https://www.myvisajobs.com/terms-of-service/https://www.myvisajobs.com/terms-of-service/
A dataset that explores Green Card sponsorship trends, salary data, and employer insights for forest engineering (gis) in the U.S.
This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Forest Engineering (Gis). It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Forest Engineering (Gis). This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.
https://www.archivemarketresearch.com/privacy-policyhttps://www.archivemarketresearch.com/privacy-policy
The global Geographic Information System (GIS) solutions market size was valued at USD XX million in 2025 and is projected to expand at a CAGR of XX % over the forecast period, reaching USD XXX million by 2033. The growing adoption of GIS solutions across various industries, such as agriculture, oil & gas, architecture, engineering and construction, transportation, mining, government, healthcare, and others, is driving market growth. The increasing need for accurate and timely geospatial data for decision-making, along with the advancements in cloud computing, artificial intelligence (AI), and machine learning (ML), are key trends contributing to market expansion. However, data security concerns and the high cost of implementation and maintenance may restrain market growth to some extent. Key players in the GIS solutions market include ESRI, Hexagon, Pitney Bowes, SuperMap, Bentley System, GE, GeoStar, Zondy Cyber Group, Caliper, Hitachi Solutions, and KCI. North America holds a significant share of the market due to the early adoption of GIS solutions and the presence of major players. Asia Pacific is expected to witness substantial growth over the forecast period owing to the increasing infrastructure development and urbanization in the region.
Attribution-NonCommercial-ShareAlike 4.0 (CC BY-NC-SA 4.0)https://creativecommons.org/licenses/by-nc-sa/4.0/
License information was derived automatically
The GIS-based Time model of Gothenburg aims to map the process of urban development in Gothenburg since 1960 and in particular to document the changes in the spatial form of the city - streets, buildings and plots - through time. Major steps have in recent decades been taken when it comes to understanding how cities work. Essential is the change from understanding cities as locations to understanding them as flows (Batty 2013)1. In principle this means that we need to understand locations (or places) as defined by flows (or different forms of traffic), rather than locations only served by flows. This implies that we need to understand the built form and spatial structure of cities as a system, that by shaping flows creates a series of places with very specific relations to all other places in the city, which also give them very specific performative potentials. It also implies the rather fascinating notion that what happens in one place is dependent on its relation to all other places (Hillier 1996)2. Hence, to understand the individual place, we need a model of the city as a whole. Extensive research in this direction has taken place in recent years, that has also spilled over to urban design practice, not least in Sweden, where the idea that to understand the part you need to understand the whole is starting to be established. With the GIS-based Time model for Gothenburg that we present here, we address the next challenge. Place is not only something defined by its spatial relation to all other places in its system, but also by its history, or its evolution over time. Since the built form of the city changes over time, often by cities growing but at times also by cities shrinking, the spatial relation between places changes over time. If cities tend to grow, and most often by extending their periphery, it means that most places get a more central location over time. If this is a general tendency, it does not mean that all places increase their centrality to an equal degree. Depending on the structure of the individual city’s spatial form, different places become more centrally located to different degrees as well as their relative distance to other places changes to different degrees. The even more fascinating notion then becomes apparent; places move over time! To capture, study and understand this, we need a "time model". The GIS-based time model of Gothenburg consists of: • 12 GIS-layers of the street network, from 1960 to 2015, in 5-year intervals • 12 GIS-layers of the buildings from 1960 to 2015, in 5-year intervals • 12 GIS- layers of the plots from1960 to 2015, in 5-year intervals In the GIS-based Time model, for every time-frame, the combination of the three fundamental components of spatial form, that is streets, plots and buildings, provides a consistent description of the built environment at that particular time. The evolution of three components can be studied individually, where one could for example analyze the changing patterns of street centrality over time by focusing on the street network; or, the densification processes by focusing on the buildings; or, the expansion of the city by way of occupying more buildable land, by focusing on plots. The combined snapshots of street centrality, density and land division can provide insightful observations about the spatial form of the city at each time-frame; for example, the patterns of spatial segregation, the distribution of urban density or the patterns of sprawl. The observation of how the interrelated layers of spatial form together evolved and transformed through time can provide a more complete image of the patterns of urban growth in the city. The Time model was created following the principles of the model of spatial form of the city, as developed by the Spatial Morphology Group (SMoG) at Chalmers University of Technology, within the three-year research project ‘International Spatial Morphology Lab (SMoL)’. The project is funded by Älvstranden Utveckling AB in the framework of a larger cooperation project called Fusion Point Gothenburg. The data is shared via SND to create a research infrastructure that is open to new study initiatives. 1. Batty, M. (2013), The New Science of Cities, Cambridge: MIT Press. 2. Hillier, B., (1996), Space Is the Machine. Cambridge: University of Cambridge
Input description of the content here and how often it is updated.Data Source(s) Input list of data sources here.Customer(s) The dashboard was requested by Unknown for inclusion into the Bachelor Degree Attainment on Unknown date.Contact InformationPlease reach out to ceswg-ecg-geospatial@usace.army.mil with any questions/concerns.Release NotesUnknown
This is the official City of Eugene Streets - Tiled. It is used in nearly every Web Map and App that is used by the City of Eugene.This is housed on the City of Eugene ArcGIS server.For technical support, please contact: City of Eugene - Public Works Engineering, MAD GIS Team Leader Mike Miller at (541)682-5248.
Last updated: September 9, 2020Update frequency: ContinualThe Capital Improvements Program (CIP) identifies major funding priorities on a five-year fiscal cycle. Each year, those priorities are updated; projects can be completed, cancelled, or continue to be in a future state. The CIP is composed of 10 major areas: airport, combined, drainage, facilities, fire, parks, streets, traffic, wastewater, water. This layer aggregates all CIP fiscal cycles into a single sourceThe layer is based on polygons, these polygons were digitized from as-builts, parcel boundaries, schematics, and the descriptions of subject matter experts. The attribute information are created from the input of several departments, and their subject matter experts. Updates to project types under the jurisdiction of the Engineering CIP Group are made on automated nightly basis: combined, drainage, streets, traffic, wastewater, and water. Non-Engineering projects are updated on a bi-annual basis.This layer is currently used by the McKinney Active Infrastructure Projects Hub. It can be found at the following location:www.mckinneytexas.org/projectstatus
https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
The CAD and GIS printer market is projected to reach a value of XXX million by 2033, growing at a CAGR of XX% from 2025 to 2033. The growth of the market is primarily driven by the increasing demand for high-quality prints in various industries, including architecture, engineering, construction, and manufacturing. Additionally, the rising adoption of computer-aided design (CAD) and geographic information systems (GIS) software is further fueling the demand for specialized printers capable of handling complex designs and maps. The market for CAD and GIS printers is segmented based on application, type, and region. Major players in the market include Canon, Epson, Hewlett-Packard, Roland, Konica Minolta, Mimaki Engineering, Mutoh, and Ricoh. North America and Europe are expected to remain key regional markets, followed by Asia-Pacific. Emerging markets in Latin America, the Middle East, and Africa are also expected to contribute to the growth of the market in the coming years.
https://www.cognitivemarketresearch.com/privacy-policyhttps://www.cognitivemarketresearch.com/privacy-policy
According to Cognitive Market Research, the global LiDAR Services market size is USD 1354.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 15.20% from 2024 to 2031.
North America held the major market share for more than 40% of the global revenue with a market size of USD 541.68 million in 2024 and will grow at a compound annual growth rate (CAGR) of 13.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 406.26 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 311.47 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.2% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 67.71 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 27.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 14.9% from 2024 to 2031.
In 2023, the GIS service category experienced significant growth in the LiDAR market.
Market Dynamics of LiDAR Services Market
Key Drivers for LiDAR Services Market
Rising Construction Sector is Driving Market Growth
The size and complexity of engineering and civil construction operations around the world have expanded dramatically to accommodate an ever-increasing population, particularly in developing countries. Technology is increasingly vital in all stages of construction, from surveying and mapping to project feasibility analysis. LiDAR technologies provide a precise and simple scan of large areas. Furthermore, laser scanners powered by global positioning systems and very sensitive cameras help engineers conduct precise feasibility studies and create designs that match project requirements. This has led to the expansion of several LiDAR service providers.
The increasing use of LiDAR-based UAVs drives the market
LiDAR maps the earth's surface with rapid laser pulses. The expanding usage of LiDAR-based unmanned aerial vehicles (UAVs) offers new surveying applications that can be accomplished at a lower cost than traditional approaches. The widespread use of unmanned aerial vehicles (UAVs) has boosted airborne LiDAR-based surveying and mapping applications. Unmanned vehicle-mounted LiDAR systems provide not only mobility and agility but also the capacity to reach terrain and circumstances where humans cannot. Professionals are increasingly turning to unmanned aerial vehicles (UAVs) for low-altitude photography, terrain mapping, and surveying.
Restraint Factor for the LiDAR Services Market
Cost Considerations
Cost considerations are a significant limitation on the LiDAR market, preventing widespread adoption across numerous industries. Despite major advances in LiDAR technology, the cost of producing high-quality LiDAR sensors remains a substantial obstacle, especially for enterprises and industries with limited budgets. The numerous components and precision engineering required for LiDAR systems add to their high production costs. In industries such as agriculture, urban planning, and environmental monitoring, where cost-effectiveness is critical, the affordability of alternative sensing technologies presents a competitive challenge to LiDAR adoption. Industries researching LiDAR integration frequently face the challenge of reconciling the technology's evident benefits with the financial repercussions of its use.
Impact of Covid-19 on the LiDAR Services Market
The Covid-19 epidemic appears to have wreaked havoc on the LiDAR services sector. Initially, it slowed the market due to supply chain disruptions, project delays, and lower expenditures in new technology. Many construction and automotive companies which use LiDAR services extensively, experienced a temporary drop in demand. However, as the epidemic spread, the necessity for contactless and remote sensing technology such as LiDAR grew. Industries implemented LiDAR technology to monitor social distancing, automated processes to reduce human touch, and increased overall safety precautions. As a result of this shift in demand, the LiDAR services market regained momentum. Introduction of the LiDAR Services Market
The light detection and ranging (LiDAR) system is a remote ...
The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
The use of remote sensing techniques to identify (geo)archaeological features is wide spread. For archaeological prospection and geomorphological mapping, Digital Terrain Models (DTMs) on based LiDAR (Light Detection And Ranging) are mainly used to detect surface and subsurface features. LiDAR is a remote sensing tool that scans the surface with high spatial resolution and allows for the removal of vegetation cover with special data filters. Archaeological publications with LiDAR data in issues have been rising exponentially since the mid-2000s. The methodology of DTM analyses within geoarchaeological contexts is usually based on “bare-earth” LiDAR data, although the terrain is often significantly affected by human activities. However, “bare-earth” LiDAR data analyses are very restricted in the case of historic hydro-engineering such as irrigation systems, mills, or canals because modern roads, railway tracks, buildings, and earth lynchets influence surface water flows and may dissect the terrain. Consequently, a "natural" pre-modern DTM with high depth accuracy is required for palaeohydrological analyses. In this study, we present a GIS-based modelling approach to generate a pre-modern and topographically purged DTM. The case study focuses on the landscape around the Early Medieval Fossa Carolina, a canal constructed by Charlemagne and one of the major medieval engineering projects in Europe. Our aim is to reconstruct the pre-modern relief around the Fossa Carolina for a better understanding and interpretation of the alignment of the Carolingian canal. Our input data are LiDAR-derived DTMs and a comprehensive vector layer of anthropogenic structures that affect the modern relief. We interpolated the residual points with a spline algorithm and smoothed the result with a low pass filter. The purged DTM reflects the pre-modern shape of the landscape. To validate and ground-truth the model, we used the levels of recovered pre-modern soils and surfaces that have been buried by floodplain deposits, colluvial layers, or dam material of the Carolingian canal. We compared pre-modern soil and surface levels with the modelled pre-modern terrain levels and calculated the overall error. The modelled pre-modern surface fits with the levels of the buried soils and surfaces. Furthermore, the pre-modern DTM allows us to model the most favourable course of the canal with minimal earth volume to dig out. This modelled pathway corresponds significantly with the alignment of the Carolingian canal. Our method offers various new opportunities for geoarchaeological terrain analysis, for which an undisturbed high-precision pre-modern surface is crucial.
This linear chart displays the number of PERM cases filed for graduates in Forest Engineering (Gis) from 2020 to 2023, highlighting the trends and changes in sponsorship over the years. It provides a deep dive into how graduates in this specific major have engaged with potential employers for permanent residency in the U.S., illustrating the major’s effectiveness in connecting students with career opportunities that lead to permanent residency
http://dcat-ap.de/def/licenses/geonutz/20130319http://dcat-ap.de/def/licenses/geonutz/20130319
This GIS-capable vector data record contains statistical precipitation values depending on the duration level and return interval. A major area of application for the data is the engineering dimensioning of water management structures. These include e.g. B. sewer networks, sewage treatment plants, pumping stations and retention basins. They are also frequently used for the dimensioning of drainage systems and infiltration systems. With the help of the data, however, it is also possible to estimate the amount of precipitation from heavy rain events with regard to their annuality. This assessment is often used to evaluate damaging events.
Further information: https://opendata.dwd.de/climate_environment/CDC/grids_germany/return_periods/precipitation/KOSTRA/KOSTRA_DWD_2010R/gis/BESCHREIBUNG_gridsgermany_return_periods_precipitation_KOSTRA_KOSTRA_DWD_2010R_gis_de.pdf
U.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
The Middle Fork Willamette River Basin encompasses 3,548 square kilometers of western Oregon and drains to the mainstem Willamette River. Fall Creek Basin encompasses 653 square kilometers and drains to the Middle Fork Willamette River. In cooperation with the U.S. Army Corps of Engineers, the U.S. Geological Survey evaluated geomorphic responses of downstream river corridors to annual drawdowns to streambed at Fall Creek Lake. This study of geomorphic change is focused on the major alluvial channel segments downstream of the U.S. Army Corps of Engineers dams including the lowermost 11.5 km of Fall Creek and 27.3 km of the Middle Fork Willamette River, as well as Fall Creek Lake. GIS layers defining the landforms, cover type, vegetation density, and secondary water type throughout the active channel study area were developed for six time periods: 1936, 2005, 2011, 2012, 2014, and 2016. GIS layers defining the wetted channel centerline throughout the active channel study area were ...
This pie chart illustrates the distribution of degrees—Bachelor’s, Master’s, and Doctoral—among PERM graduates from Civil Engineering; Gis Graduate Program. It shows the educational composition of students who have pursued and successfully obtained permanent residency through their qualifications in Civil Engineering; Gis Graduate Program. This visualization helps to understand the diversity of educational backgrounds that contribute to successful PERM applications, reflecting the major’s role in fostering students’ career paths towards permanent residency in the U.S.
This data set provides an estimate of annual groundwater recharge for each public land survey section in Michigan. Groundwater Inventory and Mapping Project, a cooperative effort between the Water Bureau - Michigan Department of Environmental Quality (now Michigan Department of Environment, Great Lakes, and Energy), USGS - Michigan Water Science Center and Michigan State University - Institute of Water Research, RS&GIS and Biosystems and Agricultural Engineering. This project was mandated by P.A. 148 (Michigan Acts of 2003). Major funding was provided by EGLE (MDEQ at the time), supplemented with additional funds from the USGS Cooperative Water Program.Public Law 148 required the MDEQ to obtain a map of state-wide groundwater recharge. The US Geological Survey and Michigan State University have created this data set to meet that need.Accuracy of the recharge estimate is estimated to be +/- 2.44 inches/yr in the western and northern Lower Peninsula, +/- 1.1 in/yr in the southeastern Lower Peninsula, and +/- 2.9 inches/yr in the Upper Peninsula. Areas in the eastern Upper Peninsula (Luce, Chippewa, and Mackinaw Counties) may have higher error because of relatively poor representation of specific geologic environments.Base flow separations were compiled 208 USGS streamflow gages in Michigan from those completed by Neff and others (2005). Within each region, an average recharge rate was calculated based on the baseflow yield. Residuals were computed for each streamflow gage.Watershed characteristics describing the geology, land cover, and general climate characteristics of the gaged watersheds were also compiled. These data were analyzed in Systat v.11 using a forward stepwise regression procedure to identify watershed characteristics that might be useful in predicting the value fo the residual. Within the eastern Lower Peninsula, the significant predictive variables, in addition to area, were: agricultural land use, urban land use, annual growing degree days, annual precipitation, and percent of the watershed underlain by lacustrine deposits. Within the western Lower Peninsula, the significant predictive variables, in addition to area, were: winter (December through March) precipitation, the percentage of the watershed underlain by till, and the percentage of the watershed occupied by forests. In the Upper Peninsula, the significant predictive variables, in addition to area, were: growing degree days and winter precipitation.Each of these predictive variables were calculated for each Public Land Survey section, the data used to predict a residual, then the residual added to the base recharge prediction for the region. Attribute Label Attribute Definition
FID Internal feature number, Sequential unique whole numbers that are automatically generated
Shape Feature geometry, Coordinates defining the features
AREA Section area in square meters
PERIMETER Section perimeter in meters
TWN PLSS Township
RNG PLSS Range
SEC PLSS Section
COUNTY County ID
Recharge_I Inches of annual groundwater recharge Neff, B.P., Day, S.M., Piggott, A.R., and Fuller, L.M., Base Flow in the Great Lakes Basin: U.S. Geological Survey Scientific Investigations Report 2005-5217, 23 p.
https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy
Market Overview The RTK GNSS Base Station market is projected to reach a market value of approximately XXX million by 2033, expanding at a CAGR of XX% from 2025 to 2033. The growth of this market is primarily driven by the increasing adoption of RTK technology in surveying and geographic information system (GIS) applications, agriculture, and construction engineering. The growing demand for precise positioning and mapping solutions has led to the widespread adoption of RTK GNSS Base Stations, which provide real-time, centimeter-level accuracy for various applications. Market Segmentation and Key Trends The RTK GNSS Base Station market is segmented based on its applications, types, and geographic regions. The major application segments include surveying and GIS, agriculture, and construction engineering. In terms of types, the market is categorized into single-frequency RTK, dual-frequency RTK, and multi-frequency RTK. Key industry players in this market include Trimble, HEXAGON, SOKKIA, CHCNAV, and Hi-Target. The market is influenced by various trends, such as the miniaturization of RTK receivers, improved accuracy of GNSS technology, and the integration of IoT devices. North America, Europe, and Asia-Pacific are the key regions driving the growth of the RTK GNSS Base Station market. This in-depth report provides a comprehensive analysis of the RTK GNSS Base Station market, covering key market insights, trends, and growth drivers.
This linear chart displays the number of PERM cases filed for graduates in Civil Engineering; Gis Graduate Program from 2020 to 2023, highlighting the trends and changes in sponsorship over the years. It provides a deep dive into how graduates in this specific major have engaged with potential employers for permanent residency in the U.S., illustrating the major’s effectiveness in connecting students with career opportunities that lead to permanent residency
Geotechnical reports are indexed within a database maintained by HPW-TEB Geotechnical Unit. Meta data associated to each geotechnical report are captured within this indexing table, including report reference number, title, author, highway and km start and end. The table has been modified to include columns that aid in georeferencing geotechnical reports. Added columns include route ID, Latitude, and Longitude. Transportation Engineering Branch is continually improving its geographical information systems with a major focus on creating linear referencing routes within ArcGIS. Georeferencing geotechnical reports will utilize the linear referencing routes in creating points and line shape files by referencing the highway number and km points or ranges as defined within the indexing table. Distributed from GeoYukon by the Government of Yukon. Discover more digital map data and interactive maps from Yukon’s digital map data collection.For more information: geomatics.help@yukon.ca
The 3D model is available for visualization purposes only. The source data is not available for download and cannot be shared.
Auckland 3D Model SpecificationsResolution: 5 cm Accuracy: 25 cm (RMSE) Area: ~ 9.2 sq. km. Captured: Feb 2020 3D Capture & Processing This 3D model has been generated using advanced aerial acquisition and photogrammetry techniques that extract accurate geometric information from aerial photographs. Extremely dense 3D point clouds are generated prior to being transformed into a fully-textured mesh object. To generate geographically accurate data, precise ground control points are integrated into our processing chain. Special care and quality control systems are implemented at all stages of the process. Major ApplicationsTown planning, construction & engineering, transport and traffic management, asset management, tourism and investment, risk and emergency management, urban logistics
Not seeing a result you expected?
Learn how you can add new datasets to our index.
https://www.myvisajobs.com/terms-of-service/https://www.myvisajobs.com/terms-of-service/
A dataset that explores Green Card sponsorship trends, salary data, and employer insights for forest engineering (gis) in the U.S.