Users can browse the map interactively or search by lot ID or address. Available basemaps include aerial images, topographic contours, roads, town landmarks, conserved lands, and individual property boundaries. Overlays display landuse, zoning, flood, water resources, and soil characteristics in relation to neighborhoods or parcels. Integration with Google Street View offers enhanced views of the 2D map location. Other functionality includes map markup, printing, viewing the property record card, and links to official tax maps where available.NRPC's implementation of MapGeo dates back to 2013, however it is the decades of foundational GIS data development at NRPC and partner agencies that has enabled its success. NRPC refreshes the assessing data yearly; the map data is maintained in an ongoing manner.

NZ Parcel Boundaries Wireframe provides a map of land, road and other parcel boundaries, and is especially useful for displaying property boundaries.
This map service is for visualisation purposes only and is not intended for download. You can download the full parcels data from the NZ Parcels dataset.
This map service provides a dark outline and transparent fill, making it perfect for overlaying on our basemaps or any map service you choose.
Data for this map service is sourced from the NZ Parcels dataset which is updated weekly with authoritative data direct from LINZ’s Survey and Title system. Refer to the NZ Parcel layer for detailed metadata.
To simplify the visualisation of this data, the map service filters the data from the NZ Parcels layer to display parcels with a status of 'current' only.
This map service has been designed to be integrated into GIS, web and mobile applications via LINZ’s WMTS and XYZ tile services. View the Services tab to access these services.
See the LINZ website for service specifications and help using WMTS and XYZ tile services and more information about this service.

Approximate boundaries for all land parcels in New Brunswick. The boundaries are structured as Polygons. The Property Identifier number or PID is included for each parcel.

California department of transportation (Caltrans), Division of Aeronautics provided airport layout drawings with estimated digitized airport property or fence lines with Google Pro images background. Caltrans Division of Research, Innovation and System Information (DRISI) GIS office digitized the airport boundary lines with Bing Maps Aerial background and built the boundary lines into a GIS polygon feature class. Generally, Airport Layout Plans do not show complete connected property or fence lines. In many cases the boundary lines were interpreted among the property and fence lines with our best judgement. The airport general information derived from FAA Aiport Master Record and Reports with their URL are included in the attribute table. Airport boundary data is intended for general reference and does not represent official airport property boundary determinations.

While the Waimakariri District Council has taken all reasonable care in providing correct information, all information should be considered as being illustrative and indicative only. Your use of this information is entirely at your own risk. You should independently verify the accuracy of any information before taking any action in reliance upon it.Read full disclaimer here.Abstract:This layer is derived from current primary parcels, as per the NZ Parcels layer on the LINZ Data Service, joined to data on matching current/future properties in WDC’S rating database.Note, this dataset includes a boundary for the primary property address only (as identified in WDC’s rating database) and does not include a boundary for all addresses that may exist on a property.Other information:Addresses:The address datasets contain street number, street name and suburb for physical addresses in Waimakariri.There can be multiple addresses on a property and an example of these are granny flats, farm cottages etc.Click here to view Address Boundary LayerClick here to view Address Point LayerUpdate Frequency:DailyPoint of Contact:Waimakariri District CouncilLineage:Data has been compiled from a number of sources and its accuracy may vary (e.g. Field Verification, Deposited Plans, AsBuilt plans and forms, sketches, aerial photo, Google Street View). There may be delays before data is updated to reflect changes in an area.

ParcelMap BC is the current, complete and trusted mapped representation of titled and Crown land parcels across British Columbia, considered to be the point of truth for the graphical representation of property boundaries. It is not the authoritative source for the legal property boundary or related records attributes; this will always be the plan of survey or the related registry information. This particular dataset is a subset of the complete ParcelMap BC data and is comprised of the parcel fabric and attributes for over two million parcels published under the Open Government Licence - British Columbia. Notes: 1. Parcel title information is sourced from the BC Land Title Register. Title questions should be directed to a local Land Title Office. 2. This dataset replaces the Integrated Cadastral Fabric.

This data provides the integrated cadastral framework for Canada Lands. The cadastral framework consists of active and superseded cadastral parcel, roads, easements, administrative areas, active lines, points and annotations. The cadastral lines form the boundaries of the parcels. COGO attributes are associated to the lines and depict the adjusted framework of the cadastral fabric. The cadastral annotations consist of lot numbers, block numbers, township numbers, etc. The cadastral framework is compiled from Canada Lands Survey Records (CLSR), registration plans (RS) and location sketches (LS) archived in the Canada Lands Survey Records.

The PLSS is the basis for Federal land ownership. This data includes township, range, section (first Division), and Intersected.

There are four layers loaded that are scale dependant with scale dependant labels. At the smallest scales, the state boundaries appear, as the user zooms in Townships and then Section then PLSS Intersected boundaries appears.

Statewide soil and land information can be discovered and viewed through eSPADE or SEED. Datasets include soil profiles, soil landscapes, soil and land resources, acid sulfate soil risk mapping, hydrogeological landscapes, land systems and land use. There are also various statewide coverages of specific soil and land characteristics, such as soil type, land and soil capability, soil fertility, soil regolith, soil hydrology and modelled soil properties.

Both eSPADE and SEED enable soil and land data to be viewed on a map. SEED focuses more on the holistic approach by enabling you to add other environmental layers such as mining boundaries, vegetation or water monitoring points. SEED also provides access to metadata and data quality statements for layers.

eSPADE provides greater functions and allows you to drill down into soil points or maps to access detailed information such as reports and images. You can navigate to a specific location, then search and select multiple objects and access detailed information about them. You can also export spatial information for use in other applications such as Google Earth™ and GIS software.

eSPADE is a free Internet information system and works on desktop computers, laptops and mobile devices such as smartphones and tablets and uses a Google maps-based platform familiar to most users. It has over 42,000 soil profile descriptions and approximately 4,000 soil landscape descriptions. This includes the maps and descriptions from the Soil Landscape Mapping program. eSPADE also includes the base maps underpinning Biophysical Strategic Agricultural Land (BSAL).

For more information on eSPADE visit: https://www.environment.nsw.gov.au/topics/land-and-soil/soil-data/espade

Identifies location, the type of sport played and condition, age and other details about the recreational facility. Data contains approximately 80 types of sport including private gyms and fitness centres. The location of facilities was checked using a range of spatial data including current aerial photos, LGA and property boundaries as well as Google maps. If feasible they were geocoded via Victorian Mapping Address System (VMAS).

Vector polygon map data of city limits from Houston, Texas containing 731 features.

City limits GIS (Geographic Information System) data provides valuable information about the boundaries of a city, which is crucial for various planning and decision-making processes. Urban planners and government officials use this data to understand the extent of their jurisdiction and to make informed decisions regarding zoning, land use, and infrastructure development within the city limits.

By overlaying city limits GIS data with other layers such as population density, land parcels, and environmental features, planners can analyze spatial patterns and identify areas for growth, conservation, or redevelopment. This data also aids in emergency management by defining the areas of responsibility for different emergency services, helping to streamline response efforts during crises..

This city limits data is available for viewing and sharing as a map in a Koordinates map viewer. This data is also available for export to DWG for CAD, PDF, KML, CSV, and GIS data formats, including Shapefile, MapInfo, and Geodatabase.

This dataset was created by the Transportation Planning and Programming (TPP) Division of the Texas Department of Transportation (TxDOT) for planning and asset inventory purposes, as well as for visualization and general mapping. County boundaries were digitized by TxDOT using USGS quad maps, and converted to line features using the Feature to Line tool. This dataset depicts a generalized coastline.Update Frequency: As NeededSource: Texas General Land OfficeSecurity Level: PublicOwned by TxDOT: FalseRelated LinksData Dictionary PDF [Generated 2025/03/14]

The Easiest Way to Collect Data from the Internet Download anything you see on the internet into spreadsheets within a few clicks using our ready-made web crawlers or a few lines of code using our APIs

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Easy to Use Crawlers Amazon Product Details and Pricing Scraper Amazon Product Details and Pricing Scraper Get product information, pricing, FBA, best seller rank, and much more from Amazon.

Google Maps Search Results Google Maps Search Results Get details like place name, phone number, address, website, ratings, and open hours from Google Maps or Google Places search results.

Twitter Scraper Twitter Scraper Get tweets, Twitter handle, content, number of replies, number of retweets, and more. All you need to provide is a URL to a profile, hashtag, or an advance search URL from Twitter.

Amazon Product Reviews and Ratings Amazon Product Reviews and Ratings Get customer reviews for any product on Amazon and get details like product name, brand, reviews and ratings, and more from Amazon.

Google Reviews Scraper Google Reviews Scraper Scrape Google reviews and get details like business or location name, address, review, ratings, and more for business and places.

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[Metadata] Agricultural Land Use (2015). Source: The University of Hawaii at Hilo Spatial Data Analysis and Visualization (SDAV) Laboratory in conjunction with the Hawaii State Department of Agriculture, 2015. The 2015 Hawaii Statewide Agricultural Land Use Baseline layer was created to provide a snapshot of contemporary commercial agricultural land use activity in Hawaii. It is based upon an assemblage of geospatial datasets, primarily high-resolution WorldView-2 satellite imagery (2011-2013) used as a base layer for digitization. Additional datasets used in this work include GIS layers (‘Agriculture and Farming’, ‘Inland Water Resources’, and ‘Cadastral and Land Descriptions’) provided by the state of Hawaii, Office of Planning Statewide GIS Program and other data provided by major land owners and managers. Digitized crop locations and boundaries were verified through a combination of on-the-ground site visits, meetings and presentations of draft layers with agricultural stakeholders and landowners, solicitations through a publicly accessible online web mapping portal, and spot-checking using Google Earth™ and other high resolution imagery sources. In addition to the satellite imagery, County Real Property Tax and Agricultural Water Use data were also used to identify commercial farm operations. Data for both real property tax assessment and agricultural water use were collected from each county that provided their most recent records, generally from 2014-2015. Not all properties that receive County agricultural tax assessment rates or reduced water cost for agricultural uses were mapped due to the small scale of some of their operations. These data sources were used to verify mapped commercial farms and identify operations that might have been missed using the imagery alone. The 2015 Hawaii Statewide Agricultural Land Use Baseline layer represents our best efforts to capture the scale and diversity of commercial agricultural activity in Hawaii in 2015 and should be used for informational purposes only.Note: April 2022: Several users of the data discovered that the original 2015 Hawaii Statewide Agricultural Land Use Baseline layer and the 2020 update to the Hawaii Statewide Agricultural Land Use Baseline layer did not overlay properly, with an offset between the layers of 10 feet to 40 feet, depending on the area. As a result, both the original and the updated layers have been republished, and now overlay as they should. The underlying data itself has not changed.Please note - if you download the data from the State's geoportal (https://geoportal.hawaii.gov/), the data is exported in WGS84 coordinates, although it is stored internally (in the State's geodatabase), served in the State's web services (https://geodata.hawaii.gov/arcgis/rest/services), and made available in the State's legacy download site (https://planning.hawaii.gov/gis/download-gis-data-expanded/) in UTM/NAD 83 HARN coordinates.For additional information, please refer to complete metadata at https://files.hawaii.gov/dbedt/op/gis/data/aglanduse_2015.pdf or contact Hawaii Statewide GIS Program, Office of Planning and Sustainable Development, State of Hawaii; PO Box 2359, Honolulu, HI 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This dataset contains documentation on the 146 global regions used to organize responses to the ArchaeGLOBE land use questionnaire between May 18 and July 31, 2018. The regions were formed from modern administrative regions (Natural Earth 1:50m Admin1 - states and provinces, https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-1-states-provinces/). The boundaries of the polygons represent rough geographic areas that serve as analytical units useful in two respects - for the history of land use over the past 10,000 years (a moving target) and for the history of archaeological research. Some consideration was also given to creating regions that were relatively equal in size. The regionalization process went through several rounds of feedback and redrawing before arriving at the 146 regions used in the survey. No bounded regional system could ever truly reflect the complex spatial distribution of archaeological knowledge on past human land use, but operating at a regional scale was necessary to facilitate timely collaboration while achieving global coverage. Map in Google Earth Format: ArchaeGLOBE_Regions_kml.kmz Map in ArcGIS Shapefile Format: ArchaeGLOBE_Regions.zip (multiple files in zip file) The shapefile format is a digital vector file that stores geographic location and associated attribute information. It is actually a collection of several different file types: .shp — shape format: the feature geometry .shx — shape index format: a positional index of the feature geometry .dbf — attribute format: columnar attributes for each shape .prj — projection format: the coordinate system and projection information .sbn and .sbx — a spatial index of the features .shp.xml — geospatial metadata in XML format .cpg — specifies the code page for identifying character encoding Attributes: FID - a unique identifier for every object in a shapefile table (0-145) Shape - the type of object (polygon) World_ID - coded value assigned to each feature according to its division into one of seventeen ‘World Regions’ based on the geographic regions used by the Statistics Division of the United Nations (https://unstats.un.org/unsd/methodology/m49/), with small changes to better reflect archaeological scholarly communities. These large regions provide organizational structure, but are not analytical units for the study. World_RG - text description of each ‘World Region’ Archaeo_ID - unique identifier (1-146) corresponding to the region code used in the ArchaeoGLOBE land use questionnaire and all ArchaeoGLOBE datasets Archaeo_RG - text description of each region Total_Area - the total area, in square kilometers, of each region Land-Area - the total area minus the area of all lakes and reservoirs found within each region (source: https://www.naturalearthdata.com/downloads/10m-physical-vectors/10m-lakes/) PDF of Region Attribute Table: ArchaeoGLOBE Regions Attributes.pdf Excel file of Region Attribute Table: ArchaeoGLOBE Regions Attributes.xls Printed Maps in PDF Format: ArchaeoGLOBE Regions.pdf Documentation of the ArchaeoGLOBE Regional Map: ArchaeoGLOBE Regions README.doc

While the Waimakariri District Council has taken all reasonable care in providing correct information, all information should be considered as being illustrative and indicative only. Your use of this information is entirely at your own risk. You should independently verify the accuracy of any information before taking any action in reliance upon it.Read full disclaimer here.Abstract:This layer is derived from current primary parcels, as per the NZ Parcels layer on the LINZ Data Service, joined to data on matching current/future properties in WDC’S rating database.Note, this dataset includes a boundary for the primary property address only (as identified in WDC’s rating database) and does not include a boundary for all addresses that may exist on a property.Other information:Addresses:The address datasets contain street number, street name and suburb for physical addresses in Waimakariri.There can be multiple addresses on a property and an example of these are granny flats, farm cottages etc.Click here to view Address Boundary LayerClick here to view Address Point LayerUpdate Frequency:DailyPoint of Contact:Waimakariri District CouncilLineage:Data has been compiled from a number of sources and its accuracy may vary (e.g. Field Verification, Deposited Plans, AsBuilt plans and forms, sketches, aerial photo, Google Street View). There may be delays before data is updated to reflect changes in an area.

This reference database in vector format (ESRI shape format) is organised according to a multi-level nomenclature. It is used to train an image classification algorithm with a view to producing land use maps for the Itashy and Vakinankaratra regions as part of the DINAAMICC project. Each GPS point was converted into a polygon by digitising the boundaries of the corresponding plot on the Google Satellite background and/or the mosaic of Spot 6/7 images acquired in 2022 and 2023 during the growing season. In addition, other polygons were digitised by photo-interpretation of the images. The database covers the entire study area in order to be representative of existing crop types and perennial structures. The final database contains 3726 polygons. Cette base de données de référence au format vecteur (ESRI shape format) est organisée selon une nomenclature à plusieurs niveaux. Elle est utilisée pour entrainer un algorithme de classification d’images en vue de produire des cartes d’occupation du sol sur les régions Itashy et Vakinankaratra dans le cadre du projet DINAAMICC. Les points GPS ont été relevés en mars et avril 2023 par deux enquêteurs formés à l'utilisation d'une tablette-GPS mobilisant l'application QField. Chaque point GPS a été converti en polygone en numérisant les limites de la parcelle correspondante sur le fonds Google Satellite et/ou la mosaïque d'images Spot 6/7 acquises en 2022 et 2023 pendant la saison de croissance des cultures. En complément d'autres polygones ont été numérisés par photo-interprétation des images. La base de données, couvrent l’ensemble de la zone d’étude afin d'avoir une représentativité des types de cultures et des structures pérennes existantes. La base de données finale compte 3726 polygones.

As one of the plain wetland systems in northern China, Baiyangdian Wetland plays a key role in ensuring the water resources security and good ecological environment of Xiong'an New Area. Understanding the current situation of Baiyangdian Wetland ecosystem is also of great significance for the construction of the New Area and future scientific planning. Based on the 10-meter spatial resolution sentinel-2B image provided by ESA in September 2017, combined with Google Earth high resolution satellite image (resolution 0.23m), the wetland ecosystem network distribution map and river network distribution map of in Baiyangdian basin in 2017 were drawn by artificial visual interpretation and machine automatic classification, which can provide reference for the wetland connectivity (including hydrological connectivity and landscape connectivity) in Baiyangdian basin. The spatial distribution data set of Baiyangdian Wetland includes vector data and raster data: (1) Baiyangdian basin boundary data (.shp); Baiyangdian basin river channel data (. shp); (2) Baiyangdian basin land use / cover classification data (including the classification data of Baiyangdian basin and the river 3 km buffer) (.tif); Baiyangdian basin constructed wetland and natural wetland distribution map (. shp); Baiyangdian basin slope map (. tif). The boundary of Baiyangdian basin in this dataset comes from the basic geographic information map of Baiyangdian basin provided by Zhou Wei and others. The DEM is the GDEM digital elevation data with 30m resolution. The original image data of wetland remote sensing classification comes from the sentinel-2B remote sensing image on September 20, 2017 provided by ESA. This data set uses the second, third, fourth and eighth bands of the 10m resolution in the image. The preprocessing operations such as radiometric calibration, mosaic and mosaic are carried out in SNAP and ArcGIS 10.2 software, and the supervised classification is carried out in ENVI software. The data used for river channel extraction is based on Google Earth high resolution satellite images. The research and development steps of this dataset include: preprocessing sentinel-2B image, establishing wetland classification system and selecting samples, drawing the latest wetland ecosystem network distribution map of Baiyangdian basin by support vector machine classification; based on Google Earth high-resolution satellite image (resolution 0.23m), this paper uses LocaSpaceViewer software to identify and extract river channels by manual visual interpretation. For the river channels with embankment, identify and draw along the embankment; for the river channels without embankment, distinguish according to the spectral difference between the river channels and the surrounding land use types and empirical knowledge, mark the uncertain areas, and conduct field investigation in the later stage, which can ensure that the identified river channels have been extracted. The identified river channels include the main river channel, each classified river channel, abandoned river channel, etc., and all rivers are continuous. It can effectively identify the channel and ensure the accuracy of extraction. According to the river network map of Baiyangdian basin obtained by manual visual interpretation, the total length of the river in Baiyangdian basin is about 2440 km, and the total area is 514 km2. Among them, there are 177 km2 river channels in mountainous area, with a length of 866 km, distributed in northeast-southwest direction, mostly at the junction of forest land and cultivated land; there are 337 km2 river channels in plain area, with a length of 1574 km. The Baiyangdian basin is divided into eight land use / cover types: river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land. The remote sensing monitoring results show that the wetland area of Baiyangdian basin accounted for 13.90% in 2017. Among all the wetland types, the area of marsh is the largest, followed by the area of flood plain, ditch accounts for about 1%, and the proportion of lake and river is less than 0.5%. Combined with the land use / cover classification map and the distribution of slope and elevation, it can be seen that nearly 60% of the area of forest land is distributed in 10 ° to 30 ° mountain area, and the rest of the land use / cover types are mainly distributed in 0 ° to 2 ° area. The elevation statistics show that nearly 80% of the lakes and large reservoirs are distributed in the height of 100 m to 300 m, the distribution of marsh is relatively uniform, mainly in the higher altitude area of 20 m to 300 m, the types of construction land, flood area and cultivated land are mainly concentrated in the area of 20 m to 100 m, and rivers and ditches are mainly concentrated in the area of 0 m to 100 m. Based on the classification results of land use / cover within the river, it can be found that the main land use type is wetland. Specifically, the types of marsh, flood area and lake are the most, while the types of ditch and river are less. With the increase of the buffer area, the proportion of non-wetland type gradually increased, while the proportion of wetland type gradually decreased. The main wetland types in 1-3km buffer zone on both sides of the river are marsh and flood zone. It is worth noting that nearly one third of the River belongs to cultivated land, that is, the river occupation is serious. In terms of area, about 1 / 3 rivers and 3 / 4 lakes are distributed in the river course. Most of the water bodies in the river course are controlled by human beings, but the marsh area in the river course only accounts for about 3% of the marsh area in the whole river course. In this study, 8 types of land features including river, flood plain, lake, marsh, ditch, cultivated land, forest land and construction land were selected. The total number of samples was 5199, of which 67% was used for supervised classification and 33% for accuracy verification of confusion matrix. The overall accuracy of support vector machine (SVM) classification results in Baiyangdian basin is 84.25%, and kappa coefficient is 0.82. River occupation will not only directly reduce the connectivity of wetlands in the basin, but also cause some environmental and economic problems such as water pollution. However, if the connectivity of wetlands is reduced, the ecological and environmental functions of wetlands will be destroyed, which will pose a great threat to the water security of the basin. Taking Baiyangdian basin as a whole, improving the connectivity of wetlands and enhancing the ecological and environmental functions of wetlands in the basin will help to improve the water ecological and environmental security of Xiong'an New Area and Baiyangdian basin.

Images were acquired from approximately 80 m above ground surface on the 12th of February 2021, using a Phantom 4 Advanced drone with an FC330 camera. The images are in file input_images.zip.

The mission planning software DJI GS Pro was used to automatically acquire images at suitable locations across the survey area to enable the reconstruction of a three dimensional model.

Images 422 to 531 were imported to the photogrammetry software Pix4D (version 4.6.4). The created Pix4D project is Station12Feb2021_limited.p4d, and the processing report is Station12Feb2021_limited_report.pdf.

Four three-dimensional ground control points were used to improve the positioning of the model. No two dimensional control points or check points were used.

These points were in ITRF 2000@2000 datum (UTM Zone 49S), with co-ordinates as per the table below:

Label, Type, X(m), Y(m), Z(m), Accuracy Horz(m), Accuracy Vert(M) BM05, 3D GCP, 478814.460, 2648561.910, 38.558, 0.050, 0.100 EW-05, 3D GCP, 478635.540, 2648617.260, 27.260, 0.050, 0.100 FuelFlange, 3D GCP, 478970.810, 2648642.250, 21.920, 0.050, 0.100 MeltbellFootingA, 3D GCP, 478680.270, 2648466.547, 35.850, 0.050, 0.100

BM-05 is a survey benchmark near the Casey flagpoles, see https://data.aad.gov.au/aadc/survey/display_station.cfm?station_id=600 EW-05 is a 44 gallon drum used as a groundwater extraction well by the remediation project Fuel Flange is the last fuel flange located on the elevated fuel line prior to the fuel line “dipping” under the wharf road. Meltbell footing A is a concrete footing for the Casey melt bell (surveyed in 2019/20).

No point cloud processing (e.g. removal of errant points) was done prior to orthomosaic and model generation.

The resulting orthomosaic (Station12Feb2021_limited_transparent_mosaic_group1.tif) has an average ground sampling distance of 2.9 cm, and covers an area of approximately 15.8 hectares, encompassing the majority of buildings along “main street” at Casey. The quarry, biopiles, helipad, and upper fuel farm area are all visible.

Contour lines were generated in Pix4D at 0.5 m intervals.

Due to the limited number of ground control points, and their imprecision, the estimated residual mean squared error across three dimensions is 0.17 m (17cm), and will be worse on the periphery of the imaged area.

The orthomosaic was exported from ArcGIS to a Google Earth file (CaseyStation Orthomosaic Feb 12 2021.kmz) using XTools Pro Version 17.2.

A map was created in ArcGIS showing the orthomosaic with a background showing contour lines obtained from the AADC data product windmill_is.mdb.

The map was exported in .jpg and .pdf format at 250 dpi. Casey Station Orthomosaic Feb 12 2021.pdf Casey Station Orthomosaic Feb 12 2021.jpg

The Pix4D folder structure has been copied across (with the exception of the temp folder) and is included in this dataset.

Pix4D Folder Structure:

Station12Feb2021_limited.zip 1_intitial • Contains Pix4D files created during the project • Contains the final processing report (as .pdf) 2_densification • Contains the 3D mesh as an .obj file • Contains the point cloud as a .LAS and .PLY file • Contains the point cloud as a .p4b file 3_dsm_ortho • Contains the digital surface model as a georeferenced .tif file • Contains the orthomosaic as a georeferenced .tif file

A text readable log file from the project processing is in the file Station12Feb2021_limited.log