Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna
Open accessAbstract
Camera traps can be used to address large-scale questions in community ecology by providing systematic data on an array of wide-ranging species. We deployed 225 camera traps across 1,125 km 2 in Serengeti National Park, Tanzania, to evaluate spatial and temporal inter-species dynamics. The cameras have operated continuously since 2010 and had accumulated 99,241 camera-trap days and produced 1.2 million sets of pictures by 2013. Members of the general public classified the images via the citizen-science website www.snapshotserengeti.org. Multiple users viewed each image and recorded the species, number of individuals, associated behaviours, and presence of young. Over 28,000 registered users contributed 10.8 million classifications. We applied a simple algorithm to aggregate these individual classifications into a final ‘consensus’ dataset, yielding a final classification for each image and a measure of agreement among individual answers. The consensus classifications and raw imagery provide an unparalleled opportunity to investigate multi-species dynamics in an intact ecosystem and a valuable resource for machine-learning and computer-vision research.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
By integrating image features over long time spans up to a month, Context R-CNN increases mean species identification precision in the Snapshot Serengeti dataset by 17.9%.
“By integrating image features over long time spans (up to a month), the model is able to increase mean species identification precision in the Snapshot Serengeti dataset137 by 17.9%.”
From Perspectives in machine learning for wildlife conservation (2022) · cites this paper as [137]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
ESTIMATING SITE OCCUPANCY RATES WHEN DETECTION PROBABILITIES ARE LESS THAN ONE
MacKenzie, D. I., Nichols, J. D., Lachman, G. B., Droege, S., Andrew Royle, J., & Langtimm, C. A. (2002) · Ecology · doi:10.1890/0012-9658(2002)083[2248:esorwd]2.0.co;2 · Cited by 4,481 · see citing works
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210 · see citing works
Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy
Bonney, R. E., Cooper, C. B., Dickinson, J. L., Kelling, S., Phillips, T., Rosenberg, K. V., & Shirk, J. (2009) · BioScience · doi:10.1525/bio.2009.59.11.9 · Cited by 2,542 · see citing works
Citizen Science as an Ecological Research Tool: Challenges and Benefits
Dickinson, J. L., Zuckerberg, B., & Bonter, D. N. (2010) · Annual Review of Ecology Evolution and Systematics · doi:10.1146/annurev-ecolsys-102209-144636 · Cited by 1,919 · see citing works
Occupancy Estimation and Modeling: Inferring Patterns and Dynamics of Species Occurrence
MacKenzie, D. I. (2005) · Cited by 1,892 · see citing works
ESTIMATING SITE OCCUPANCY, COLONIZATION, AND LOCAL EXTINCTION WHEN A SPECIES IS DETECTED IMPERFECTLY
MacKenzie, D. I., Nichols, J. D., Hines, J. E., Knutson, M. G., & Franklin, A. B. (2003) · Ecology · doi:10.1890/02-3090 · Cited by 1,749 · see citing works
N ‐Mixture Models for Estimating Population Size from Spatially Replicated Counts
Royle, J. A. (2004) · Biometrics · doi:10.1111/j.0006-341x.2004.00142.x · Cited by 1,550 · see citing works
Designing occupancy studies: general advice and allocating survey effort
MacKenzie, D. I., & Royle, J. A. (2005) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2005.01098.x · Cited by 1,439 · see citing works
Serengeti: Dynamics of an Ecosystem.
Yalden, D. W., SINCLAIR, A. R. E., & Norton‐Griffiths, M. (1980) · Journal of Ecology · doi:10.2307/2259478 · Cited by 1,215 · see citing works
Royle, J. A., & Dorazio, R. M. (2008) · Cited by 1,180 · see citing works
Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape
O’Brien, T. G., Kinnaird, M. F., & Wibisono, H. T. (2003) · Animal Conservation · doi:10.1017/s1367943003003172 · Cited by 1,118 · see citing works
Estimating animal density using camera traps without the need for individual recognition
Rowcliffe, J. M., Field, J., Turvey, S. T., & Carbone, C. (2008) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2008.01473.x · Cited by 907 · see citing works
Tobler, M. W., Carrillo‐Percastegui, S. E., Pitman, R. L., et al. (2008) · Animal Conservation · doi:10.1111/j.1469-1795.2008.00169.x · Cited by 871 · see citing works
Camera Traps in Animal Ecology
O’Connell, A. F., Karanth, K. U., & Nichols, J. D. (2010) · Springer eBooks · doi:10.1007/978-4-431-99495-4 · Cited by 768 · see citing works
Estimating tiger Panthera tigris populations from camera-trap data using capture—recapture models
Karanth, K. U. (1995) · Biological Conservation · doi:10.1016/0006-3207(94)00057-w · Cited by 671 · see citing works
Camera traps in animal ecology : methods and analyses
O’Connell, A. F., Nichols, J. D., & Kāranta, K. U. (2011) · Cited by 615 · see citing works
Realising the full potential of citizen science monitoring programs
TULLOCH, A. I. T., Possingham, H. P., Joseph, L. N., Szabo, J. K., & Martin, T. G. (2013) · Biological Conservation · doi:10.1016/j.biocon.2013.05.025 · Cited by 577 · see citing works
The use of photographic rates to estimate densities of tigers and other cryptic mammals
Carbone, C., Christie, S., Conforti, K., Coulson, T. N., Franklin, N., Ginsberg, J. R., Griffiths, M., Holden, J., Kawanishi, K., Kinnaird, M. F., et al. (2001) · Animal Conservation · doi:10.1017/s1367943001001081 · Cited by 562 · see citing works
Kéry, M. (2010) · Cited by 478 · see citing works
Investigating species co‐occurrence patterns when species are detected imperfectly
MacKenzie, D. I., Bailey, L. L., & Nichols, J. D. (2004) · Journal of Animal Ecology · doi:10.1111/j.0021-8790.2004.00828.x · Cited by 470 · see citing works
A BAYESIAN STATE-SPACE FORMULATION OF DYNAMIC OCCUPANCY MODELS
Royle, J. A., & Kéry, M. (2007) · Ecology · doi:10.1890/06-0669.1 · Cited by 453 · see citing works
Camera trapping photographic rate as an index of density in forest ungulates
Rovero, F., & Marshall, A. R. (2009) · Journal of Applied Ecology · doi:10.1111/j.1365-2664.2009.01705.x · Cited by 418 · see citing works
MODELING AVIAN ABUNDANCE FROM REPLICATED COUNTS USING BINOMIAL MIXTURE MODELS
Kéry, M., Royle, J. A., & Schmid, H. (2005) · Ecological Applications · doi:10.1890/04-1120 · Cited by 358 · see citing works
Spatially explicit models for inference about density in unmarked or partially marked populations
Chandler, R. B., & Royle, J. A. (2013) · The Annals of Applied Statistics · doi:10.1214/12-aoas610 · Cited by 320 · see citing works
Crowd science user contribution patterns and their implications
Sauermann, H., & Franzoni, C. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408907112 · Cited by 303 · see citing works
SPATIALLY EXPLICIT MODELS FOR INFERENCE ABOUT DENSITY IN UNMARKED OR PARTIALLY MARKED POPULATIONS
Chandler, R. B., & Royle, J. A. (2012) · Lincoln (University of Nebraska) · Cited by 296 · see citing works
Holdø, R. M., Holt, R. D., & Fryxell, J. M. (2009) · The American Naturalist · doi:10.1086/597229 · Cited by 280 · see citing works
SAMPLING DESIGN TRADE-OFFS IN OCCUPANCY STUDIES WITH IMPERFECT DETECTION: EXAMPLES AND SOFTWARE
Bailey, L. L., Hines, J. E., Nichols, J. D., et al. (2007) · Ecological Applications · doi:10.1890/1051-0761(2007)017[0281:sdtios]2.0.co;2 · Cited by 255 · see citing works
OCCUPANCY ESTIMATION AND MODELING WITH MULTIPLE STATES AND STATE UNCERTAINTY
Nichols, J. D., Hines, J. E., MacKenzie, D. I., et al. (2007) · Ecology · doi:10.1890/06-1474 · Cited by 221 · see citing works
Abundance, Density and Relative Abundance: A Conceptual Framework
O’Brien, T. G. (2010) · Springer eBooks · doi:10.1007/978-4-431-99495-4_6 · Cited by 205 · see citing works
The Wildlife Picture Index: monitoring top trophic levels
O'Brien, T. G., Baillie, J. E. M., Krueger, L., & Cuke, M. (2010) · Animal Conservation · doi:10.1111/j.1469-1795.2010.00357.x · Cited by 151 · see citing works
A spatial mark–resight model augmented with telemetry data
Sollmann, R., Gardner, B., Parsons, A. W., et al. (2012) · Ecology · doi:10.1890/12-1256.1 · Cited by 145 · see citing works
Fear of Darkness, the Full Moon and the Nocturnal Ecology of African Lions
Packer, C., Swanson, A., Ikanda, D. K., et al. (2011) · PLoS ONE · doi:10.1371/journal.pone.0022285 · Cited by 145 · see citing works
Self-citationSINCLAIR, A. R. E., Packer, C., Mduma, S., & Fryxell, J. M. (2008) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226760353.001.0001 · Cited by 119 · see citing works
Self-citationHierarchical modelling and estimation of abundance and population trends in metapopulation designs
Kéry, M., & Royle, J. A. (2009) · Journal of Animal Ecology · doi:10.1111/j.1365-2656.2009.01632.x · Cited by 106 · see citing works
Data acquisition and management software for camera trap data: A case study from the TEAM Network
Fegraus, E., Lin, K., Ahumada, J., Baru, C., Chandra, S., & Youn, C. (2011) · Ecological Informatics · doi:10.1016/j.ecoinf.2011.06.003 · Cited by 100 · see citing works
A Novel Method to Reduce Time Investment When Processing Videos from Camera Trap Studies
Swinnen, K. R., Reijniers, J., Breno, M., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0098881 · Cited by 89 · see citing works
O’Brien, T. G., & Kinnaird, M. F. (2011) · Ecological Applications · doi:10.1890/10-2284.1 · Cited by 67 · see citing works
Striped rabbits in Southeast Asia
Surridge, A. K., Timmins, R. J., Hewitt, G. M., et al. (1999) · Nature · doi:10.1038/23393 · Cited by 65 · see citing works
The Asian Tapir in Kerinci Seblat National Park, Sumatra: evidence collected through photo-trapping
Holden, J., Yanuar, A., & Martyr, D. J. (2003) · Oryx · doi:10.1017/s0030605303000097 · Cited by 63 · see citing works
O’Brien, T. G. (2008) · Animal Conservation · doi:10.1111/j.1469-1795.2008.00178.x · Cited by 53 · see citing works
Software for minimalistic data management in large camera trap studies
Krishnappa, Y. S., & Turner, W. C. (2014) · Ecological Informatics · doi:10.1016/j.ecoinf.2014.06.004 · Cited by 47 · see citing works
Using claw marks to study lion predation on giraffes of the S erengeti
Strauss, M. K. L., & Packer, C. (2012) · Journal of Zoology · doi:10.1111/j.1469-7998.2012.00972.x · Cited by 41 · see citing works
Self-citationBitetti, D., & Mario, S. G. (2011) · Mastozoología neotropical · Cited by 15 · see citing works
Swanson, A. B., Kosmala, M., Lintott, C. J., et al. (2014) · Data Archiving and Networked Services (DANS) · doi:10.5061/dryad.5pt92 · Cited by 10 · see citing works
Self-citationLintott, C., Simpson, R., Smith, A., et al. (2015) · Figshare · doi:10.6084/m9.figshare.1397518 · Cited by 1 · see citing works
Self-citationSnapshot Serengeti Processing and Aggregation Scripts
Kosmala, M., Swanson, A., Lintott, C., et al. (2015) · Figshare · doi:10.6084/m9.figshare.1397507 · Cited by 1 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 1
- Hines G., Swanson A., Kosmala M. & Lintott C. J. in Proceedings of the Twenty-Seventh Annual Conference on Innovative Applications of Artificial Intelligence (IAAI-15) (2015).
Cited by
Sorted by citations
Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., & Clune, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1719367115 · Cited by 1,230 · see citing works
Self-citationA Survey of Deep Active Learning
Ren, P., Xiao, Y., Chang, X., Huang, P.-Y., Li, Z., Gupta, B. B., Chen, X., & Wang, X. (2022) · ACM Computing Surveys · doi:10.1145/3472291 · Cited by 1,169 · see citing works
Perspectives in machine learning for wildlife conservation
Tuia, D., Kellenberger, B., Beery, S., Costelloe, B. R., Zuffi, S., Risse, B., Mathis, A., Mathis, M. W., van Langevelde, F., Burghardt, T., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-27980-y · Cited by 740 · see citing works
Applications for deep learning in ecology
Christin, S., Hervet, É., & Lecomte, N. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13256 · Cited by 724 · see citing works
Recognition in Terra Incognita
Beery, S., Van Horn, G., & Perona, P. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01270-0_28 · Cited by 679 · see citing works
Machine learning to classify animal species in camera trap images: Applications in ecology
Tabak, M. A., Norouzzadeh, M. S., Wolfson, D. W., Sweeney, S. J., Vercauteren, K. C., Snow, N. P., Halseth, J. M., Di Salvo, P. A., Lewis, J. S., White, M. D., et al. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13120 · Cited by 504 · see citing works
Schneider, S., Taylor, G. W., Linquist, S., & Kremer, S. C. (2019) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.13133 · Cited by 245 · see citing works
Shahinfar, S., Meek, P., & Falzon, G. (2020) · Ecological Informatics · doi:10.1016/j.ecoinf.2020.101085 · Cited by 229 · see citing works
Chimpanzee face recognition from videos in the wild using deep learning
Schofield, D., Nagrani, A., Zisserman, A., Hayashi, M., Matsuzawa, T., Biro, D., & Carvalho, S. (2019) · Science Advances · doi:10.1126/sciadv.aaw0736 · Cited by 224 · see citing works
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
Beery, S., Wu, G., Rathod, V., Votel, R., & Huang, J. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.01309 · Cited by 140 · see citing works
Species Distribution Modeling for Machine Learning Practitioners: A Review
Beery, S., Cole, E., Parker, J., Perona, P., & Winner, K. (2021) · ACM SIGCAS Conference on Computing and Sustainable Societies · doi:10.1145/3460112.3471966 · Cited by 100 · see citing works
Palmer, M. S., Huebner, S. E., Willi, M., Fortson, L., & Packer, C. (2021) · Human Computation · doi:10.15346/hc.v8i2.123 · Cited by 20 · see citing works
Self-citation
- Authors
- Swanson, A., Kosmala, M., Lintott, C., Simpson, R., Smith, A., & Packer, C.
- Affiliations
- University of Minnesota (US)
- University of Oxford (GB)
- Adler Planetarium (US)
- Corresponding author
- Swanson, Alexandra
- Venue
- Scientific Data
- Published
- 2015-06-09
- Volume
- 2
- Issue
- 1
- Pages
- 150026-150026
- ISSN
- 2052-4463 (ISSN-L)
- PMC
- PMC4460915
- OpenAlex
- W2413367505
- Cited by
- 510
- References
- 47
- Citations by year
- 2026: 43 · 2025: 51 · 2024: 56 · 2023: 59 · 2022: 60 · 2021: 69 · 2020: 54 · 2019: 48 · 2018: 31 · 2017: 19 · 2016: 16 · 2015: 4
- Type
- data paper
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Data (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Funders
- National Science Foundation (1020479)
- Alfred P. Sloan Foundation
- University of Minnesota
- American Society of Mammalogists
- Topics
- Physical Sciences › Environmental Science › Ecology › Wildlife Ecology and Conservation
- Physical Sciences › Environmental Science › Ecological Modeling › Species Distribution and Climate Change
- Physical Sciences › Environmental Science › Ecology › Animal Ecology and Behavior Studies
- Keywords
- camera trap images, mammals, African savanna, Serengeti National Park, citizen science, image classification, consensus classification, animal behavior, species abundance, machine learning, computer vision
- MeSH terms
- Animals, Behavior, Animal, Image Processing, Computer-Assisted, Mammals, Tanzania, Ecosystem
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
