Map of DAS, nodal, vibroseis and Reftek stations during March 2016 deployment. The plot on the left has nodal stations labeled; the plot on the right has vibroseis observations labeled. Stations are shown in map-view using Brady's rotated X-Y coordinates with side plots denoting elevation with respect to the WGS84 ellipsoid. Blue circles denote vibroseis data, x symbols denote DAS (cyan for horizontal and magenta for vertical), black asterisks denote Reftek data, and red plus signs denote nodal data. This map can be found on UW-Madison's askja server at /PoroTomo/DATA/MAPS/Deployment_Stations.pdf

This data set provides a means of identifying an x-y coordinate for the approximate center (centroid) of landnet units based on the corresponding standardized PLSS description (e.g., for PLSS Section this is DTRS -- Direction, Township, Range, and Section codes). This process is sometimes referred to as "protraction". The Landnet centroid shapefile includes coordinates in WTM83/91 and latitude/longitude expressed as decimal degrees or degrees, minutes and seconds.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2017, primarily as reported through the Census Bureau's Boundary and Annexation Survey (BAS).

PLSS Townships and Sections dataset current as of 2008. Public Land Survey square-mile section boundaries within Sedgwick County. Layer was developed interactively by GIS staff. Primary attribues include section, township, and range identifiers, and x-y coordinates, and Public Safety (ortho) map numbers..

New Parking Citations dataset here: https://data.lacity.org/Transportation/Parking-Citations/4f5p-udkv/about_data ---Archived as of September 2023--- Parking citations with latitude / longitude (XY) in US Feet coordinates according to the California State Plane Coordinate System - Zone 5 (https://www.conservation.ca.gov/cgs/rgm/state-plane-coordinate-system). For more information on Geographic vs Projected coordinate systems, read here: https://www.esri.com/arcgis-blog/products/arcgis-pro/mapping/gcs_vs_pcs/ For information on how to change map projections, read here: https://learn.arcgis.com/en/projects/make-a-web-map-without-web-mercator/

Feature extracted from Signs layer containing all Stop, Be Prepared To Stop, Stop Here signs in West Virginia. Data sets include RouteID, Sign ID Number, County Code, Route Number, Sub Route Number, Sign System, Supplemental Code, Supplemental Description, Direction, Milepoint, Number of Signs, Location, Mutcdname and Mutcode, Mutcdcat, Text, County, Photo URL, and XY Coordinates. Data is current as of 2015 and is updated as needed . Coordinate System: NAD_1983_UTM_Zone_17N.

This feature class is updated every business day using Python scripts and the Permit database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates.This dataset contains the points of diversion (POD) for water rights based on the coordinate location (XY) provided in the NDWR’s Permit Database. Since there can be multiple permits on the same POD site, this dataset contains duplicate point features where several permits may be stacked on top of each other spatially. The advantage to using this dataset is that all permits in NDWR’s Permit database are available. Use a filter or definition query to restrict the permits needed.Background:NDWR’s Permit Database was created in 1992. Water Right applications are entered into the database with the Township Range and Section (TRS) of the proposed place of use). The Permit Database was designed to automatically create the point of diversion (POD) based on the centroid of the TRS provided.Starting in 2007, the Hydrology section began mapping PODs by the permit application description. Water rights points of diversion are mapped that contain one of the following: coordinate location (XY), bearing/distance based on a monument tie, application map that can be georeferenced, parcel number, or location description that can be identified on a topo map. The workflow for mapping PODs includes updating the auto-generated POD in the Permit Database to the location coordinates derived from mapping the application description. Some older water rights including Vested or Decreed Water Rights may not be mapped due to lack of sufficient location information.The Water Rights Section of NDWR is responsible for reviewing and approving water rights applications, for new appropriations and for changes to existing water rights, as well as evaluating and responding to protests of applications, approving subdivision dedications for water quantity, evaluating domestic well credits and relinquishments, issuing certificates for permitted water rights, conducting field investigations, and processing requests for extensions of time for filing proofs of completion and proofs of beneficial use.Please note that this POD feature class may not contain all water right information on a site or permit. The GIS datasets do not replace the need to review the Permit database and hard copy permit files and are intended for convenience in sharing information on a map, finding a location, seeing spatial patterns, and planning.Code Descriptions:app_status app_status_nameABN ABANDONED (inactive)ABR ABROGATED (inactive)APP APPLICATION (pending)CAN CANCELLED (inactive)CER CERTIFICATE (active)CUR CURTAILED (inactive)DEC DECREED (active)DEN DENIED (inactive)EXP EXPIRED (inactive)FOR FORFEITED (inactive)PER PERMIT (active)REJ REJECTED (inactive)REL RELINQUISHED (inactive)RES RESERVED (pending)RFA READY FOR ACTION (pending)RFP READY FOR ACTION PROTESTED (pending)RLP RELINQUISH A PORTION (active)RSC RESCINDED (inactive)RVK REVOKED (inactive)RVP REVOCABLE PERMIT (active)SUP SUPERSEDED (inactive)SUS SUSPENDED (inactive)VST VESTED RIGHT (pending)WDR WITHDRAWN (inactive)manner of use (mou) use_nameCOM COMMERCIALCON CONSTRUCTIONDEC AS DECREEDDOM DOMESTICDWR DEWATERINGENV ENVIRONMENTALIND INDUSTRIALIRC IRRIGATION-CAREY ACTIRD IRRIGATION-DLEIRR IRRIGATIONMM MINING AND MILLINGMUN MUNICIPALOTH OTHERPWR POWERQM QUASI-MUNICIPALREC RECREATIONALSTK STOCKWATERINGSTO STORAGEUKN UNKNOWNWLD WILDLIFEMMD MINING, MILLING AND DEWATERINGEVP EVAPORATIONsource source_nameEFF EFFLUENTGEO GEOTHERMALLAK LAKEOGW OTHER GROUND WATEROSW OTHER SURFACE WATERRES RESERVOIRSPR SPRINGSTO STORAGESTR STREAMUG UNDERGROUNDDate Field Descriptions:Permit Date—Date the permit was issued.File Date—Date application was filed at the Division.Sent for Publication—Date the notice that the application was filed was sent to the newspaper of record for publication.Last Publication—The last date of publication of said notice in the paper; 30 days from this date is the last day for filing a protest to an application.POC Filed Date—When a Proof of Completion of Work is accepted by this office, it becomes “filed” rather than just received. The filed date is the same as the received date.

This map shows the bike paths of all Pace rides in 2018 in the form of XY coordinate points. The NSC (Neighborhood Service Center) Quadrant feature layer lays underneath the point layer as to give a visual division of activity in each of the four quadrants of Rochester.Note: depending on the basemap you choose, you may have to zoom out and locate to Rochester.

Orthophotography of the Hauts-de-Seine department carried out in 1998 This orthophotography of the Hauts-de-Seine territory was taken in the summer of 1998. The technical characteristics of this orthophotography are as follows: Projection: RGF93-CC49/IGN69 Resolution: 25 cm Shooting period: summer 1998 Format: JPEG Specific comments This vertical aerial view has been orthorectified, that is to say, the deformations related to the relief and perspective have been eliminated in order to propose an image superimposed on a map. This orthophotography shall be made available in the form of: 1 km x 1 km georeferenced tiles of “zip” files containing the tile in JPEG format and its associated georeferencing file the syntax used for the naming of tiles: “ORT_AAAA_XXXX_YYYY”, uses the XY coordinates in RGF93-CC49 according to the Xmin_Ymax standard of the relevant slab the download of a slab is done by clicking on the slab in the table or in the map and downloading the associated zip file Aerial shooting is also offered as a standardised map service (WMS). The prohibited areas prohibited from aerial shooting in the right-of-way of speech therapy have been blurred in accordance with the Order of 27 October 2017 establishing the list of these zones throughout France. Related data The Hauts-de-Seine Department offers you all of its aerial shots taken at different periods (from 1978 to today). Orhophotographs (HR: high resolution) produced on these occasions are freely available on the platform. With a very close resolution and high precision, these orthophotplans form a coherent set allowing comparisons over time.

This is a Locator for finding British National Grid references. It provides lookups on the British National Grid, which can be applied to all Ordnance Survey maps of Great Britain. You can use it to query by absolute coordinates or by tile. Both types of query return the centre point of the corresponding 10k grid square BNG tile. Enter grid coordinates as absolute XY: 123456, 654321 Enter tile queries as Grid squares: TL44; as sub tile: TQ1234 or; as quadrant SN1234SE

Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication.Rates of confirmed COVID-19 in Ottawa Wards, excluding LTC and RH cases, and number of cases in LTCH and RH in Ottawa Wards. Data are provided for all cases (i.e. cumulative), cases reported within 30 days of the data pull (i.e. last 30 days), and cases reported within 14 days of the data pull (i.e. last 14 days). Based on the most up to date information available at 2pm from the COVID-19 Ottawa Database (The COD) on the day prior to publication. You can see the map on Ottawa Public Health's website.Accuracy: Points of consideration for interpretation of the data:Data extracted by Ottawa Public Health at 2pm from the COVID-19 Ottawa Database (The COD) on May 12th, 2020. The COD is a dynamic disease reporting system that allow for continuous updates of case information. These data are a snapshot in time, reflect the most accurate information that OPH has at the time of reporting, and the numbers may differ from other sources. Cases are assigned to Ward geography based on their postal code and Statistics’ Canada’s enhanced postal code conversion file (PCCF+) released in January 2020. Most postal codes have multiple geographic coordinates linked to them. Thus, when available, postal codes were attributed to a XY coordinates based on the Single Link Identifier provided by Statistics’ Canada’s PCCF+. Otherwise, postal codes that fall within the municipal boundaries but whose SLI doesn’t, were attributed to the first XY coordinates within Ottawa listed in the PCCF+. For this reason, results for rural areas should be interpreted with caution as attribution to XY coordinates is less likely to be based on an SLI and rural postal codes typically encompass a much greater surface area than urban postal codes (e.i. greater variability in geographic attribution, less precision in geographic attribution). Population estimates are based on the 2016 Census. Rates calculated from very low case numbers are unstable and should be interpreted with caution. Low case counts have very wide 95% confidence intervals, which are the lower and upper limit within which the true rate lies 95% of the time. A narrow confidence interval leads to a more precise estimate and a wider confidence interval leads to a less precise estimate. In other words, rates calculated from very low case numbers fluctuate so much that we cannot use them to compare different areas or make predictions over time.Update Frequency: Biweekly Attributes:Ward Number – numberWard Name – textCumulative rate (per 100 000 population), excluding cases linked to outbreaks in LTCH and RH – cumulative number of residents with confirmed COVID-19 in a Ward, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardCumulative number of cases, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward, excluding cases linked to outbreaks in LTCH and RHCumulative number of cases linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19 linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 30 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 30 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHNumber of cases in the last 30 days linked to outbreaks in LTCH and RH - Number of residents with confirmed COVID-19, reported in the 30 days prior to the data pull, linked to an outbreak in a long-term care home or retirement home by WardRate (per 100 000 population) in the last 14 days, excluding cases linked to outbreaks in LTCH and RH –number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding those linked to outbreaks in LTCH and RH, divided by the total population of that WardNumber of cases in the last 14 days, excluding cases linked to outbreaks in LTCH and RH - cumulative number of residents with confirmed COVID-19 in a Ward reported in the 30 days prior to the data pull, excluding cases linked to outbreaks in LTCH and RHContact: OPH Epidemiology Team

Two-dimensional coordinates for lines traced onto images of beaks.

• This is a .zip archive xy coordinates (250 files, .txt); and a list of specimen names (1 file, .csv).
• All images traced in FIJI.
• For each specimen, there is a trace of the beak rostrum and a separate trace of the beak bite surface.
• Each trace file should be a list of xy coordinates that ends at the beak tip. This must be checked/verified/corrected for all files before running any analyses! I recommend visual inspection by plotting each beak dataset as a scatterplot in a color spectrum (rainbow, etc.).
• These were traced over pixel images, so each file has a different number of xy coordinates (depending on the pixel resolution/image size that was traced).
• All bird specimen images were downloaded from Phenome10k.org
• All cephalopod specimens were traced from images published in:
  • Xavier, J. C. & Cherel, Y. 2009 Cephalopod beak guide for the Southern Ocean. British Antarctic Survey.

A detailed vegetation map of a 120m by 28m study site was published by Rudolph (1963). A paper copy of the original maps of this research was obtained from archives at the University of Ohio.The map was digitised into a GIS layer and converted to meters. In the field, the plot was found because some of the marking pegs were still present on site and aerial photographs were used to locate points. GPS was used to determine the real world coordinates of the plot location. The site was remapped using a one metre square grid and change analysis undertaken using a GIS. Rudolph's map classifies the cover of mosses, lichens and algae into four classes: Heavy (40-90%), Patch (10-40%), Scattered (less than 10%) and none (0%). The combination of these classes was used to describe the vegetation in 2004. Within each one metre square the percentage cover of mosses, lichens and algae were recorded. The x,y distance of the cell centre from the north west corner of the plot was also recorded together with a description of the surface rock, wetness and percentage under snow. Vegetation change was able to be compared between 1962 and 2004. The changes in relation to the physical characteristics of the surface of the plot, such as rock type, wetness and slope were analysed. The data was converted to a Dbase file and then imported to a GIS point layer using the xy location as the geographical coordinates. The vegetation was also described using relevee measurements to determine cover of vegetation. The grid was 20 x 10 cm (200 point relevee) and analysed to determine species association. The 2004 map was compared with the 1962 map with statistics generated that describe the change.

Since the time of Darwin, biologists have sought to understand the evolution and origins of phenotypic variation. To understand the genetic and molecular sources of morphological differences, we capitalize on the cichlid fish system. Cichlids of the East African Rift Lakes have undergone an extensive adaptive radiation, including variation in body shape, head shape, and pigmentation. These morphological differences are often intimately linked to the ecology and behavior of these animals. Here, we investigate the genetic basis of these phenotypes using quantitative trait loci (QTL) mapping using four genera of Lake Malawi cichlids and two F2 hybrid populations. The first hybrid cross is between Aulonocara koningsi, which lives in the open sandy region and feeds insects from the open sand, and Metriaclima mbenjii, an omnivore rock-dwelling fish. The second cross is between Labidochromis caeruleus, a suction-feeding insectivore that swims continuously searching for prey, and Labeotropheus trewavasae, which feeds by biting or scraping attached algae from the rocks in its benthic habitat. Such work can provide insights into the molecular basis of phenotypic adaptation, the genetic architecture of morphology, and the evolution of cichlid fishes. Methods For the first hybrid cross, a single Metriaclima female crossed to two Aulonocara males; the inclusion of the second grandsire was inadvertent and resulted from an unexpected fertilization event in these species with external fertilization. This single F1 family was subsequently incrossed to produce a hybrid F2 population of 491 fishes. For the second cross, a single Labidochromis caeruleus female was crossed with a single Labeotropheus trewavasae male to create one F1 family, which was subsequently incrossed to produce a hybrid F2 population of 447 fishes. Hybrid fish and 10 each of the pure parental species were euthanized and 2D imaged at five months, with each image including a color standard and scale. Sex of each animal was determined based on gonad dissection and omitted if there was any ambiguity. Linear measures were collected from these images using the program ImageJ as pixels, converted to cm using measures of a scale in each picture using Excel, and processed to residual after regression to standard length (snout to caudal peduncle) in R. Landmark data was collected from 2D images using TPSdig, converted to XY coordinates using TPSutil, and geometric morphometrics including size correction was conducted using the geomorph package in R. Pigment data was collected as described in the associated manuscript. Genotype data came from DNA extracted from caudal fin tissue, that was sequenced as RADseq libraries, and processed using the R program Stacks. The A allele is designated as coming from the Metriaclima granddam and the B allele from the Aulonocara grandsire. In the second cross, the A allele is designated as coming from the Labidochromis granddam and the B allele from the Labeotropheus grandsire. QTL mapping of residual phenotypes using this genotype data was conducted using MQM in R/qtl. File names with "MmAk" indicates the Metriaclima x Aulonocara cross, while "LcLt" refers to Labidochromis x Labeotropheus.

This map shows the bike paths of all Pace rides in 2019 in the form of XY coordinate points. The NSC (Neighborhood Service Center) Quadrant feature layer lays underneath the point layer as to give a visual division of activity in each of the four quadrants of Rochester.

Snapshot of all Yield and Yield Ahead Signs in West Virginia as extracted by Mutcdname from an overall Sign Dataset. Datasets include RouteID, SignID, County Code, Route Numbered, Sub Route Number, Sign System, supplemental code, Supplemental Description, Direction, Milepoint, Number of Signs, Location, Mutcdname and Mutcode, Mutcdcat, Text, County, Photo URL, and XY Coordinates. Data is current as of 2015 and is updated as needed. Coordinate System: NAD_1983_UTM_Zone_17N

The dataset contains information of juvenile coral demographics from multiple regions (n = 4) situated throughout the Torres Straits (Far North) and the Great Barrier Reef (North, Central, South). Each region has multiple reefs (n = 2-5), with multiple sites (n = 1-6) nested within each reef, and multiple quadrats within each site (n = 3-9). At the beginning of the study, permanent quadrats (50 x 50 cm) were placed around =>1 juvenile coral (<40 mm maximum diameter), standardised to a 5 m depth profile. All juvenile corals within the quadrat had their location mapped, size measured (maximum diameter), identified to the lowest taxon possible (typically genus), and orientation categorised (exposed, vertical, cryptic). An image of the entire quadrat was taken, plus four detailed images of each quadrat quadrant. Processing of the in-situ maps and images included mapping the xy co-ordinates of every mapped individual colony, and calculating the % cover of the benthic substrate. Every ~12 months, the entire process is repeated. Quadrats are revisited, the juvenile corals re-measured, marked as dead, and new colonies added, and associated images taken. Datasets allow for the quantification of juvenile coral demographic rates (growth, survival, and recruitment), changes in % cover within the quadrats, with all data geolocated to the 16 xy coordinates within each quadrat.

Access Metadata is fully public. Data files will be uploaded and made fully public once the research has been published. For special requests, please directly contact the Data Custodian and CC the Project Leader.

Lineage: NA – the dataset is the raw dataset prior to cleaning. It contains all necessary information to conduct cleaning by the user.

List of points d’Eau Fireendie (PEI) of the Hérault department. The completeness of the data is not guaranteed. SDIS34 is responsible for the maintenance of the departmental database for external defence against fire (DECI), the production of information falls within the competence of local and regional authorities. For more information on Hérault’s CITT, you can consult the documents available at the following address: https://deci.sdis34.fr/ressources-deci The description of the fields is based on the recommendations of the Afigéo: https://www.afigeo.asso.fr/wp-content/uploads/2020/12/10/modele-minimal-donnees-pei.pdf The type_pei field may contain the following values: PI = Fire Pole, BI = Mouth Fire, CI = Reserve or Tank, BA = Agricultural Terminal, PR = Relay Pole, PA = Vacuum Point The data shall be provided in the following formats: — GeoPackage (because it’s much better than the Shape!), with the associated symbology that corresponds to that available on the mapping of the hydraclic peis management software — CSV containing XY coordinates, with the associated CSVT file that specifies the type of each field and a QML file to display symbology in QGIS All geographical coordinates are projected Lambert93 (EPSG:2154).

This data set shows the Tag number, Quadrat location, Species code, diameter and XY coordinates of stems >=10 cm D130 present at the time of Hurricane Hugo and in the first census. The data set is composed of two files both with the same file structure. In LFDP_C1treemap.txt the diameters (Fdiam) are as recorded in the field data. In LFDP_C1TREEMAPa.txt the stem diameters (Fdiam) were calculated to allocate "missed" stems (stems >=10 cm D130) that were found in survey 2, 3 or Census 2 to Census 1 survey 1. We calculated the diameter the stem would have had, if it had been recorded at the same time the quadrat it was located in was assessed, in the appropriate survey for that stem size. To extrapolate the stem size back in time, we used the actual growth rate of that individual stem if more than one measurement was available. If only one diameter measurement was available we used the median growth rate for that species in the appropriate size class stems >=10, <30 cm D130). In our publications we will combine data sets LFDP_C1treemap.txt and LFDP_C1TREEMAPa.txt to make Census 1 and to reconstruct the forest for stems >= 10 cm D130 at the time of Hurricane Hugo. We have divided the data into two separate files to ensure that when stem diameters are compared to future censuses the diameter data in LFDP_C1TREEMAPa.txt are not used to calculate growth rates. The last corrections to the Census 1 data were made in May 2001. The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and "cleaned". The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Submit to the LFDP PIs a short (1 page) description of how you intend to use the data; · Invite LFDP PIs to be co-authors on any publication that uses the data in a substantial way (some PIs may decline and other LFDP scientists may need to be included); If the LFDP PIs are not co-authors, send the PIs a draft of any paper using LFDP data, so that the PIs may comment upon it; In the methods section of any publication using LFDP data, describe that data as coming from the "Luquillo Forest Dynamics Plot, part of the Luquillo Experimental Forest Long-Term Ecological Research Program"; Acknowledge in any publication using LFDP data the "The Luquillo Experimental Forest Long-Term Ecological Research Program, supported by the U.S. National Science Foundation, the University of Puerto Rico, and the International Institute of Tropical Forestry"; · Supply the LFDP PIs with 10 reprints of any publication using LFDP data. · Accept that the LFDP PIs can not guarantee that the LFDP data you intend to use, has not already been submitted for publication or published. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.

If you use this dataset, please cite the IJRR data paper (bibtex is below). We present a dataset collected from a canoe along the Sangamon River in Illinois. The canoe was equipped with a stereo camera, an IMU, and a GPS device, which provide visual data suitable for stereo or monocular applications, inertial measurements, and position data for ground truth. We recorded a canoe trip up and down the river for 44 minutes covering 2.7 km round trip. The dataset adds to those previously recorded in unstructured environments and is unique in that it is recorded on a river, which provides its own set of challenges and constraints that are described in this paper. The data is divided into subsets, which can be downloaded individually. Video previews are available on Youtube: https://www.youtube.com/channel/UCOU9e7xxqmL_s4QX6jsGZSw The information below can also be found in the README files provided in the 527 dataset and each of its subsets. The purpose of this document is to assist researchers in using this dataset. Images ====== Raw --- The raw images are stored in the cam0 and cam1 directories in bmp format. They are bayered images that need to be debayered and undistorted before they are used. The camera parameters for these images can be found in camchain-imucam.yaml. Note that the camera intrinsics describe a 1600x1200 resolution image, so the focal length and center pixel coordinates must be scaled by 0.5 before they are used. The distortion coefficients remain the same even for the scaled images. The camera to imu tranformation matrix is also in this file. cam0/ refers to the left camera, and cam1/ refers to the right camera. Rectified --------- Stereo rectified, undistorted, row-aligned, debayered images are stored in the rectified/ directory in the same way as the raw images except that they are in png format. The params.yaml file contains the projection and rotation matrices necessary to use these images. The resolution of these parameters do not need to be scaled as is necessary for the raw images. params.yml ---------- The stereo rectification parameters. R0,R1,P0,P1, and Q correspond to the outputs of the OpenCV stereoRectify function except that 1s and 2s are replaced by 0s and 1s, respectively. R0: The rectifying rotation matrix of the left camera. R1: The rectifying rotation matrix of the right camera. P0: The projection matrix of the left camera. P1: The projection matrix of the right camera. Q: Disparity to depth mapping matrix T_cam_imu: Transformation matrix for a point in the IMU frame to the left camera frame. camchain-imucam.yaml -------------------- The camera intrinsic and extrinsic parameters and the camera to IMU transformation usable with the raw images. T_cam_imu: Transformation matrix for a point in the IMU frame to the camera frame. distortion_coeffs: lens distortion coefficients using the radial tangential model. intrinsics: focal length x, focal length y, principal point x, principal point y resolution: resolution of calibration. Scale the intrinsics for use with the raw 800x600 images. The distortion coefficients do not change when the image is scaled. T_cn_cnm1: Transformation matrix from the right camera to the left camera. Sensors ------- Here, each message in name.csv is described ###rawimus### time # GPS time in seconds message name # rawimus acceleration_z # m/s^2 IMU uses right-forward-up coordinates -acceleration_y # m/s^2 acceleration_x # m/s^2 angular_rate_z # rad/s IMU uses right-forward-up coordinates -angular_rate_y # rad/s angular_rate_x # rad/s ###IMG### time # GPS time in seconds message name # IMG left image filename right image filename ###inspvas### time # GPS time in seconds message name # inspvas latitude longitude altitude # ellipsoidal height WGS84 in meters north velocity # m/s east velocity # m/s up velocity # m/s roll # right hand rotation about y axis in degrees pitch # right hand rotation about x axis in degrees azimuth # left hand rotation about z axis in degrees clockwise from north ###inscovs### time # GPS time in seconds message name # inscovs position covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz m^2 attitude covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz deg^2 velocity covariance # 9 values xx,xy,xz,yx,yy,yz,zx,zy,zz (m/s)^2 ###bestutm### time # GPS time in seconds message name # bestutm utm zone # numerical zone utm character # alphabetical zone northing # m easting # m height # m above mean sea level Camera logs ----------- The files name.cam0 and name.cam1 are text files that correspond to cameras 0 and 1, respectively. The columns are defined by: unused: The first column is all 1s and can be ignored. software frame number: This number increments at the end of every iteration of the software loop. camera frame number: This number is generated by the camera and increments each time the shutter is triggered. The software and camera frame numbers do not have to start at the same value, but if the difference between the initial and final values is not the same, it suggests that frames may have been dropped. camera timestamp: This is the cameras internal timestamp of the frame capture in units of 100 milliseconds. PC timestamp: This is the PC time of arrival of the image. name.kml -------- The kml file is a mapping file that can be read by software such as Google Earth. It contains the recorded GPS trajectory. name.unicsv ----------- This is a csv file of the GPS trajectory in UTM coordinates that can be read by gpsbabel, software for manipulating GPS paths. @article{doi:10.1177/0278364917751842, author = {Martin Miller and Soon-Jo Chung and Seth Hutchinson}, title ={The Visual–Inertial Canoe Dataset}, journal = {The International Journal of Robotics Research}, volume = {37}, number = {1}, pages = {13-20}, year = {2018}, doi = {10.1177/0278364917751842}, URL = {https://doi.org/10.1177/0278364917751842}, eprint = {https://doi.org/10.1177/0278364917751842} }