GIS – Great Lakes Sediment Budget – Technical Methodology – Buffline Digitization Madeleine Dewey EIT1 , Cedric Wrobel EIT1 1United States Army Corps of Engineers Great Lakes and Ohio River Division, Buffalo District Department of Coastal and Geotechnical Design Editor and Senior Reviewer: Weston Cross PG1 Published: September 2021 Abstract: This document is intended for use as a reference guide to complete bluffline digitization work for the Great Lakes Sediment Budget, a project of the Great Lakes Restoration Initiative. Digitization work consists of manually drawing polylines along the lakeshore to delineate where the bluffline, or more broadly, the line of significance, exists. This reference can be used for both historic, and contemporary blufflines. In addition, this guide outlines what datasets, ESRI ArcGIS tools, and strategies should be employed. The manual for ESRI ArcMap 10.7, the version of ArcGIS used to create this guide, can be found at: https://support.esri.com/en/products/desktop/arcgis‐desktop/arcmap/10‐7‐1

The Geographic Information System (GIS) Solutions market is experiencing robust growth, driven by increasing adoption across diverse sectors. The market, estimated at $15 billion in 2025, is projected to expand significantly over the forecast period (2025-2033), fueled by a Compound Annual Growth Rate (CAGR) of approximately 8%. This growth is attributed to several key factors. Firstly, the rising need for precise spatial data analysis and visualization across industries like agriculture (precision farming), oil & gas (resource exploration and management), and construction (infrastructure planning and development) is driving demand. Secondly, advancements in GIS software and services, including cloud-based solutions and AI-powered analytics, are enhancing efficiency and accessibility. Thirdly, government initiatives promoting smart cities and infrastructure development are further boosting market expansion. The market is segmented by application (Agriculture, Oil & Gas, AEC, Transportation, Mining, Government, Healthcare, Others) and type (Software, Services), with software solutions currently holding a larger market share due to increasing digitization and data-driven decision-making. North America and Europe are currently the leading regional markets, benefiting from established infrastructure and high technology adoption rates, but Asia-Pacific is poised for significant growth driven by rapid urbanization and infrastructure development. Despite the promising growth trajectory, certain challenges remain. High initial investment costs for GIS software and implementation can be a barrier to entry for smaller businesses. Furthermore, the need for skilled professionals to effectively utilize and manage GIS data poses a considerable constraint. However, the ongoing development of user-friendly interfaces and accessible training programs is mitigating this issue. The competitive landscape is characterized by a mix of established players like ESRI, Hexagon, and Pitney Bowes, alongside emerging technology providers. These companies are actively investing in R&D and strategic partnerships to maintain their competitive edge and capitalize on the market's expansion. The long-term outlook for the GIS solutions market remains positive, with continuous innovation and expanding applications across various sectors paving the way for sustained growth throughout the forecast period.

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. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This is an instructional document developed for volunteers who follow the Fluvanna History Initiative on One Shared Story's GIS Hub.Training was held at the Fluvanna County Public Library on Sunday September 29, 2019. This effort is being coordinated through an Esri GIS Premium Hub Community with assitance from GIS Corp and funding from the UVA Equity Atlas and the BAMA Works Fund.

Index of symbols found in British, Italian and Austro-Hungarian maps, and guidelines that indicate to the scholar the most suitable methods for digitizing World War I military maps and interpreting their symbology.

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.

Cape Denison, Commonwealth Bay, GIS dataset is a topographic database detailing huts, penguins and natural features such as moraine and lakes. The dataset includes a 5m contour interval.

These shapefiles were obtained by digitising an existing Cape Denison historical map. All information about natural features, biota, etc are sourced from the map. Note, there is more recent data, or better quality data available with other Cape Denison datasets.

Utility GIS Field Data Collection Market Outlook

According to our latest research, the Global Utility GIS Field Data Collection market size was valued at $1.4 billion in 2024 and is projected to reach $3.1 billion by 2033, expanding at a robust CAGR of 9.3% during the forecast period of 2025–2033. The significant growth in this market is primarily driven by the increasing adoption of advanced geospatial technologies by utility companies seeking to modernize their infrastructure and enhance operational efficiency. The proliferation of smart grids, the growing need for real-time asset monitoring, and the integration of IoT devices have collectively intensified the demand for precise, field-based GIS data collection solutions. This market is further propelled by regulatory mandates emphasizing infrastructure resilience and digital transformation initiatives across the utilities sector, making GIS field data collection systems indispensable for asset management, network mapping, and operational optimization.

Regional Outlook

North America holds the largest share of the global Utility GIS Field Data Collection market, accounting for nearly 38% of the total market value in 2024. This dominance is underpinned by the region’s mature utility infrastructure, widespread digitalization, and early adoption of GIS technologies. The United States, in particular, has invested heavily in upgrading aging utility networks and deploying smart grid solutions, which has necessitated sophisticated GIS field data collection tools. Additionally, favorable regulatory frameworks and a strong presence of leading GIS software providers have accelerated technology uptake. The emphasis on disaster management, grid reliability, and environmental compliance further amplifies the demand for advanced GIS field data collection systems in North America.

In contrast, Asia Pacific emerges as the fastest-growing region, projected to register an impressive CAGR of 12.1% over the forecast period. The rapid urbanization, expanding utility networks, and significant government investments in infrastructure modernization across China, India, and Southeast Asia are pivotal growth drivers. These economies are leveraging GIS field data collection to support mega infrastructure projects, rural electrification, and efficient resource management. The increasing penetration of cloud-based GIS solutions and mobile data collection apps is enabling utilities in Asia Pacific to overcome legacy system limitations, optimize field operations, and improve service delivery. As a result, the region is witnessing a surge in both public and private sector investments aimed at digitalizing utility asset management.

Meanwhile, emerging economies in Latin America and Middle East & Africa are gradually adopting Utility GIS Field Data Collection technologies, albeit at a slower pace due to budget constraints, skills shortages, and infrastructural challenges. These regions face unique hurdles such as fragmented utility networks, inconsistent regulatory support, and limited access to advanced geospatial tools. However, localized demand is rising as governments and utility operators recognize the value of GIS in reducing losses, improving maintenance cycles, and supporting sustainable resource management. International aid programs, technology transfer initiatives, and growing awareness of digital transformation benefits are expected to accelerate adoption in these regions over the next decade.

Report Scope

Data set that contains information on archaeological remains of the pre historic settlement of the Letolo valley on Savaii on Samoa. It is built in ArcMap from ESRI and is based on previously unpublished surveys made by the Peace Corps Volonteer Gregory Jackmond in 1976-78, and in a lesser degree on excavations made by Helene Martinsson Wallin and Paul Wallin. The settlement was in use from at least 1000 AD to about 1700- 1800. Since abandonment it has been covered by thick jungle. However by the time of the survey by Jackmond (1976-78) it was grazed by cattle and the remains was visible. The survey is at file at Auckland War Memorial Museum and has hitherto been unpublished. A copy of the survey has been accessed by Olof Håkansson through Martinsson Wallin and Wallin and as part of a Masters Thesis in Archeology at Uppsala University it has been digitised.

Olof Håkansson has built the data base structure in the software from ESRI, and digitised the data in 2015 to 2017. One of the aims of the Masters Thesis was to discuss hierarchies. To do this, subsets of the data have been displayed in various ways on maps. Another aim was to discuss archaeological methodology when working with spatial data, but the data in itself can be used without regard to the questions asked in the Masters Thesis. All data that was unclear has been removed in an effort to avoid errors being introduced. Even so, if there is mistakes in the data set it is to be blamed on the researcher, Olof Håkansson. A more comprehensive account of the aim, questions, purpose, method, as well the results of the research, is to be found in the Masters Thesis itself. Direkt link http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1149265&dswid=9472

Purpose:

The purpose is to examine hierarchies in prehistoric Samoa. The purpose is further to make the produced data sets available for study.

Prehistoric remains of the settlement of Letolo on the Island of Savaii in Samoa in Polynesia

GIS data digitised from 2 DigitalGlobe images at a scale of 1:1000.
The features were digitised using ArcGIS Pro and were created within a topology to ensure the spatial integrity of the data. Line data include coastlines, ice fronts and grounding lines. Polygon data include continent, island, ice tongue and rock features.

The images and data are of the Bølingen Islands and surrounding area, in the Prydz Bay region of Antarctica.
(18FEB23042505-P2AS-017311657010_01_P001.TIL; 18FEB23042504-M2AS-017311657010_01_P001.TIL)
(24MAR16035205-P2AS-017311660010_01_P001.TIL; 24MAR16035205-M2AS-017311660010_01_P001.TIL)
Copyright 2024 DigitalGlobe Incorporated, Longmont CO USA 80503-6493

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.

To Digitise in ArcGIS Online you will need to Add Map Notes. Follow the following steps to digitise the area of an agricultural field:

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.

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.

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 mapping component of the BRCA project used a combination of methods to interpret and delineate vegetation and land use polygons. The USGS applied an electronic segmentation method (e-Cognition software) to create preliminary linework on features with high-contrast photo-signatures. Using the preliminary linework as a baseline starting point, the primary photointerpreter drew polygons directly on screen through heads-up digitizing using ArcGIS editing tools. Additionally, trained photointerpreters assisting the primary photointerpreter drew polygons on Mylar overlays covering 1m resolution, 1:12,000-scale, 9 x 9-inch true-color aerial photographs. This process enabled the photointerpreters to view the landscape in stereo in order to identify finer details. The linework drawn on Mylar overlays was then transferred into digital media by heads-up digitizing using ArcGIS software. The park and environs were interpreted and mapped to the same level of detail.

The seamless geological map of southern West, South-West and South Greenland at scale 1:100,000 is the result of the digitisation and homogenisation of 16 geological maps in scale 1:100,000, supplemented with scale 1:500,000 maps. The map is optimised for display in scale 1:100,000. A legend is enclosed as pdf-file. Three GEUS fonts are enclosed. In order to symbolise the data correct the three fonts need to be installed on the local machine (see Readme.txt file) The maps are delivered in ArcGIS and QGIS formats.

This dataset contains GIS spatial layers of the medium and heavy disturbance footprint surrounding Casey Research Station at the years 2002, 2008, 2015 and 2018. The footprint mapping used a consistent methodology for digitisation for the years 2002, 2015, and 2018, whereas the year 2008 included ground-truther field measurements. Some variation in the footprint is due to snow cover hiding areas of disturbance. The sources and date of the data used is as follows: • 2018 Casey Heavy Disturbance – digitised by S. Brooks from Worldview-3 imagery, captured 27/1/2018. • 2018 Casey Medium Disturbance – digitised by S. Brooks from Worldview-3 imagery, captured 27/1/2018. • 2015 Casey Heavy Disturbance – digitised by S. Brooks from AAS 5024 UAV-imagery. • 2015 Casey Medium Disturbance – digitised by S. Brooks from AAS 5024 UAV-imagery. • 2008 Casey Heavy Disturbance – produced from data associated with: Brooks, S.T. 2014. Developing a Standardised Approach to Measuring the Environmental Footprint of Antarctic Research Stations. Journal of Environmental Assessment Policy and Management, 16(04), 1450037. • 2008 Casey Medium Disturbance – produced from aerial imagery and field data opportunistically collected in association with: Brooks, S.T. 2014. Developing a Standardised Approach to Measuring the Environmental Footprint of Antarctic Research Stations. Journal of Environmental Assessment Policy and Management, 16(04), 1450037. • 2002 Casey Heavy Disturbance – digitised by S. Brooks from 2002 AADC-held Orthophoto • 2002 Casey Medium Disturbance – digitised by S. Brooks from 2002 AADC-held Orthophoto

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.

Building polygons were created in February 2013 by Geoscience Australia by manually digitising the outline of each building off the 2011 orthophotography. Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was extremely time consuming - only the height_max field was significantly improved. Disclaimer

From the ** utilities surveyed across the world, around ** percent agreed that they have geographic information systems (GIS) in place for their water supply and drinking water management. Only ** percent of them stated that they use drone imaging for the same purpose.