Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Grala, K., McMillen, Randall D., & Cartwright, J. H. (2022). On-screen Digitizing Using QGIS. Mississippi State University: Geosystems Research Institute. [View Document]GEO TutorialNumber of Pages: 13Publication Date: 08/2022This work was supported through funding by the National Oceanic and Atmospheric Administration Regional Geospatial Modeling Grant, Award # NA19NOS4730207.

Digitising of the coastline, glacier extents, water features, wildlife, and human footprints of the Heard Island Remote Sensing Project, 2009. This GIS data has Dataset_id = 273 and is available for downloading under the heading Heard and McDonald Islands (see url below).

The coastline and glacier data available for download was updated in May 2014 after the correction of dates in the Date of Capture field which records the date of capture of the image from which the digitising was done. The coastline of the offshore rocks which were large enough to map as polygons was added to the coastline data.

Every five years, since 1990, the Delaware Valley Regional Planning Commission has produced a GIS Land Use layer for its 9-county region. In 2000, digital orthophotography was flown by DVRPC. Utilizing this orthophotography, all Land Use annotation and digitizing was performed on-screen, or "heads-up," a first at DVRPC. Digitizing was done using ESRI ArcGIS 8 software at a 1:2400 (1 inch = 200 feet) scale.

Spatial data for the potential landscapes in Bulgaria

Digitising of the coastline, glacier extents, water features, wildlife, and human footprints of the Heard Island Remote Sensing Project, 2009. This GIS data has Dataset_id = 273 and is available for downloading under the heading Heard and McDonald Islands (see url below).

The coastline and glacier data available for download was updated in May 2014 after the correction of dates in the Date of Capture field which records the date of capture of the image from which the digitising was done. The coastline of the offshore rocks which were large enough to map as polygons was added to the coastline data.

This dataset contains well log files collected from wells penetrating the Tuscarora Sandstone, structural geologic map of West Virginia and salinity information based on brine geochemistry in West Virginia and Pennsylvania. A combination of proprietary and free software may be required to view some of the information provided. Software used for data analysis and figure creation include ESRI ArcGIS. For GIS map files, you will have to change the directories of the files to match your computer. LAS files were digitized using IHS Petra software, but may be viewed in Microsoft Notepad, or converted to .csv files in Microsoft Excel.

Aerial photography (35mm film) of penguin colonies was acquired over some islands north east of Brattstrand Bluff islands (Eric Woehler). The penguin colonies were traced, then digitised (John Cox), and saved as DXF-files. Using the ArcView extension 'Register and Transform' (Tom Velthuis), The DXF-files were brought into a GIS and transformed to the appropriate islands. Update May 2015 - This dataset has been rename from "Brattstrand Bluff penguin GIS dataset" to "Islands NE of Brattstrand Bluff penguin GIS dataset" to better describe the location of the colonies. The penguin colonies are on a small group of islands approximately 12km north east of Brattstrand Bluff. Latitude 69.148 south and longitude 77.268 east. The Data Centre does not have a copy of the original photographs or described GIS data. In May 2015, the Data Centre has attached the following to this record: The DXF file produced by John Cox by digitising the aerial photography. Note this document is not georeferenced. Four photographs taken in 2009 by Barbara Wienecke, Seabird Ecologist, showing penguin colonies on these islands. A shapefile exists of the digitised colonies. The digitising by Ursula Harris, Australian Antarctic Data Centre, was done by georeferencing the DXF drawing over unprocessed Quickbird Image 05NOV15042413-M1BS-052187281010_01_P002. It was done in two parts, the largest island and then the two smaller islands. This allowed for better matching. The accuracy of this data is unknown.

Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

This feature class was digitized from the map, A.B. 1717, by Jeff Galef on August 22, 2012. The features were labeled as being in the Primary or Secondary Zone. The digitizing was done at a 1:4,000 scale. The features were digitized by a map that was georeferenced by Jeff Galef on July 25, 2012. The number of control points used was 25. The RMS error was 13.74340. The georeferencing was performed against the 2009 NAIP imagery, which was projected to UTM Zone 10, NAD 83.Digitizing was difficult since the line borders and the associated colors often did not match up. That is, there was a fair amount of overlap. The decision was made that the digitizing would follow the thick red and black lines where available. Otherwise, the digitizing followed the coloring. This feature class was edited on November 26, 2013 by Terri Fong to reflect the San Francisco Bay Conservation and Development Commission's map amendments of 2011. The amendments are described in Resolution No. 11-05 which can be found here: http://www.bcdc.ca.gov/BPA/Final2011.07.01.ResolutionNo1.10.pdf. This resolution changes the size of the Water Related Industry Reserve Area near Collinsville. The current Boundaries of the Suisun Marsh map can be found here: http://www.bcdc.ca.gov/plans/SMboundaries.pdf.

A GIS dataset of around Cape Denison and part of George V land created from two IKONOS satellite images. Layers created from digitising directly from the imagery include: mapping extent, continent, building, refuge, coastline, reef, offshore rocks, sea, snow, sheet, island, birds, rock, moraine, sea ice, lakes - The mapping extent layer represents the edge of the IKONOS imagery. - The continent layer represents the land mass shown in IKONOS imagery. It was generated using the digitised coastline and bounded by lines that represent the edge of the image. - The snow spatial data represents the snow cover in January 2001 - The sheet ice spatial data represents the ice extent in January 2001 - The penguin spatial data represents the penguin colony extents, based on guano deposits. - The rock spatial data represents the exposed bare rock

This is the georeferenced map of City and Suburbs of Sydney 1890 archived by State Library of NSW

Planimetric Bridge features. In 2006, the Des Moines Regional GIS group contracted with Sanborn to digitize the planimetric features utilizing 3D stereo digitizing methods and GIS processing required under the RFP. The Program Management task included coordination and oversight of the NewCom Technology tasks; incorporating the imagery and photogrammetric data from the spring of 2006 flight, stereo digitizing the planimetric features and GIS processing of the impervious surface features to ensure clean topological data structure for subsequent area / polygon calculations. Maintainenance of the data includes heads-up digitizing using the orthophoto images.

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. James W. Sewall Company developed a complete GIS coverage for the park and revised the preliminary vegetation map classes to better match the results from the cluster analysis and NMS ordination. Polygons representing vegetation stands were digitized on-screen in ArcGIS 8.3, and later in ArcMap 9.1 and 9.2, using lines drawn on the acetate overlays, base layers of 1:8,000 CIR aerial photography, orthorectified photo composite image, and plot location and data. The minimum map unit used was 0.5 ha (1.24 ac). Stereo pairs were used to double check stand signatures during the digitizing process. Photo interpretation and polygon digitization extended outside the NPS boundary, especially where vegetation units were arbitrarily truncated by the boundary. Each polygon was attributed with the name of a vegetation map class or an Anderson Level II land use category based on plot data, field observations, aerial photography signatures, and topographic maps. Data fields identifying the USNVC association inclusions within the vegetation map class were attributed to the vegetation polygons in the shapefile. The GIS coverages and shapefiles were projected to Universal Transverse Mercator (UTM) Zone 19 North American Datum 1983 (NAD83). FGDC compliant metadata (FGDC 1998a) were created with the NPS-MP ESRI extension and included with the vegetation map shapefile. A photointerpretation key to the map classes for the 2006 draft vegetation map is included as Appendix A. The composite vegetation coverage was clipped to the NPS 2002 MIMA boundary shapefile for accuracy assessment (AA). After the 2006 vegetation map was completed, the thematic accuracy of this map was assessed.

The global mobile backpack scanning system market is experiencing robust growth, driven by increasing demand across diverse sectors. Applications such as power line inspection, forestry surveys, mining surveying, and building surveys are key contributors to this expansion. The non-invasive nature of these systems, coupled with their portability and efficiency in data acquisition, makes them attractive alternatives to traditional surveying methods. Furthermore, advancements in sensor technology, improved data processing capabilities, and the rising adoption of 3D modeling and GIS software are fueling market expansion. We estimate the market size in 2025 to be $850 million, based on reasonable extrapolation of industry growth trends and considering the substantial investments in infrastructure and technological advancements across key sectors. Assuming a conservative Compound Annual Growth Rate (CAGR) of 12% over the forecast period (2025-2033), the market is projected to reach approximately $2.7 billion by 2033. This growth reflects the increasing need for efficient and precise data acquisition in various industries, leading to higher adoption rates and substantial market penetration. Several factors contribute to the market's positive outlook. The integration of advanced technologies, like LiDAR and hyperspectral imaging, within mobile backpack scanning systems enhances data accuracy and expands application possibilities. Moreover, the decreasing cost of these systems and increased awareness of their benefits among various stakeholders are further driving market adoption. However, factors such as the high initial investment cost, the need for specialized training for operation and data interpretation, and potential regulatory hurdles in certain regions might slightly restrain market growth. Nevertheless, the overall market trajectory remains strongly positive, with significant opportunities for growth and innovation across both developed and developing economies. Key players are actively involved in product development and strategic partnerships to capitalize on this burgeoning market. This comprehensive report provides a detailed analysis of the global mobile backpack scanning system market, projected to reach $2.5 billion by 2030. We delve into market dynamics, key players, emerging trends, and future growth prospects, utilizing data-driven insights to help businesses strategize effectively in this rapidly evolving sector. The report is essential for stakeholders including manufacturers, investors, researchers, and government agencies. Keywords: Mobile Backpack Scanner, 3D Scanning, LiDAR, Point Cloud, Surveying, Mapping, Inspection, Forestry, Mining, Power Lines, Building Construction.

Planimetric Bridge features. In 2006, the Des Moines Regional GIS group contracted with Sanborn to digitize the planimetric features utilizing 3D stereo digitizing methods and GIS processing required under the RFP. The Program Management task included coordination and oversight of the NewCom Technology tasks; incorporating the imagery and photogrammetric data from the spring of 2006 flight, stereo digitizing the planimetric features and GIS processing of the impervious surface features to ensure clean topological data structure for subsequent area / polygon calculations. Maintenance of the data includes heads-up digitizing using the orthophoto images.

This dataset (lineaments_ln_ll.shp) comprises structural features compiled into GIS format from existing literature, published up to 2003. The data represent fault/lineament locations known or inferred in the Alberta Plains. We have chosen to digitize and publish all lineaments from source maps even where they extended beyond the Alberta boundary. Each compiled feature is characterized by a set of attributes including: affected formations (oldest affected and oldest non-affected stratigraphic unit), fault type, fault sense of displacement, evidence used to infer the fault/lineament, original reference information and publication scale, and an estimate of the georeferencing error. The completeness of the captured attribute set varies for each feature as a function of the level of detail in the source article. The data set should be used cautiously. First, the original authors' interpretation of subsurface faults, particularly of 'basement faults', from air photo or satellite imagery lineaments is tenuous. Second, the vast majority of faults inferred in the foreland basin (Alberta Plains) east of the deformation front are normal-slip faults. although only the dip slip component has been inferred, some of these faults may also have a strike-slip component, generally not accounted for. Third, the location of lineaments includes cumulative errors inherent in the process of transferring into GIS lineaments traced by hand in the pre-computer era on small scale (regional) paper-copy maps. Such errors include spatial imprecisions in original lineament identification and drawing and errors in georefencing of the source map, as well as minor errors introduced during lineament digitization. Although each of them is minor at the scale of the original map, the cumulative effect of these errors may be significant and even misleading for large-scale (township or larger) projects.

This is the georeferenced map of Concord 1894 archived by Dictionary of Sydney

Planimetric Parking features. In 2006, the Des Moines Regional GIS group contracted with Sanborn to digitize the planimetric features utilizing 3D stereo digitizing methods and GIS processing required under the RFP. The Program Management task included coordination and oversight of the NewCom Technology tasks; incorporating the imagery and photogrammetric data from the spring of 2006 flight, stereo digitizing the planimetric features and GIS processing of the impervious surface features to ensure clean topological data structure for subsequent area / polygon calculations. Maintenance of the data includes heads-up digitizing using the orthophoto images.

Planimetric Miscellaneous Structure features. In 2006, the Des Moines Regional GIS group contracted with Sanborn to digitize the planimetric features utilizing 3D stereo digitizing methods and GIS processing required under the RFP. The Program Management task included coordination and oversight of the NewCom Technology tasks; incorporating the imagery and photogrammetric data from the spring of 2006 flight, stereo digitizing the planimetric features and GIS processing of the impervious surface features to ensure clean topological data structure for subsequent area / polygon calculations. Maintenance of the data includes heads-up digitizing using the orthophoto images.