Use the Attachment Viewer template to provide an app for users to explore a layer's features and review attachments with the option to update attribute data. Present your images, videos, and PDF files collected using ArcGIS Field Maps or ArcGIS Survey123 workflows. Choose an attachment-focused layout to display individual images beside your map or a map-focused layout to highlight your map next to a gallery of images. Examples: Review photos collected during emergency response damage inspections. Display the results of field data collection and support downloading images for inclusion in a report. Present a map of land parcel along with associated documents stored as attachments. Data requirements The Attachment Viewer template requires a feature layer with attachments. It includes the capability to view attachments of a hosted feature service or an ArcGIS Server feature service (10.8 or later). Currently, the app can display JPEG, JPG, PNG, GIF, MP4, QuickTime (.mov), and PDF files in the viewer window. All other attachment types are displayed as a link. Key app capabilities App layout - Choose between an attachment-focused layout, which displays one attachment at a time in the main panel of the app with the map on the side, or a map-focused layout, which displays the map in the main panel of the app with a gallery of attachments. Feature selection - Allows users to select features in the map and view associated attachments. Review data - Enable tools to review and update existing records. Zoom, pan, download images - Allow users to interact with and download attachments. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

Shoreline change analysis is an important environmental monitoring tool for evaluating coastal exposure to erosion hazards, particularly for vulnerable habitats such as coastal wetlands where habitat loss is problematic world-wide. The increasing availability of high-resolution satellite imagery and emerging developments in analysis techniques support the implementation of these data into coastal management, including shoreline monitoring and change analysis. Geospatial shoreline data were created from a semi-automated methodology using WorldView (WV) satellite data between 2013 and 2020. The data were compared to contemporaneous field-surveyed Real-time Kinematic (RTK) Global Positioning System (GPS) data collected by the Grand Bay National Estuarine Research Reserve (GBNERR) and digitized shorelines from U.S. Department of Agriculture National Agriculture Imagery Program (NAIP) orthophotos. Field data for shoreline monitoring sites was also collected to aid interpretation of results. This data release contains digital vector shorelines, shoreline change calculations for all three remote sensing data sets, and field surveyed data. The data will aid managers and decision-makers in the adoption of high-resolution satellite imagery into shoreline monitoring activities, which will increase the spatial scale of shoreline change monitoring, provide rapid response to evaluate impacts of coastal erosion, and reduce cost of labor-intensive practices. For further information regarding data collection and/or processing methods, refer to the associated journal article (Smith and others, 2021).

Dissertation and dataset present an archaeological study of the Huarmey Valley region, located on the northern coast of Peru. My work uses modern and innovative digital methods. My research focuses on better understanding the location of one of the most important sites in the valley, Castillo de Huarmey, by learning about the context in which it functioned. The Imperial Mausoleum located at the site, along with the burial chamber beneath it, is considered one of the most important discoveries regarding the Wari culture in recent years.In the dissertation, I address issues concerning both the location of the site on a macro scale - in the entire Huarmey Valley, on a micro scale - the context of the Huarmey Valley delta – and the spatial relationships within the burial chamber located beneath the Mausoleum. I ask the questions (i) How did Castillo de Huarmey communicate with other sites dated to the same period located in the valley and also in adjacent valleys? Did this influence its role in the region? (ii) Is the Mausoleum at Castillo de Huarmey located intentionally and what was the meaning of this location at the micro and macro scale? (iii) What spatial relations existed between Castillo de Huarmey and other sites from the same period? (iv) Does the position of the artifacts, found in situ in the burial chamber, show important relationships between buried individuals? (v) Does spatial analysis show interesting spatial patterns within the burial inside the chamber?The questions can be answered by describing and testing the digital methods proposed in the doctoral dissertation related to both field data collection and their analysis and interpretation. These methods were selected and adapted to a specific area (the Northern Coast of Peru) and to the objective of answering the questions posed in the thesis. The wide range of digital methods used in archaeology is made possible by the use of Geographic Information Systems (abbreviated GIS) in research. To date, GIS in archaeology is used in three aspects (Wheatley and Gillings 2002): (i) statistical and spatial analysis to obtain new information, (ii) landscape archaeology, and (iii) Cultural Resource Management.My dissertation is divided into three main components that discuss the types of digital methods used in archaeology. The division of these methods will be adapted to the level of detail of the research (from the location of the site in the region, to the delta of the Huarmey Valley, to the burial chamber of the Mausoleum) and to the way they are used in archaeology (from Cultural Resource Management, to archaeological landscape analysis, to statistical-spatial analysis). One of the aims of the dissertation is to show the methodological path of the use of digital methods, i.e. from the acquisition of data in the field, through analysis, to their interpretation in a cultural context. However, the main objective of my research is to interpret the spatial relationships from the macro to the micro level, in the case described, against the background of other sites located in the valley, the location of Castillo de Huarmey in the context of the valley delta, and finally to the burial chamber of the Mausoleum. The uniqueness of the described burial makes the research and its results pioneering in nature.As a final result of my work I would like to determine whether relationships can be demonstrated between the women buried in the burial chamber and whether the location of particular categories of artifacts can illustrate specific spatial patterns of burial. Furthermore, my goal is to attempt to understand the relationship between the Imperial Mausoleum and other sites (archival as well as newly discovered) located in the Huarmey Valley and to understand the role of the site's location.Published dataset represents, described in the dissertation, mobile GIS survey on the site PV35-5 created in Survey123, ESRI application; xml and xls used for creating the survey that was used during the research of the site, as well as the results of the survey published in ArcGIS Pro package. The package includes collected data as points, saved as .shp, as well as ortophotomaps (as geotiff) and Digital Elevation Model and hillshade of PV35-5. The published dataset represents part of the dissertation describing archaeological landscape analysis of Huarmey Valley’s delta.

Analysis of ‘Tulare County Land Use Survey 2007’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/24d8af24-fd48-4b6b-bd2a-f9aaed5bd880 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Geographical distribution (%) of incidents by governorate.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: - Prevalence of computers and access to the Internet. - Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip), type of locality (urban, rural, refugee camps) and governorate.

Analysis unit

• Household.
• Persons 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on the 8th of May 2014 and ended on the 23rd of June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates: 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

Analysis of ‘Stanislaus County Land Use Survey 2004’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/e324486f-138b-40ae-81fb-b1e18d6078e4 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

GPS Field Controller Market Outlook

The global GPS Field Controller market size was valued at approximately USD 1.2 billion in 2023, and it is anticipated to reach USD 2.5 billion by 2032, exhibiting a robust compound annual growth rate (CAGR) of 8.3% over the forecast period. The growth of this market is fueled by the increasing demand for precision and accuracy in field operations across diverse industries such as agriculture, construction, and mining. As technological advancements continue to enhance the capabilities of GPS field controllers, they are becoming indispensable tools in modern data collection and field management processes, driving market expansion.

One of the primary growth factors propelling the GPS Field Controller market is the rapid advancement in GPS technology itself. With continuous improvements in satellite navigation systems, including the introduction of new satellite constellations and the enhancement of signal accuracy, GPS field controllers are becoming more reliable and precise. This technological evolution is crucial for sectors like surveying and agriculture, where even minor inaccuracies can lead to significant operational inefficiencies. The integration of advanced features such as real-time kinematic (RTK) positioning and multi-constellation support have further optimized the performance of GPS field controllers, making them essential for precise field data collection and management.

Moreover, the surge in global urbanization and infrastructure development projects is contributing significantly to the growth of the GPS Field Controller market. As urban centers expand and new construction projects are initiated, the demand for efficient and accurate field mapping and surveying tools is rising. GPS field controllers play a critical role in ensuring the precision of topographical surveys and construction site assessments, streamlining project workflows, and reducing costs and time overruns. The construction industry, in particular, is increasingly adopting these technologies to enhance the accuracy of their operations and ensure compliance with stringent regulatory standards, further driving market demand.

Environmental sustainability and smart agriculture practices are also significant drivers of the GPS Field Controller market. As global awareness about environmental conservation grows, there is an increasing focus on optimizing agricultural operations to minimize resource wastage and maximize yield. GPS field controllers facilitate precision agriculture by enabling detailed field mapping, efficient resource allocation, and real-time monitoring of agricultural activities. This capability is particularly beneficial for resource-intensive agricultural processes, fostering increased adoption of GPS field controllers in the agricultural sector and contributing to the market's overall growth trajectory.

Field Mount Controllers are increasingly becoming a vital component in the realm of GPS field controllers, especially as industries demand more robust and versatile solutions for field operations. These controllers are designed to withstand the harshest of environmental conditions, making them ideal for outdoor applications in agriculture, construction, and mining. Their rugged design ensures durability and reliability, which are critical factors for industries that operate in challenging terrains. With the ability to integrate seamlessly with other field equipment, Field Mount Controllers enhance the efficiency of data collection and management, providing users with real-time insights and control over their operations. This integration capability is particularly beneficial in sectors where precision and accuracy are paramount, allowing for streamlined workflows and improved decision-making processes.

From a regional perspective, the Asia Pacific region is expected to witness substantial growth in the GPS Field Controller market. This growth is attributed to the rapid industrialization and urbanization occurring in countries like China and India, where the demand for advanced surveying and construction tools is escalating. Additionally, the region's burgeoning agricultural sector is increasingly embracing precision farming technologies, further supporting market expansion. Meanwhile, North America and Europe continue to be significant markets due to the high adoption rate of advanced technologies and substantial investment in infrastructure development, providing a steady demand for GPS field controllers.

Component Analysis

Размер и доля сегментированы по Type (Survey software, Form builders, Data gathering apps, Feedback collection tools, Field data collection tools) and Application (Market research, Customer feedback, Academic research, Field surveys, Product testing) and регионам (Северная Америка, Европа, Азиатско-Тихоокеанский регион, Южная Америка, Ближний Восток и Африка)

Analysis of ‘Marin County Land Use Survey 2011’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/aebcf794-58b1-41d7-a051-b9d0ea1c8da4 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2013 Tuolumne County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Tuolumne County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.0 and 10.2 using 2012 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2013 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted during June 2013. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed, especially in forested areas. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legend specific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2015 Calaveras County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data were gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. SPECIAL NOTE FOR CALAVERAS 2015 SURVEY The Calaveras 2015 landuse survey took place prior to the Butte Fire that impacted a large portion of Calaveras County during September 2015. The survey only shows landuse for pre-fire conditions. There was no survey post fire. This data represents a land use survey of Calaveras County conducted by the California Department of Water Resources, North Central Regional Office staff. Land use field boundaries were digitized with ArcGIS 10.3 using 2014 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were reviewed and updated using 2015 Landsat 8 imagery. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, and are not meant to be used as parcel boundaries. The field work for this survey was conducted from August 31, 2015 through September 10, 2015. Images, land use boundaries and ESRI ArcMap software were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using dropdown selections from defined domains. Agricultural fields the staff were unable to access were designated 'E' in the Class field for Entry Denied in accordance with the 2009 Landuse Legend. Upon completion of the survey, a Python script was used to convert the data table into the standard land use format. ArcGIS geoprocessing tools and topology rules were used to locate errors for quality control. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Sources of irrigation water were identified for general areas and occasionally supplemented by information obtained from landowners. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

This map is designated as Final.Land-Use Data Quality ControlEvery published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process.Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional datasets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2009 El Dorado County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of: Kim Rosmaier. This data was developed to monitor land use for the primary purpose of quantifying water use within this study area and determining changes in water use associated with land use changes over time. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of El Dorado County conducted by the California Department of Water Resources, North Central Regional Office staff. For digitizing, the county was subdivided into three areas using the centerline of U.S. Route 50 and a north/south line for boundaries. Land use field boundaries were digitized with ArcGIS 9.3 using 2005 U.S.D.A National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. The three digitized shapefiles were merged into a single file and the shared boundaries were removed. Field boundaries were reviewed and updated using 2009 NAIP imagery when it became available. Field boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. The field work for this survey was conducted between the end of July and the first week of November 2009. Images, land use boundaries and ESRI ArcMap software, version 9.3 were loaded onto laptop computers that were used as the field data collection tools. Staff took these laptops into the field and virtually all agricultural fields were visited to positively identify the land use. Global positioning System (GPS) units connected to the laptops were used to confirm the surveyor's location with respect to the fields. Land use codes were digitized in the field using customized menus to enter land use attributes. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation, so some urban areas may have been missed. Especially in rural residential areas, urban land use was delineated by drawing polygons to surround houses or other buildings along with a minimal area of land surrounding these structures. These footprint areas represent the locations of structures but do not represent the entire footprint of urban land. Information on sources of irrigation water was identified for general areas and occasionally supplemented by information obtained from landowners or by the observation of wells. Water source information was not collected for each field in the survey, so the water source listed for a specific agricultural field may not be accurate. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.

The Inspection Management Software Marketsize was valued at USD 7.98 USD Billion in 2023 and is projected to reach USD 18.43 USD Billion by 2032, exhibiting a CAGR of 12.7 % during the forecast period.The Inspection Management System is a digital implementation that has been created to automate and simplify the processes of inspection across industries and therefore ensure true accuracy, quality, and compliance. It is the starting point and the operational and coordinating laboratories for all the activities of the inspection process, from the outline of the inspection plan to the reporting and follow-up activities. Critical aspects of inspection management software include customizable checklists, support for mobile operations at places, live data collection, photo or video documentation, built-in alerts and reminders, analysis and reporting tools, and amalgamation with other business systems. The list of benefits of using inspection management software is quite long. It facilitates productivity through the removal of manual paperwork and the simplification of the workflow. It reduces the bias among the processes, and insightful accuracy and consistency become the prime objectives. objectives. The real-time data availability makes it possible to take fast decisions and fix mistakes. It also incorporates compliance with regulatory provisions, decreases the possibility of mistakes, and eventually coalesces time and resources. Recent developments include: August 2023: Benchmark Gensuite, the provider of the inspection management platform, expanded its operations in Mason, Ohio. To provide its customers with a better service at home, globally, and across various operating profiles, its employees now have the opportunity to work together in person., April 2023: Fulcrum announced a strategic partnership with the American Society of Safety Professionals (ASSP), offering its members a special Starter Package of Fulcrum's field inspection management platform at no cost. Up to four fully customizable digital forms, including two reconstructed ASSP safety standard templates, which are available for use from the box, can be easily created by ASSP members., November 2022: Fulcrum announced that it has been selected by Osmose, a provider of network asset management and infrastructure supporting services for electric and telecommunications utilities, to optimize the delivery of pole inspection and assessment services to its customers. Fulcrum provides Osmose with the first mobile SaaS inspection solution, simplifying data collection processes to speed up field production, replacing its proprietary Field Data Collection tool., August 2022: Fulcrum announced new field inspection management tools that improve efficiency, allow constant improvement, and drive safer and higher quality results in this area. To inform workflow optimization and plan new inspection procedures, Fulcrum customers now have access to vital information at every level of the inspection processes., January 2022: Autodesk Inc. announced the acquisition of Moxion, a New Zealand-based developer of a cloud-based digital dailies platform. Autodesk's own cloud computing platform for media and entertainment has been strengthened through the acquisition of talent and technology from Moxion. This acquisition brings new users into Autodesk and helps improve the integration of processes throughout the content production chain.. Key drivers for this market are: Rising Adoption of the Business Automation Process for Seamless Inspection to Support Market Expansion. Potential restraints include: High Installation Costs and Need for Professional Expertise to Hinder the Market Progress. Notable trends are: Growing Implementation of Touch-based and Voice-based Infotainment Systems to Increase Adoption of Intelligent Cars.

The MultiTrack Field Recorder market is experiencing robust growth, driven by increasing demand across various applications, particularly in the environmental monitoring, geological surveying, and research sectors. The market's Compound Annual Growth Rate (CAGR) is estimated at 7% from 2025 to 2033, indicating significant expansion potential. This growth is fueled by advancements in recording technology, offering higher fidelity, longer recording times, and enhanced data analysis capabilities. The increasing adoption of remote sensing technologies and the need for precise and reliable data collection in diverse field environments are key drivers. Furthermore, the development of more compact and user-friendly devices is making multitrack field recorders more accessible to a wider range of users. While initial capital investment can be a barrier to entry for some smaller organizations, the long-term cost benefits associated with efficient data acquisition and analysis outweigh this initial hurdle. The market is segmented by application (e.g., environmental monitoring, seismic surveying, acoustic research) and type (e.g., portable, stationary, specialized). North America and Europe currently hold a significant market share, but the Asia-Pacific region is showing rapid growth potential, driven by increasing infrastructure development and research activities. Competition in the market is moderate, with several established players and emerging companies vying for market share through product innovation and strategic partnerships. The forecast period (2025-2033) anticipates continued market expansion, particularly in developing economies. However, factors such as economic fluctuations and potential technological disruptions could impact the market's trajectory. Nevertheless, the ongoing need for accurate and efficient data acquisition in various fields ensures a positive outlook for the MultiTrack Field Recorder market. The increasing integration of advanced analytics and cloud-based data management solutions further enhances the value proposition of these devices, accelerating their adoption and market penetration. Therefore, strategic investments in research and development, alongside targeted marketing efforts focusing on the specific needs of various application sectors, are crucial for success in this growing market.

This data represents a land use survey of 2012 Merced County conducted by the California Department of Water Resources, South Central Region Office staff. Land use boundaries were digitized, and land use data was gathered by staff of DWR’s South Central Region Office using extensive field visits and aerial photography. Detailed agricultural land uses, and lesser detailed urban and native vegetation land uses were mapped. Landsat 5 imagery was analyzed prior to the field survey by DRA staff to map fields likely to have winter crops. The land use data went through standard quality control procedures before final processing. Quality control procedures were performed jointly by staff at DWR’s DRA headquarters and South Central Region Office. Land use field boundaries were digitized with ArcGIS 9.3 using 2010 NAIP as the base, and Google Earth were used as reference as well. Agricultural fields were delineated by following actual field boundaries instead of using the centerlines of roads to represent the field borders. Field boundaries were not drawn to represent legal parcel (ownership) boundaries and are not meant to be used as parcel boundaries. Field work for land use surveys occurs primarily during the summer and early fall, so it can be difficult to identify fields where winter crops have been produced during the survey year. To improve the mapping of winter crops, we analyzed Landsat 5 imagery to identify fields with high winter vegetative cover. The identification of these fields was based on an analysis of Landsat 5 imagery. Visual inspection of the Landsat scene displayed in false color infrared was used to select fields with high and low vegetative cover. These fields were used to develop spectral signatures using ERDAS Imagine and eCognition Developer software. The Landsat image was classified using a maximum likelihood supervised classification to label each pixel as vegetated or not vegetated, then the zonal attributes of polygons representing agricultural fields were summarized to identify fields vegetated during the winter. Polygons representing these fields were used on laptop taken to the field to highlight the fields which should be checked closely for winter crop residue. Site visits occurred from July through October 2012. Images and land use boundaries were loaded onto laptop computers that, in most cases, were used as the field data collection tools. GPS units connected to the laptop computers were used to confirm surveyor's location with respect to the fields. Staff took these laptop computers into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 9.3. Some staff took printed aerial photos into the field and wrote directly onto these photo field sheets. The data from the photo field sheets were digitized back in the office. The primary focus of this land use survey is mapping agricultural fields. Urban residences and other urban areas were delineated using aerial photo interpretation. Some urban areas may have been missed. Rural residential land use was delineated by drawing polygons to surround houses and other buildings along with some of the surrounding land. These footprint areas do not represent the entire footprint of urban land. Water source information was not collected for this land use survey. Therefore, the water source has been designated as Unknown. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s South Central Region, and at DRA's headquarters office under the leadership of Jean Woods, Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the “SPATIAL DATA STANDARDS FOR THE CALIFORNIA DEPARTMENT OF WATER RESOURCES” version 3.1, dated September 11, 2019.DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data.Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov.

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Land Surveying Equipment Market Size 2025-2029

The land surveying equipment market size is forecast to increase by USD 2.95 billion, at a CAGR of 6.3% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for accurate mapping and data analysis in various industries, including construction, engineering, and real estate. This trend is driving the adoption of advanced surveying technologies, such as robotic total stations and 3D scanners, which offer higher precision and efficiency compared to traditional methods. Robotic total stations, in particular, are gaining popularity due to their ability to automate the surveying process, reducing human error and increasing productivity. These devices use GPS technology and self-leveling mechanisms to capture data and generate accurate surveys. However, the market faces challenges in the form of regulatory and compliance requirements. As surveying data is critical for infrastructure projects, governments and regulatory bodies impose stringent regulations to ensure data accuracy and security. Compliance with these regulations can be time-consuming and costly, posing a significant challenge for market players. Moreover, the emergence of drone technology in surveying applications is another trend transforming the market landscape. Drones equipped with high-resolution cameras and LiDAR sensors are increasingly being used for topographic surveys, volumetric analysis, and infrastructure inspections. However, the use of drones in surveying raises concerns regarding data privacy, security, and safety, which need to be addressed through regulatory frameworks and technological solutions. In conclusion, the market is poised for growth due to the increasing demand for accurate mapping and data analysis. The adoption of advanced surveying technologies, such as robotic total stations and drones, is driving innovation and efficiency in the market. However, regulatory and compliance challenges, data privacy concerns, and safety issues pose significant obstacles that market players need to navigate effectively to capitalize on market opportunities and maintain a competitive edge.

What will be the Size of the Land Surveying Equipment Market during the forecast period?

Request Free SampleThe market is characterized by continuous evolution and dynamic market activities. Construction staking and cadastral surveying remain key applications, with data integrity and report generation playing essential roles in ensuring accuracy and reliability. Data processing, survey software, and RTK systems facilitate efficient data collection and analysis, while total stations and control surveys ensure alignment and precision. Alignment surveys and as-built surveys are crucial for infrastructure development, as are elevation surveys and field data collection in construction sites. Aerial surveying, site plans, and BIM integration are transforming the industry with advanced technologies such as laser scanning and drone surveying. Mining surveying, land development, and engineering surveying require high levels of data analysis and GIS integration for effective planning and execution. Hydrographic surveying, ground control points, and coordinate systems ensure data accuracy in various applications. Emerging technologies like artificial intelligence, machine learning, and point cloud processing are revolutionizing the industry, offering new possibilities for data analysis and automation. The market's ongoing unfolding is marked by the integration of GPS mapping, infrastructure development, and environmental monitoring, among others. Construction surveying, site preparation, and boundary surveys are essential components of real estate development, with 3D modeling and GNSS receivers streamlining the process. The market's continuous evolution underscores the importance of staying updated with the latest trends and technologies.

How is this Land Surveying Equipment Industry segmented?

The land surveying equipment industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductTS and TLUAVGNSS systemPipe lasersOthersEnd-userConstructionMiningOil and gasOthersGeographyNorth AmericaUSCanadaEuropeFranceGermanyUKAPACAustraliaChinaIndiaJapanSouth AmericaBrazilRest of World (ROW)

By Product Insights

The ts and tl segment is estimated to witness significant growth during the forecast period.The market experiences significant growth due to the increasing adoption of advanced technologies in surveying applications. Total stations and theodolites, such as TS and TL, play a pivotal role in this market. These instruments, which measure angles, distances, and elevations with high precision, are indispensable in construction, infrastr

Additional file 2. The ImageJ macro code to read SpotCards.

This layer represents DCR playgrounds and playground equipment. Both layers work together to provide an inventory of the current DCR playgrounds that are open to public use. Conditions can change rapidly so this is just a snapshot in time.This dataset has 2 nested sets of features: Data A: Playgrounds- Each point represents the approximate center of a DCR playground. - Attributes describe the playground as a whole, including access, fencing, parking, amenities etc. - Picture attachments visualize each playground. Attributes:AssocDCRfac: DCR facility with which this Playground is associated. PG_name: Name of Playground.Town: Town or City this Playground is in. SqFootage: approximate area of Playground (square feet).BikeRack: Availability of a bike rack.FacilityLighting: If the playground is illuminated for the dark.PredomMat: Predominant surface material of the ground covered by the Playground.PathSurf: Surface of any paths between equipment on this Playground.ADAparking: Availability of Accessivle parking.FenceType: Type of fence (if any).FenceHtEst: Height of fence (in feet).TablesNumber: Count of tables.TablesPredMat: Material of tables.BenchesNumber: Count of benches.BenchesPredMat: Predominant material of benches.ShadeNatural: Availability of Natural shade.ShadeBuilt: Availability of Built shade.TrashRecNum: Count of trash receptacles.TrashRecType: Type of trash receptacles.RecyclingNum: Count of Recycling receptacles (if any).InspDate: Date when the data was last updated.Address: The nearest address, for navigation.Data B: equipment - Each point represents an individual play equipment in the playground point in Data A (above). So each Data A point must have at least one point in Data B. Most playgrounds have many play structures and thus, multiple Data B points. - Attributes describe the play equipment according to it's component pieces. - Picture attachments visualize each piece of equipment. The Visit_date field for each record/playground indicates when the dataset was last updated and thus, is an indicator of the currentness of this data. The actual condition of the playground can change fast and render observations outdated.Attributes:PG_name: Name of PlaygroundEquipNum: An ID number for the Equipment that is unique to this playground.PredomMat: Predominant material of equipment.PlayAgeGrp: Intended age group for play.SurfaceMat: Material of surface under each equipment.Seesaw: Count of this kind of equipment in this structure. Oscillate: Same as above.Climber: Same as above.HorizOverhd: Same as above.BalanceFeat: Same as above.SandFeat: Same as above.SpringCoil: Same as above.Deck: Same as above.Swing: Same as above.Roof: Same as above.InteractPanel: Same as above.Bridge: Same as above.AcousElem: Same as above.OpenSlide: Same as above.TubeSlide: Same as above.CrawlTunnel: Same as above.Other: Count of any playground component not listed.InspDate: Date when the data was last updated.The 2 datasets are linked by the field: pg_name Item type:This public, non-editable View is based on the Hosted Feature layer, DCR Playgrounds(editable).Source:Ongoing updates: DCR Playgrounds manager. Created: from fieldwork conducted by Warner Larson Landscape Architects in 2016. A professional playground inspector was part of the team. Commissioned by DCR to be used for management and planning. Field data collection with the ESRI Collector app on smart tablets (iPad minis) using web-maps created by DCR GIS. Please note:This layer contains data that changes frequently and may need to be updated.