Satellite tagging was implemented in 2013. Satellite tagging is conducted using a Dan Inject air rifle and deployment arrows designed by Wildlife Computers. Two types of tags are deployed. One type is a Wildlife Computers SPOT5-240C tag that collects location and temperature. The other tag type is the Wildlife Computers SPLASH10-292B, which provides location as well as depth, temperature, and light level.

The data labeling market is experiencing robust growth, projected to reach $3.84 billion in 2025 and maintain a Compound Annual Growth Rate (CAGR) of 28.13% from 2025 to 2033. This expansion is fueled by the increasing demand for high-quality training data across various sectors, including healthcare, automotive, and finance, which heavily rely on machine learning and artificial intelligence (AI). The surge in AI adoption, particularly in areas like autonomous vehicles, medical image analysis, and fraud detection, necessitates vast quantities of accurately labeled data. The market is segmented by sourcing type (in-house vs. outsourced), data type (text, image, audio), labeling method (manual, automatic, semi-supervised), and end-user industry. Outsourcing is expected to dominate the sourcing segment due to cost-effectiveness and access to specialized expertise. Similarly, image data labeling is likely to hold a significant share, given the visual nature of many AI applications. The shift towards automation and semi-supervised techniques aims to improve efficiency and reduce labeling costs, though manual labeling will remain crucial for tasks requiring high accuracy and nuanced understanding. Geographical distribution shows strong potential across North America and Europe, with Asia-Pacific emerging as a key growth region driven by increasing technological advancements and digital transformation. Competition in the data labeling market is intense, with a mix of established players like Amazon Mechanical Turk and Appen, alongside emerging specialized companies. The market's future trajectory will likely be shaped by advancements in automation technologies, the development of more efficient labeling techniques, and the increasing need for specialized data labeling services catering to niche applications. Companies are focusing on improving the accuracy and speed of data labeling through innovations in AI-powered tools and techniques. Furthermore, the rise of synthetic data generation offers a promising avenue for supplementing real-world data, potentially addressing data scarcity challenges and reducing labeling costs in certain applications. This will, however, require careful attention to ensure that the synthetic data generated is representative of real-world data to maintain model accuracy. This comprehensive report provides an in-depth analysis of the global data labeling market, offering invaluable insights for businesses, investors, and researchers. The study period covers 2019-2033, with 2025 as the base and estimated year, and a forecast period of 2025-2033. We delve into market size, segmentation, growth drivers, challenges, and emerging trends, examining the impact of technological advancements and regulatory changes on this rapidly evolving sector. The market is projected to reach multi-billion dollar valuations by 2033, fueled by the increasing demand for high-quality data to train sophisticated machine learning models. Recent developments include: September 2024: The National Geospatial-Intelligence Agency (NGA) is poised to invest heavily in artificial intelligence, earmarking up to USD 700 million for data labeling services over the next five years. This initiative aims to enhance NGA's machine-learning capabilities, particularly in analyzing satellite imagery and other geospatial data. The agency has opted for a multi-vendor indefinite-delivery/indefinite-quantity (IDIQ) contract, emphasizing the importance of annotating raw data be it images or videos—to render it understandable for machine learning models. For instance, when dealing with satellite imagery, the focus could be on labeling distinct entities such as buildings, roads, or patches of vegetation.October 2023: Refuel.ai unveiled a new platform, Refuel Cloud, and a specialized large language model (LLM) for data labeling. Refuel Cloud harnesses advanced LLMs, including its proprietary model, to automate data cleaning, labeling, and enrichment at scale, catering to diverse industry use cases. Recognizing that clean data underpins modern AI and data-centric software, Refuel Cloud addresses the historical challenge of human labor bottlenecks in data production. With Refuel Cloud, enterprises can swiftly generate the expansive, precise datasets they require in mere minutes, a task that traditionally spanned weeks.. Key drivers for this market are: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Potential restraints include: Rising Penetration of Connected Cars and Advances in Autonomous Driving Technology, Advances in Big Data Analytics based on AI and ML. Notable trends are: Healthcare is Expected to Witness Remarkable Growth.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet North America contains data across North America, which accounts for ~13% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.

There are a total of 1561 image chips of 256 x 256 pixels in LandCoverNet North America V1.0 spanning 40 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

This metadata document describes the data contained in the "processedData" folder of this data package. This data package contains all data collected from 130 Argos and Argos-linked GPS satellite transmitters attached to Buff-breasted Sandpipers at breeding sites in Alaska, migratory stopovers in Texas, and wintering areas in Brazil, Argentina, and Uruguay, 2016-2018. The 130 deployed tags were comprised of 22 Argos Doppler PTTs (2 gram PTTs built by Microwave Telemetry) and 108 Argos-linked GPS PTTs (2 gram PinPoint tags built by Lotek). However, 14 Pinpoints never reported data, so they only appear in the deployment data records and not the location data files. One of the 94 functional PinPoint tags never reported GPS data, and 15 of the Pinpoint tags never obtained an Argos Doppler-derived location estimate. The raw (original) data from these tags were processed to accomplish three goals: 1) extract encoded GPS locations; 2) flag implausible locations; and 3) decode raw sensor ...

Satellite tagging was implemented in 2013. Satellite tagging is conducted using a Dan Inject air rifle and deployment arrows designed by Wildlife Computers. Two types of tags are deployed. One type is a Wildlife Computers SPOT5-240C tag that collects location and temperature. The other tag type is the Wildlife Computers SPLASH10-292B, which provides location as well as depth, temperature, and light level.

Coast Train is a library of images of coastal environments, annotations, and corresponding thematic label masks (or ‘label images’) collated for the purposes of training and evaluating machine learning (ML), deep learning, and other models for image segmentation. It includes image sets from both geospatial satellite, aerial, and UAV imagery and orthomosaics, as well as non-geospatial oblique and nadir imagery. Images include a diverse range of coastal environments from the U.S. Pacific, Gulf of Mexico, Atlantic, and Great Lakes coastlines, consisting of time-series of high-resolution (≤1m) orthomosaics and satellite image tiles (10–30m). Each image, image annotation, and labelled image is available as a single NPZ zipped file. NPZ files follow the following naming convention: {datasource}_{numberofclasses}_{threedigitdatasetversion}.zip, where {datasource} is the source of the original images (for example, NAIP, Landsat 8, Sentinel 2), {numberofclasses} is the number of classes us ...

MLRSNet provides different perspectives of the world captured from satellites. That is, it is composed of high spatial resolution optical satellite images. MLRSNet contains 109,161 remote sensing images that are annotated into 46 categories, and the number of sample images in a category varies from 1,500 to 3,000. The images have a fixed size of 256×256 pixels with various pixel resolutions (~10m to 0.1m). Moreover, each image in the dataset is tagged with several of 60 predefined class labels, and the number of labels associated with each image varies from 1 to 13. The dataset can be used for multi-label based image classification, multi-label based image retrieval, and image segmentation.

The Dataset includes: 1. Images folder: 46 categories, 109,161 high-spatial resolution remote sensing images. 2. Labels folders: each category has a .csv file. 3. Categories_names. xlsx: Sheet1 lists the names of 46 categories, and the Sheet2 shows the associated multi-label to each category.

LandCoverNet is a global annual land cover classification training dataset with labels for the multi-spectral satellite imagery from Sentinel-1, Sentinel-2 and Landsat-8 missions in 2018. LandCoverNet Asia contains data across Asia, which accounts for ~31% of the global dataset. Each pixel is identified as one of the seven land cover classes based on its annual time series. These classes are water, natural bare ground, artificial bare ground, woody vegetation, cultivated vegetation, (semi) natural vegetation, and permanent snow/ice.
There are a total of 2753 image chips of 256 x 256 pixels in LandCoverNet South America V1.0 spanning 92 tiles. Each image chip contains temporal observations from the following satellite products with an annual class label, all stored in raster format (GeoTIFF files):
* Sentinel-1 ground range distance (GRD) with radiometric calibration and orthorectification at 10m spatial resolution
* Sentinel-2 surface reflectance product (L2A) at 10m spatial resolution
* Landsat-8 surface reflectance product from Collection 2 Level-2

Radiant Earth Foundation designed and generated this dataset with a grant from Schmidt Futures with additional support from NASA ACCESS, Microsoft AI for Earth and in kind technology support from Sinergise.

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, other)

Description

4088 images and 4088 associated labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

1018 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7335647

Labels have been reclassified from 4 classes to 2 classes.

Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.

These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******

A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

classes.txt, a file containing the class names

images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents

labels.zip, a zipped folder containing the 1-band label images

overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, bllue=0=other)

resized_images.zip, RGB images resized to 512x512x3 pixels

resized_labels.zip, label images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel, Goldstein, Evan, Bernier, Julie, Bosse, Stephen, Colacicco, Rosa, Corak, Nick, Fitzpatrick, Sharon, del Jesús González Guillén, Anais, Ku, Venus, Paprocki, Julie, Platt, Lindsay, Steele, Bethel, Wright, Kyle, & Yasin, Brandon. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7335647

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

Between 2004 and 2006 we conducted four harbor seal tagging trips in Cook Inlet during the months of October and May. In total, we captured and released 93 harbor seals, 77 of which were tagged with satellite transmitters. Each transmitter was glued to the hair on the back of the seal using durable epoxy. Fourteen of the seals were also equipped with specially developed transmitters that were attached to one of the rear flippers. Transmissions from the 91 tags resulted in 178,536 location estimates and 310,593 dive and haul-out behavior records. These data formed the basis for the development of novel analysis techniques. Johnson et al. (2008) described a novel continuous-time correlated random walk (CTCRW) method for predicting animal locations from satellite tags. Higgs and Ver Hoef (2011) described a new statistical method for analyzing dive behavior based on dive histogram recordings obtained from satellite tags.

The blue shark (Prionace glauca), is a species found in Atlantic Canadian waters which is commonly encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) and Smart Position and Temperature tag (SPOT) from Wildlife Computers were applied to blue sharks from 2004 to 2008 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial and recreational vessels from mid-August to early October, but mostly in September. A variety of tag models were deployed: PAT 4 (n=16), Mk10 (N=28), and SPOT3 (N=2) and 39 of 46 tags reported. The blue sharks tagged ranged in size from 124 cm to 251 cm Fork Length (curved); 30 were female, 15 were male and 1 was unknown sex. Time at liberty ranged from 4 – 210 days and 16 tags remained on for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.

This dataset contains mahi-mahi (Coryphaena hippurus) satellite tagging data collected during R/V F. G. Walton Smith cruise WS19159 in the Atlantic and Gulf of Mexico from 2019-04-26 to 2019-08-26 as part of a mission to satellite tag adult control and oil exposed mahi-mahi and characterize the habitat where they were captured. Mahi-mahi were tagged with Wildlife Computers Inc. pop-up satellite archival tags (PSATs) programmed to collect light, depths, temperature, and acceleration values and then transmitted via the Argos satellite network. All the raw PSATs data for each tagged mahi-mahi are included in the dataset. PSATs data include temperatures, depths, light levels, and acceleration, as well as fish tracks. The R/V F.G. Walton Smith cruise WS19159 was led by chief scientist Lela Schlenker with the objective of tagging mahi-mahi with pop-up satellite archival tags.

Pop Up Satellite Archival Tag Market Outlook

The global market size for Pop Up Satellite Archival Tags (PSATs) was valued at approximately USD 25 million in 2023 and is expected to reach nearly USD 45 million by 2032, growing at a CAGR of 6.5% during the forecast period. One of the key growth factors contributing to this market is the increasing demand for advanced tagging technologies to monitor and manage marine life, fisheries, and oceanographic research. This growth is fueled by the need for more precise and comprehensive data collection methods to address the pressing issues of marine conservation and resource management.

The increasing focus on marine biodiversity conservation has significantly driven the demand for PSATs. Governments and environmental organizations are prioritizing the preservation of marine ecosystems, leading to substantial investments in technologies that can provide detailed insights into marine life behavior and migration patterns. PSATs have proven to be instrumental in this regard, offering researchers the ability to track the movements and environmental conditions of marine species with high accuracy. This trend is expected to continue as the global emphasis on environmental sustainability intensifies.

Another major growth factor for the PSAT market is the rising need for sustainable fisheries management. Overfishing and illegal fishing practices have severely impacted fish populations worldwide, necessitating the adoption of advanced monitoring tools to ensure sustainable fishing practices. PSATs provide critical data on fish movement, habitat use, and survival rates, enabling fisheries managers to implement more effective regulations and quotas. As the global demand for seafood continues to rise, the reliance on PSATs for sustainable fisheries management is likely to grow substantially.

Technological advancements in satellite communication and miniaturization have also played a pivotal role in the expansion of the PSAT market. Improvements in battery life, data transmission capabilities, and the size of the tags have made them more efficient and accessible for a broader range of applications. Furthermore, the integration of machine learning and artificial intelligence with PSAT data analytics is enhancing the precision and utility of the information gathered, thereby attracting more users across various sectors. These technological innovations are expected to drive the market growth significantly over the forecast period.

Regionally, North America holds a substantial share of the PSAT market, driven by robust research activities and government initiatives focused on marine conservation. The presence of leading research institutions and a strong regulatory framework supporting environmental protection further bolster the market in this region. Europe follows closely, with significant investments in oceanographic research and sustainable fisheries management. The Asia Pacific region is anticipated to exhibit the highest growth rate, fueled by increasing awareness of marine conservation issues and the expansion of commercial fisheries. The adoption of PSAT technology in Latin America and the Middle East & Africa is also on the rise, although at a slower pace compared to other regions.

Product Type Analysis

The PSAT market can be segmented by product type, including MiniPAT, sPAT, mrPAT, and others. Each of these product types serves different purposes and offers unique benefits, contributing to their demand across various applications. MiniPATs, for instance, are widely used due to their compact size and extended battery life, making them suitable for long-term studies of smaller marine species. These tags are particularly favored by researchers looking to gather detailed data without interfering significantly with the animal's natural behavior.

sPATs, or standard Pop-up Archival Tags, are another popular segment, known for their robustness and durability. These tags are typically used for larger marine animals such as sharks and tuna, where the size of the tag is less of an issue. sPATs offer a balance between data collection capabilities and physical resilience, making them a versatile choice for a wide range of marine research projects. Their ability to withstand harsh marine conditions and provide reliable data over extended periods makes them a valuable tool for both scientific research and fisheries management.

mrPATs, or Mini Research Pop-up Archival Tags, represent a niche but growing segment within the PSAT market. These tags are designed for specialized research application

This dataset provides the hand-labelled crop / non-crop points used for training, which were created by labelling high-resolution satellite imagery in QGIS and Google Earth Pro. Data is available for Ethiopia, Sudan, Togo and Kenya.

Code used to process these points is available in the following github repository: https://github.com/nasaharvest/crop-maml

For more information, or if you use any part of this dataset, please refer to / cite the following paper: Gabriel Tseng, Hannah Kerner, Catherine Nakalembe and Inbal Becker-Reshef. 2021. Learning to predict crop type from heterogeneous sparse labels using meta-learning. GeoVision Workshop at CVPR ’21: June 19th, 2021

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, other)

Images and 2-class labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts (water, other)

Description

3649 images and 3649 associated labels for semantic segmentation of Sentinel-2 and Landsat 5-band (R+G+B+NIR+SWIR) satellite images of coasts. The 2 classes are 1=water, 0=other. Imagery are a mixture of 10-m Sentinel-2 and 15-m pansharpened Landsat 7, 8, and 9 visible-band imagery of various sizes. Red, Green, Blue, near-infrared, and short-wave infrared bands only

These images and labels could be used within numerous Machine Learning frameworks for image segmentation, but have specifically been made for use with the Doodleverse software package, Segmentation Gym**.

Two data sources have been combined

Dataset 1

* 579 image-label pairs from the following data release**** https://doi.org/10.5281/zenodo.7344571
* Labels have been reclassified from 4 classes to 2 classes.
* Some (422) of these images and labels were originally included in the Coast Train*** data release, and have been modified from their original by reclassifying from the original classes to the present 2 classes.
* These images and labels have been made using the Doodleverse software package, Doodler*.

Dataset 2

• 3070 image-label pairs from the Sentinel-2 Water Edges Dataset (SWED)***** dataset, https://openmldata.ukho.gov.uk/, described by Seale et al. (2022)******
• A subset of the original SWED imagery (256 x 256 x 12) and labels (256 x 256 x 1) have been chosen, based on the criteria of more than 2.5% of the pixels represent water

File descriptions

• classes.txt, a file containing the class names
• images.zip, a zipped folder containing the 3-band RGB images of varying sizes and extents
• labels.zip, a zipped folder containing the 1-band label images
• nir.zip, a zipped folder containing the 1-band near-infrared (NIR) images
• swir.zip, a zipped folder containing the 1-band shorttwave infrared (SWIR) images
• overlays.zip, a zipped folder containing a semi-transparent overlay of the color-coded label on the image (red=1=water, blue=0=other)
• resized_images.zip, RGB images resized to 512x512x3 pixels
• resized_labels.zip, label images resized to 512x512x1 pixels
• resized_nir.zip, NIR images resized to 512x512x1 pixels
• resized_swir.zip, SWIR images resized to 512x512x1 pixels

References

*Doodler: Buscombe, D., Goldstein, E.B., Sherwood, C.R., Bodine, C., Brown, J.A., Favela, J., Fitzpatrick, S., Kranenburg, C.J., Over, J.R., Ritchie, A.C. and Warrick, J.A., 2021. Human‐in‐the‐Loop Segmentation of Earth Surface Imagery. Earth and Space Science, p.e2021EA002085https://doi.org/10.1029/2021EA002085. See https://github.com/Doodleverse/dash_doodler.

**Segmentation Gym: Buscombe, D., & Goldstein, E. B. (2022). A reproducible and reusable pipeline for segmentation of geoscientific imagery. Earth and Space Science, 9, e2022EA002332. https://doi.org/10.1029/2022EA002332 See: https://github.com/Doodleverse/segmentation_gym

***Coast Train data release: Wernette, P.A., Buscombe, D.D., Favela, J., Fitzpatrick, S., and Goldstein E., 2022, Coast Train--Labeled imagery for training and evaluation of data-driven models for image segmentation: U.S. Geological Survey data release, https://doi.org/10.5066/P91NP87I. See https://coasttrain.github.io/CoastTrain/ for more information

****Buscombe, Daniel. (2022). Images and 4-class labels for semantic segmentation of Sentinel-2 and Landsat RGB, NIR, and SWIR satellite images of coasts (water, whitewater, sediment, other) (v1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.7344571

*****Seale, C., Redfern, T., Chatfield, P. 2022. Sentinel-2 Water Edges Dataset (SWED) https://openmldata.ukho.gov.uk/

******Seale, C., Redfern, T., Chatfield, P., Luo, C. and Dempsey, K., 2022. Coastline detection in satellite imagery: A deep learning approach on new benchmark data. Remote Sensing of Environment, 278, p.113044.

Satellite tags were deployed on 48 east Australian humpback whales (breeding stock E1) in 2008, 2009 and 2010 on their southward migration, northward migration and feeding grounds in order to identify and describe migratory pathways, feeding grounds and possible calving areas. At the time, these movements were not well understood and calving grounds not clearly identified. To the best of our knowledge, this dataset details all long-term tag deployments that have occurred to date on breeding stock E1.

Satellite tags were deployed on whales in the following locations:
• Eden, southern NSW (Australia), October 2008: whales were tagged off Eden during their southern migration.
• Evans Head, northern NSW (Australia), June and July 2009: whales were tagged off Evans Head during their northern migration.
• East Antarctica, February 2010: whales were tagged on their feeding grounds within IWC Management Area V.
• Sunshine Coast, QLD (Australia), October 2010: whales were tagged off the Sunshine Coast during their southern migration.

The various files in the download are:
Argos locations generated by tagging of East Australian (breeding stock E1) humpback whale

This file contains all Argos locations generated by satellite tags deployed on humpback whales. Deployment details can be found separately (dataset title: 'Summary of satellite tag deployments on breeding stock E1 humpback whales'). Locations were calculated by Argos using a least-squares analysis. Columns are: Argos PTT: The unique satellite tag identification number. GMT: The date and time (dd/mm/yyyy hh:mm) of each Argos location in UTC. Argos location class: The location class retrieved from Argos, Argos diagnostic data. Classes are based on the type of location (Argos Doppler Shift) and the number of messages received during the satellite pass. Location classes in order of decreasing accuracy are 3, 2, 1, 0, A, B and Z (definition from Argos User's Manual V1.6.6, 2016). Longitude: The longitude of the Argos location estimate. Units: decimal degrees, WGS84 reference system. Latitude: The latitude of the Argos location estimate. Units: decimal degrees, WGS84 reference system.

Speed-distance-angle filter applied to Argos locations generated by tagging of East Australian (breeding stock E1) humpback whale.

This file contains all Argos locations generated by satellite tags deployed on humpback whales. Deployment details can be found separately (dataset title: 'Summary of satellite tag deployments on breeding stock E1 humpback whales'). Locations were calculated by Argos using a least-squares analysis. Additionally, this file contains a column detailing the outcome of the application of the sdafilter - an algorithm based on swimming speed, distance between successive locations, and turning angles to remove unlikely position estimates (speed of 10 ms , spike angles of 15° and 25°, spike lengths of 2500m and 5000m; Freitas et al. 2008). Freitas C, Lydersen C, Fedak M, Kovacs K (2008) A simple new algorithm for filtering marine mammal Argos locations. Marine Mammal Science 24 (2): 315‑325. Columns are: Argos PTT: The unique satellite tag identification number. GMT: The date and time (dd/mm/yyyy hh:mm) of each Argos location in UTC. Argos location class: The location class retrieved from Argos, Argos diagnostic data. Classes are based on the type of location (Argos Doppler Shift) and the number of messages received during the satellite pass. Location classes in order of decreasing accuracy are 3, 2, 1, 0, A, B and Z (definition from Argos User's Manual V1.6.6, 2016). Longitude: The longitude of the Argos location estimate. Units: decimal degrees, WGS84 reference system. Latitude: The latitude of the Argos location estimate. Units: decimal degrees, WGS84 reference system. Argosfilter outcome: The result of the Argos sdafilter - "removed" (location removed by the filter), "not" (location not removed) and "end_location" (location at the end of the track where the algorithm could not be applied).

State-space model location estimates of satellite tagged East Australian (breeding stock E1) humpback whales.

Using the raw Argos tracking data set (Dataset name: Argos locations generated by tagging of East Australian (breeding stock E1) humpback whale), we accounted for the spatial error associated with Argos locations by fitting a correlated random walk state-space model to generate a location estimate at each observed location time. Within this state-space model, we applied the sdafilter to remove unlikely position estimates (speed of 10 ms, spike angles of 15° and 25°, spike lengths of 2500m and 5000m). See: Jonsen ID, Grecian WJ, Phillips L, Carroll G, McMahon C, Harcourt RG, Hindell MA, Patterson TA (2023) aniMotum, an R package for animal movement data: Rapid quality control, behavioural estimation and simulation. Methods in Ecology and Evolution 14(3): 806‑816. This dataset contains the state-space modelled location estimates of tagged east Australian humpback whales. Associated tag deployment details can be found separately (dataset title: 'Summary of satellite tag deployments on breeding stock E1 humpback whales'). The columns are: Argos PTT: The unique satellite tag identification number. GMT: The date and time (dd/mm/yyyy hh:mm) of each state-space model location estimate in UTC. Longitude: The longitude of the state-space model location estimate. Units: decimal degrees, WGS84 reference system. Latitude: The latitude of the state-space model location estimate. Units: decimal degrees, WGS84 reference system.

Summary of satellite tag deployments on breeding stock E1 humpback whales

A summary of satellite tag deployments on breeding stock E1 humpback whales. Argos PTT = the unique tag identification number; Deploy year = year of deployment; Deploy date = date of deployment; End date = date of final transmitted location; Tracking duration = duration of tag deployment from tag deployment date to last location date; Deploy location = broad geographic location where satellite tag was deployed; Deploy latitude = tag deployment latitude; Deploy longitude = tag deployment longitude; Stage of annual cycle upon deployment = migration direction or feeding grounds; Sex = determined genetically where a biopsy sample was collection; Maturity = an estimate of maturity relative to body size and behaviour; Initial activity = whale behaviour at tagging; Number of locations = the number of Argos locations transmitted; Tag programming = duty cycle applied to tag on and off time as a strategy to extend battery life; Retained for SSM = whether the state space model was applied to the Argos locations generated to account for Argos location error; SSM derived track distance estimate = the length of the satellite track from the state space modelled location estimates in kilometres; Movement captured = the types of movement and behaviour detailed in each satellite track.

Depolyment of popup satellite tag deployment from recreational vessels off central America

Global Positioning System (GPS) tags are nowadays widely used in wildlife tracking. This geolocation technique can suffer from fix loss biases due to poor satellite GPS geometry, that result in tracking data gaps leading to wrong research conclusions. In addition, new solar-powered GPS tags deployed on birds can suffer from a new “battery drain bias” currently ignored in movement ecology analyses. We use a GPS tracking dataset of bearded vultures (Gypaetus barbatus), tracked for several years with solar GPS tags, to evaluate the causes and triggers of fix and data retrieval loss biases. We compare two models of solar GPS tags using different data retrieval systems (Argos vs GSM-GPRS), and programmed with different duty cycles. Neither of the models was able to accomplish the duty cycle programed initially. Fix and data retrieval loss rates were always greater than expected, and showed non-random gaps in GPS locations. Number of fixes per month of tracking was a bad criterion to identify tags with smaller biases. Fix-loss rates were four times higher due to battery drain than due to poor GPS satellite geometry. Both tag models were biased due to the uneven solar energy available for the recharge of the tag throughout the annual cycle, resulting in greater fix-loss rates in winter compared to summer. In addition, we suggest that the bias found along the diurnal cycle is linked to a complex three-factor interaction of bird flight behavior, topography and fix interval. More fixes were lost when vultures were perching compared to flying, in rugged versus flat topography. But long fix-intervals caused greater loss of fixes in dynamic (flying) versus static situations (perching). To conclude, we emphasize the importance of evaluating fix-loss bias in current tracking projects, and deploying GPS tags that allow remote duty cycle updates so that the most appropriate fix and data retrieval intervals can be selected.

The mako shark (Isurus oxyrinchus), is a species found in Atlantic Canadian waters which is encountered in commercial and recreational fisheries. Pop-up Satellite Archival Tags (PSAT) from Wildlife Computers were applied to mako sharks from 2011 to 2013 to collect data on depth (pressure), temperature and ambient light level (for position estimation). Deployments were conducted in Canada on commercial vessels, typically in summer and fall from July to October. Two types of tag models were deployed: Mk10 (N=28), and MiniPAT (N=9) and 28 of 37 tags reported (one female shark was recaptured). The mako sharks tagged ranged in size from 80 cm to 229 cm Fork Length (curved); 13 were female, 17 were male, and 7 were unknown sex. Time at liberty ranged from 0 – 185 days and 6 tags remained on for the programmed duration. Raw data transmitted from the PSAT’s after release was processed through Wildlife Computers software (GPE3) to get summary files, assuming a maximum swimming speed of 2m/s, NOAA OI SST V2 High Resolution data set for SST reference and ETOPO1-Bedrock dataset for bathymetry reference. The maximum likelihood position estimates are available in .csv and .kmz format and depth and temperature profiles are also in .csv format. Other tag outputs as well as metadata from the deployments can be obtained upon request from: warren.joyce@dfo-mpo.gc.ca or heather.bowlby@dfo-mpo.gc.ca.