven by rapid urbanization, infrastructure modernization, and government-led initiatives to enhance building safety and energy efficiency. Europe, with its mature construction sector and strong focus on sustainability, is also contributing significantly to market development. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, fueled by increasing awareness of safety regulations and the adoption of digital asset management practices.

Component Analysis

The damper labeling and GIS mapping market is segmented by component into software, hardware, and services, each playing a pivotal role in the overall ecosystem. The software segment is experiencing substantial growth, driven by the increasing demand for advanced GIS platforms that offer real-time data visualization, asset tr

Dataset Information

Note that this is the full week of data that was sampled from Twitter. The 10,005,301 count mentioned in the introductory paper below refers to the weekday portion of the data (i.e., Monday through Friday). If you remove Saturday (Jan 12, 2013) and Sunday (Jan 13, 2013), then you will get the Monday through Friday portion that was analyzed in the paper. Has Missing Values? No

Dataset Characteristics Multivariate, Time-Series, Spatiotemporal Subject Area Social Science Associated Tasks Classification, Regression, Clustering

Variable Information This dataset contains geospatial and timestamp data for one week worth of Tweets in the contiguous United States. The Tweets were created between January 12, 2013 and January 18, 2013. The exact location (i.e., longitude and latitude) and timestamp (hour, minute, second) of each Tweet was recorded. All timestamps are reported in central standard time in the format "YYYY-MM-DD HH:MM:SS". The geo-tag information was used to assign each Tweet to one of the four standard time zones (for details see Helwig et al., 2015). The data were collected by the CyberGIS Center for Advanced Digital and Spatial Studies at the University of Illinois at Urbana-Champaign. Details on the data preprocessing and analysis can be found in Helwig et al. (2015). Class Labels 1. longitude: exact longitude coordinate of Tweet (real valued) 2. latitude: exact latitude coordinate of Tweet (real valued) 3. timestamp: 20130112000000 = 2013-01-12 00:00:00 CST (integer) 4. timezone: 1 = Eastern, 2 = Central, 3 = Mountain, 4 = Pacific (integer)

Overview

The Office of the Geographer and Global Issues at the U.S. Department of State produces the Large Scale International Boundaries (LSIB) dataset. The current edition is version 11.4 (published 24 February 2025). The 11.4 release contains updated boundary lines and data refinements designed to extend the functionality of the dataset. These data and generalized derivatives are the only international boundary lines approved for U.S. Government use. The contents of this dataset reflect U.S. Government policy on international boundary alignment, political recognition, and dispute status. They do not necessarily reflect de facto limits of control.

National Geospatial Data Asset

This dataset is a National Geospatial Data Asset (NGDAID 194) managed by the Department of State. It is a part of the International Boundaries Theme created by the Federal Geographic Data Committee.

Dataset Source Details

Sources for these data include treaties, relevant maps, and data from boundary commissions, as well as national mapping agencies. Where available and applicable, the dataset incorporates information from courts, tribunals, and international arbitrations. The research and recovery process includes analysis of satellite imagery and elevation data. Due to the limitations of source materials and processing techniques, most lines are within 100 meters of their true position on the ground.

Cartographic Visualization

The LSIB is a geospatial dataset that, when used for cartographic purposes, requires additional styling. The LSIB download package contains example style files for commonly used software applications. The attribute table also contains embedded information to guide the cartographic representation. Additional discussion of these considerations can be found in the Use of Core Attributes in Cartographic Visualization section below.

Additional cartographic information pertaining to the depiction and description of international boundaries or areas of special sovereignty can be found in Guidance Bulletins published by the Office of the Geographer and Global Issues: https://data.geodata.state.gov/guidance/index.html

Contact

Direct inquiries to internationalboundaries@state.gov. Direct download: https://data.geodata.state.gov/LSIB.zip

Attribute Structure

The dataset uses the following attributes divided into two categories: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | Core CC1_GENC3 | Extension CC1_WPID | Extension COUNTRY1 | Core CC2 | Core CC2_GENC3 | Extension CC2_WPID | Extension COUNTRY2 | Core RANK | Core LABEL | Core STATUS | Core NOTES | Core LSIB_ID | Extension ANTECIDS | Extension PREVIDS | Extension PARENTID | Extension PARENTSEG | Extension

These attributes have external data sources that update separately from the LSIB: ATTRIBUTE NAME | ATTRIBUTE STATUS CC1 | GENC CC1_GENC3 | GENC CC1_WPID | World Polygons COUNTRY1 | DoS Lists CC2 | GENC CC2_GENC3 | GENC CC2_WPID | World Polygons COUNTRY2 | DoS Lists LSIB_ID | BASE ANTECIDS | BASE PREVIDS | BASE PARENTID | BASE PARENTSEG | BASE

The core attributes listed above describe the boundary lines contained within the LSIB dataset. Removal of core attributes from the dataset will change the meaning of the lines. An attribute status of “Extension” represents a field containing data interoperability information. Other attributes not listed above include “FID”, “Shape_length” and “Shape.” These are components of the shapefile format and do not form an intrinsic part of the LSIB.

Core Attributes

The eight core attributes listed above contain unique information which, when combined with the line geometry, comprise the LSIB dataset. These Core Attributes are further divided into Country Code and Name Fields and Descriptive Fields.

County Code and Country Name Fields

“CC1” and “CC2” fields are machine readable fields that contain political entity codes. These are two-character codes derived from the Geopolitical Entities, Names, and Codes Standard (GENC), Edition 3 Update 18. “CC1_GENC3” and “CC2_GENC3” fields contain the corresponding three-character GENC codes and are extension attributes discussed below. The codes “Q2” or “QX2” denote a line in the LSIB representing a boundary associated with areas not contained within the GENC standard.

The “COUNTRY1” and “COUNTRY2” fields contain the names of corresponding political entities. These fields contain names approved by the U.S. Board on Geographic Names (BGN) as incorporated in the ‘"Independent States in the World" and "Dependencies and Areas of Special Sovereignty" lists maintained by the Department of State. To ensure maximum compatibility, names are presented without diacritics and certain names are rendered using common cartographic abbreviations. Names for lines associated with the code "Q2" are descriptive and not necessarily BGN-approved. Names rendered in all CAPITAL LETTERS denote independent states. Names rendered in normal text represent dependencies, areas of special sovereignty, or are otherwise presented for the convenience of the user.

Descriptive Fields

The following text fields are a part of the core attributes of the LSIB dataset and do not update from external sources. They provide additional information about each of the lines and are as follows: ATTRIBUTE NAME | CONTAINS NULLS RANK | No STATUS | No LABEL | Yes NOTES | Yes

Neither the "RANK" nor "STATUS" fields contain null values; the "LABEL" and "NOTES" fields do. The "RANK" field is a numeric expression of the "STATUS" field. Combined with the line geometry, these fields encode the views of the United States Government on the political status of the boundary line.

ATTRIBUTE NAME | | VALUE | RANK | 1 | 2 | 3 STATUS | International Boundary | Other Line of International Separation | Special Line

A value of “1” in the “RANK” field corresponds to an "International Boundary" value in the “STATUS” field. Values of ”2” and “3” correspond to “Other Line of International Separation” and “Special Line,” respectively.

The “LABEL” field contains required text to describe the line segment on all finished cartographic products, including but not limited to print and interactive maps.

The “NOTES” field contains an explanation of special circumstances modifying the lines. This information can pertain to the origins of the boundary lines, limitations regarding the purpose of the lines, or the original source of the line.

Use of Core Attributes in Cartographic Visualization

Several of the Core Attributes provide information required for the proper cartographic representation of the LSIB dataset. The cartographic usage of the LSIB requires a visual differentiation between the three categories of boundary lines. Specifically, this differentiation must be between:

• International Boundaries (Rank 1);
• Other Lines of International Separation (Rank 2); and
• Special Lines (Rank 3).

Rank 1 lines must be the most visually prominent. Rank 2 lines must be less visually prominent than Rank 1 lines. Rank 3 lines must be shown in a manner visually subordinate to Ranks 1 and 2. Where scale permits, Rank 2 and 3 lines must be labeled in accordance with the “Label” field. Data marked with a Rank 2 or 3 designation does not necessarily correspond to a disputed boundary. Please consult the style files in the download package for examples of this depiction.

The requirement to incorporate the contents of the "LABEL" field on cartographic products is scale dependent. If a label is legible at the scale of a given static product, a proper use of this dataset would encourage the application of that label. Using the contents of the "COUNTRY1" and "COUNTRY2" fields in the generation of a line segment label is not required. The "STATUS" field contains the preferred description for the three LSIB line types when they are incorporated into a map legend but is otherwise not to be used for labeling.

Use of

The cadastral overview map (KUEK5) is a geospatial database specially developed for Dresden (basic map) and maps the urban area of the state capital Dresden with the help of selected, partly generalized data from the official real estate cadastre information system (ALKIS) on a scale of 1:5,000.

Representation of selected inscriptions on buildings from a size of 15 m2 in the urban area of the state capital Dresden.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. A draft hard copy vegetation map at the 1:12,000 scale was printed and checked against the interpreted aerial photographs. As a final internal accuracy check, we applied photointerpretative observations and classification relevés over the vegetation map to determine if the polygon labels matched the field data. Finally, map validation occurred prior to the accuracy assessment. Staff from RSGIG conducted a field trip in conjunction with other meetings in Flagstaff, AZ in January 2001 to refine and assess the initial mapping effort. On this trip we collected additional photointerpretative observations and ground-truthed aerial photograph signatures using landmarks and GPS waypoints. Map classes were lumped or split to account for inadequacies in the final photointerpretation. Metadata are required for all spatial data produced by the federal government. RSGIG used SIMMS™ software and CPRS used ArcCatalogue software to create the FGDC-compliant metadata files attached to the spatial databases and to this report (see Appendix A). The metadata files explain the vegetation coverage and ancillary coverages created by RSGIG, the classification relevé data coverage created by CPRS, and the accuracy assessment observation data created by CPRS.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The transfer process for the CHCU vegetation mapping project involved taking the interpreted line work and rendering it into a comprehensive digital network of attributed polygons. To accomplish this, we created an ArcInfo© GIS database using in-house protocols. The protocols consist of a shell (master file) of Arc Macro Language (AML) scripts and menus (nearly 100 files) that automate the transfer process, thus insuring that all spatial and attribute data are consistent and stored properly. The actual transfer of information from the interpreted orthophotos to a digital, geo-referenced format involved scanning, rasterizing, vectorizing, cleaning, building topology, and labeling each polygon.

Convolutional neural network (CNN)-based deep learning (DL) methods have transformed the analysis of geospatial, Earth observation, and geophysical data due to their ability to model spatial context information at multiple scales. Such methods are especially applicable to pixel-level classification or semantic segmentation tasks. A variety of R packages have been developed for processing and analyzing geospatial data. However, there are currently no packages available for implementing geospatial DL in the R language and data science environment. This paper introduces the geodl R package, which supports pixel-level classification applied to a wide range of geospatial or Earth science data that can be represented as multidimensional arrays where each channel or band holds a predictor variable. geodl is built on the torch package, which supports the implementation of DL using the R and C++ languages without the need for installing a Python/PyTorch environment. This greatly simplifies the software environment needed to implement DL in R. Using geodl, geospatial raster-based data with varying numbers of bands, spatial resolutions, and coordinate reference systems are read and processed using the terra package, which makes use of C++ and allows for processing raster grids that are too large to fit into memory. Training loops are implemented with the luz package. The geodl package provides utility functions for creating raster masks or labels from vector-based geospatial data and image chips and associated masks from larger files and extents. It also defines a torch dataset subclass for geospatial data for use with torch dataloaders. UNet-based models are provided with a variety of optional ancillary modules or modifications. Common assessment metrics (i.e., overall accuracy, class-level recalls or producer’s accuracies, class-level precisions or user’s accuracies, and class-level F1-scores) are implemented along with a modified version of the unified focal loss framework, which allows for defining a variety of loss metrics using one consistent implementation and set of hyperparameters. Users can assess models using standard geospatial and remote sensing metrics and methods and use trained models to predict to large spatial extents. This paper introduces the geodl workflow, design philosophy, and goals for future development.

This data set replaces the 2010 edition (Edition 1.0) of the 2005 Land Cover of North America. Following the release of the first 2005 land cover data, several errors were identified in the data, including both errors in labeling and misinterpretation of thematic classes. To correct the labeling errors, each country focused on its national territory and corrected the errors which it considered most critical or misleading. For the continental data sets (including surrounding water fringe) 17440830 pixels (4.33% of the area) changed in the update. The following national counts exclude the water fringe: Canada, 10223412 pixels changed (6.44%); Mexico, 141142 pixels changed (0.45%), and U.S., 6878656 pixels changed (4.54%). The countries worked together to produce a definitive list of land cover classifications for the 2005 data; this document is available for download from the same site as the data and is entitled: North American Land Cover Classifications (2005). Version 1 of the 2005 North American Land Cover data set was produced as part of the North American Land Change Monitoring System (NALCMS), a trilateral effort between the Canada Centre for Remote Sensing, the United States Geological Survey, and three Mexican organizations including the National Institute of Statistics and Geography (Instituto Nacional de Estadistica y Geografia), National Commission for the Knowledge and Use of the Biodiversity (Comisión Nacional Para el Conocimiento y Uso de la Biodiversidad) and the National Forestry Commission of Mexico (Comisión Nacional Forestal). The collaboration is facilitated by the Commission for Environmental Cooperation, an international organization created by the Canada, Mexico, and United States governments under the North American Agreement on Environmental Cooperation to promote environmental collaboration between the three countries. The general objective of NALCMS is to devise, through collective effort, a harmonized multi-scale land cover monitoring approach which ensures high accuracy and consistency in monitoring land cover changes at the North American scale and which meets each country’s specific requirements. The data set of 2005 Land Cover of North America at a resolution of 250 meters is the first step toward this goal. The initial data set used to generate land cover information over North America was produced by the Canada Centre for Remote Sensing from observations acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS/Terra). All seven land spectral bands were processed from Level 1 granules into top-of-atmosphere reflectance covering North America at a 250-meter spatial and 10-day temporal resolution. In order to generate a seamless and consistent land cover map of North America, national maps were generated for Canada by the CCRS; for Mexico by INEGI, CONABIO, and CONAFOR; and for the United States by the USGS. Each country used specific training data and land cover mapping methodologies to create national data sets. This North America data set was produced by combining the national land cover data sets.

Annotation

GIS data: neighborhood labels as depicted in New York City: A City of Neighborhoods.

All previously released versions of this data are available at BYTES of the BIG APPLE- Archive

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Instrumental to the photo interpretive effort was the use of the GPS located vegetation plots collected by the field crew. These plots provided an idea of what the signatures of the individual map units should look like. In addition to the tablular data associated with each vegetation plot were five photographs collected at each plot. These photographs helped not only in identifying the immediate area but also provided us with a “look” at the areas surrounding the vegetation plot which might be a different map unit. These photographs may be “hyperlinked” within ArcMap to the salient vegetation observation point for a better concept of on the ground conditions.All interpreted mylar layers were scanned at 300 dpi. Each scanned mylar was then rectified to the NAIP base layer using recognizable ground features as registration points. The resulting scan produced a raster image that was subsequently vectorized. Each vectorized output was then extensively edited to produce clean digital vector lines. From the digitized vectors we created polygons by building topology in the GIS program. Finally, we created labels for each polygon and used these to add the attribute information. Attribution for all the polygons at CHIC included information pertaining to map units, NVC associations, Anderson land-use classes, and other relevant data. Attribute data were taken directly from the interpreted photos or were added later using the orthophotos as a guide.

The United Nations Geospatial Data, or Geodata, is a worldwide geospatial dataset of the United Nations. The United Nations Geodata is provided to facilitate the preparation of cartographic materials in the United Nations includes geometry, attributes and labels to facilitate the adequate depiction and naming of geographic features for the preparation of maps in accordance with United Nations policies and practices. The geospatial dataset include polygons/areas of countries (BNDA_simplified). Please refer this page for more information.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles.

Spatial data from observation points and quantitative plots were used to edit the formation-level maps of George Washington Birthplace National Monument to better reflect homogeneous vegetation classes. Using Arcview 3.3, polygon boundaries were revised onscreen over leaf-off photography. Units used to label polygons on the map (i.e. map classes) are equivalent to one or more vegetation classes from the regional vegetation classification, or to a land-use class from the Anderson Level II classification system. Each polygon on the George Washington Birthplace National Monument map was assigned to one of 19 map classes based on plot data, field observations, aerial photography signatures, and topographic maps.

NDFD temperature, max and min temperature, apparent temperature and relative humidity forecastsLink to graphical web page: https://digital.weather.govLink to data download (grib2): https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/Link to metadataQuestions/Concerns about the service, please contact the DISS GIS teamTime Information:This map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have two options for determining the latest time information about the service:Issue a returnUpdates=truerequest for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following relevant fields returned:idp_validtime - valid time of forecastidp_validendtime - end time of forecastidp_fcst_hour - start time of number of hours from current time forecast is validIn ArcGIS.com this option can be turned on by clicking the three dots under "NDFD Temp" heading and choosing "Enable Time Animation".

The U.S. Geological Survey (USGS) modeled the distribution of terrestrial ecosystems for the contiguous United States using a standardized, deductive approach to associate unique physical environments with ecological systems characterized in NatureServe's Ecological Systems of the United States classification (Comer et al., 2003). This approach was first developed for South America (Sayre et al., 2008) and is now being implemented globally (Sayre et al., 2007). Unique physical environments were delineated from a massive biophysical stratification of the nation into the major structural components of ecosystems: biogeographic regions (Cress et al., 2008c), land surface forms (Cress et al., 2008a), surficial lithology (Cress et al., 2008d), and topographic moisture potential (Cress et al., 2008b). Each of these structural components was mapped for the contiguous United States and then spatially combined to produce ecosystem structural footprints which represented unique abiotic (physical) environments. Among 49,168 unique structural footprint classes, 13,482 classes which met a minimum pixel count threshold (20,000 pixels) were aggregated into 419 NatureServe ecosystems through semi-automated labeling process using rule set formulations for attribution of each ecosystem.

This polygon feature class represents a generalized spatial extent of active BLM Administrative Unit Boundaries at the State, District, and Field Office levels. Only data for Alaska are included.

This dataset includes the inputs and results for developing a transportation geo-typology that categorizes every location in the United States in terms of their main drivers of transportation demand and supply. It provides the raw inputs to the census tract level microtypes and county or CBSA level geotypes as well as the final typology labels at both the tract (microtype) and county/CBSA (geotype) levels. Inputs include information on the street network, economic characteristics, topography, commute patterns, and land use. The methodology is published in "Popovich, N., Spurlock, C. A., Needell, Z., Jin, L., Wenzel, T., Sheppard, C., & Asudegi, M. (2021). A methodology to develop a geospatial transportation typology. Journal of transport geography, 93, 103061".

This dataset consists of annotated high-resolution aerial imagery of roof shapes/geometries in Bonn, Germany, in the Ultralytics YOLO instance segmentation dataset format. Aerial imagery was sourced from OpenAerialMap, specifically from the Maxar Open Data Program. Roof shape labels and building outlines were sourced from OpenStreetMap. Images and labels are split into training, validation, and test sets, meant for future machine learning models to be trained upon, for both building segmentation and roof shape/geometry classification.The dataset is intended for applications such as informing studies on solar capacity estimation, urban morphology, 3D modeling, risk assessments, and other related fields. There are seven roof shape types: gabled, flat, skillion, hipped, gambrel, half-hipped, pyramidal, and mansard. Note: The data is in a .zip due to file upload limits. Please find a more detailed dataset description in the README.md