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Computer Vision

Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding

17,549 datasets

Computer Vision

Input and output data (images + boulder labels, model setup, model weights and more) for t

Planetary images and labeled boulders from Earth, Mars, and the Moon, supporting the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images". The dataset includes 8.8 GB of raw rasters and shapefiles, plus pre-processed training data and model weights for a Mask R-CNN model named BoulderNet. It was authored by Nils C. Prieur of the University of Oslo.

ImageGeospatialSatellite ImageryComputer VisionBoulder DetectionPlanetary ScienceSynthetic+1

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Computer Vision

Mars Surface Images from Curiosity Rover, 2012-2015

6,691 labeled Mars surface images collected by the Curiosity rover's Mastcam and MAHLI instruments between August 2012 and July 2015. The dataset, created by researchers at the Jet Propulsion Laboratory, is split into 24 classes and partitioned into train, validation, and test sets based on the Martian day of acquisition. Images are provided as lower-resolution 'browse' versions at roughly 256x256 pixels.

ImageGeospatialLabeled DataMars ImageryComputer VisionPlanetary ScienceSynthetic+1

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Computer Vision

Baseline Biogeochemical Data from Australia's Continental Margin, 2007-2014

Surficial seabed samples from over 350 locations across Australia's western, northern, and eastern continental margins collected during federal government surveys between 2007 and 2014. The dataset includes parameters for organic matter source, concentration, and bioavailability, analyzed by researchers from the Australian Ocean Data Network. The study links sediment properties to water column productivity and makes inferences about governing biogeochemical processes.

TabularGeospatialOceanographyBenchmarkWater QualityBiogeochemistryAustralia CoastalMarine Sediments+1

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Computer Vision

MarSOC: Tidal Marsh Soil Organic Carbon Data from 29 Countries

A dataset of 17,454 tidal marsh soil organic carbon records compiled from a systematic literature review. It was created by Tania L. Maxwell at the University of Cambridge. The dataset includes metadata and processing scripts for data cleaning and model testing.

TabularGeospatialEnvironmental scienceSoil carbonTidal MarshSynthetic+1

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Computer Vision

Pancreatic Zymogen Granule Purification Protocol Using Percoll Gradients

A procedure for purifying pancreatic zymogen granules in large quantity based on Percoll gradient centrifugation. The method is described by Xuequn Chen and John A. Williams from the University of Michigan. Percoll, a non-toxic colloidal silica with a density of 1.130 g/ml, is used to separate organelles with densities greater than 1.060 g/ml.

TabularBiochemistry MethodsDensity Gradient CentrifugationOrganelle PurificationPancreatic Zymogen Granules+1

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Computer Vision

Oxidation State Assignments for Metal Centers from Crystallographic Data

A machine learning model trained on chemist-assigned oxidation states encoded in chemical names from the Cambridge Crystallographic Database. The approach considers the immediate local chemical environment around a metal center and achieves a prediction accuracy exceeding 98%. This work by Kevin Maik Jablonka of École Polytechnique Fédérale de Lausanne demonstrates how collective knowledge can be harvested to create a tool for chemists.

TabularMachine LearningOxidation StateChemistryCrystallographyMaterials Science+1

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Computer Vision

Chacma Baboon Approach Behavior Data from Gorongosa National Park, 2018-2019

Jana Muschinski from the University of Oxford provides a dataset of 341 proximity events for chacma baboons (Papio ursinus griseipes). The data records presence/absence of approach behaviors where one baboon moved within 2 meters of another, collected from video footage filmed in Gorongosa National Park, Mozambique, between 2018 and 2019. Video coding was performed between 2020 and 2021 using BORIS software, with data cleaned and formatted into a matrix using Python and R.

TabularGeospatialBehavioral EcologyPresence AbsenceEthologyPrimate BehaviorGorongosa+1

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Computer Vision

Cultural Places of Uganda from OpenStreetMap Volunteer Mapping

OpenStreetMap volunteer mappers have tagged cultural and historic places across Uganda. The dataset includes museums, galleries, theatres, places of worship, monuments, and historic sites, sourced from a free, collaborative world map. It was last updated on 2026-06-22 22:07:01.337716 by the Humanitarian OpenStreetMap Team.

GeospatialJSONTourismOpenstreetmapHistoric SitesCultural Heritage+1

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Computer Vision

Points of Interest in Uganda from OpenStreetMap

Uganda's public-facing amenities, shops, tourist attractions, and landmarks tagged via OpenStreetMap keys. The data is useful for general POI reference, urban analysis, and basemap detail. It was created by the Humanitarian OpenStreetMap Team and last updated on 2026-06-22.

GeospatialJSONUgandaFacilities InfrastructureGeodataOpenstreetmapPoints Of InterestUrban AnalysisPoints Of Interest Poi+1

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Computer Vision

Sea Ports and Ferry Terminals of Uganda from OpenStreetMap

Sea ports and ferry terminals in Uganda, tagged via OpenStreetMap keys `amenity`, `building`, or `port`. The data is sourced from OpenStreetMap, a free, collaborative world map, and is provided by the Humanitarian OpenStreetMap Team (HOT). The dataset was last updated on 2026-06-22.

GeospatialJSONFacilities InfrastructureGeodataOpenstreetmapPortsMaritime Logistics+1

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Computer Vision

Uganda Populated Places and Settlements from OpenStreetMap

OpenStreetMap data provides a collection of named settlements and residential areas across Uganda, including cities, towns, villages, and suburbs. The dataset was compiled by the Humanitarian OpenStreetMap Team (HOT) and last updated on 2026-06-22. Coverage reflects volunteer mapper activity, with urban areas likely better represented than remote regions.

GeospatialJSONGeodataOpenstreetmapPopulated PlacesPopulated Places SettlementsDemographicsSettlements+1

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Computer Vision

Health Facilities of Uganda from OpenStreetMap Volunteer Mapping

OpenStreetMap data provides locations of health facilities in Uganda, including hospitals, clinics, pharmacies, doctors, and dentists tagged via `amenity` or `healthcare` keys. The dataset is maintained by the Humanitarian OpenStreetMap Team (HOT) and was last updated on 2026-06-22. Coverage reflects where volunteer mappers have been active, with urban areas typically better represented than remote regions.

GeospatialJSONUgandaGeodataOpenstreetmapHealthcare AccessHealth FacilitiesHealthcareHealth+1

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Computer Vision

Railway Lines and Stations of Uganda from OpenStreetMap

OpenStreetMap data for railway lines and stations tagged `railway=*` across Uganda, including rail lines, tram tracks, and stations. The dataset is provided by the Humanitarian OpenStreetMap Team (HOT) and was last updated on 2026-06-22. Coverage reflects where volunteer mappers have been active, with urban areas typically well-represented.

GeospatialJSONRailwaysFacilities InfrastructureGeodataOpenstreetmapTransportationInfrastructure+1

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Computer Vision

Waterways of Uganda from OpenStreetMap for Hydrological Analysis

Waterways of Uganda is a geospatial dataset of water features across the region, including rivers, streams, canals, lakes, and ponds. It is sourced from OpenStreetMap, a free collaborative world map, and is provided by the Humanitarian OpenStreetMap Team (HOT). The dataset was last updated on 2026-06-22.

GeospatialJSONGeodataOpenstreetmapRiversHydrologyFlood RiskWaterways+1

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Computer Vision

Road Network of Uganda from OpenStreetMap

Uganda's road network includes motorways, residential streets, service roads, footways, and trails tagged with the OpenStreetMap `highway` key. The data is compiled by the Humanitarian OpenStreetMap Team (HOT) from volunteer contributions and was last updated on 2026-06-22. Coverage likely reflects mapper activity, with urban areas well represented and remote areas potentially sparse.

GeospatialJSONGeodataRoadsOpenstreetmapTransportationHumanitarian+1

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Computer Vision

Points of Interest in Chad from OpenStreetMap Volunteer Mappers

Points of interest across Chad tagged with categories such as amenities, shops, tourist attractions, and man-made landmarks. The data is sourced from OpenStreetMap, a free collaborative world map, and was last updated on 2026-06-22. Coverage reflects volunteer mapper activity, with urban areas likely being well-represented and remote areas potentially sparse.

GeospatialJSONOpenstreetmapPoints Of InterestUrban AnalysisGeospatial DataChadAmenities+1

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Computer Vision

Sea Ports and Ferry Terminals of Chad from OpenStreetMap

OpenStreetMap data for maritime infrastructure in Chad, tagged via amenity, building, or port keys. The dataset is compiled by the Humanitarian OpenStreetMap Team (HOT) and was last updated on 2026-06-22. Coverage reflects volunteer mapper activity, with urban areas likely better represented than remote regions.

GeospatialJSONOpenstreetmapTransportationPortsMaritime LogisticsChad+1

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Computer Vision

Waterways of Chad from OpenStreetMap for Flood Risk Modeling

OpenStreetMap data provides a vector map of water features across Chad, including rivers, streams, canals, lakes, and ponds. The dataset is sourced from volunteer mappers via the Humanitarian OpenStreetMap Team and was last updated on 2026-06-22. Coverage is likely more complete in urban areas where mappers have been active.

GeospatialJSONOpenstreetmapHydrologyWaterwaysChad+1

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Computer Vision

Road Network of Chad from OpenStreetMap

A geospatial dataset of the road network across Chad, tagged with OpenStreetMap's `highway=*` key. It includes features such as motorways, residential streets, service roads, footways, and trails. The data is sourced from OpenStreetMap and was last updated on 2026-06-22.

GeospatialJSONTransportation InfrastructureOpenstreetmapRoad NetworkGeospatial DataChad+1

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Computer Vision

Points of Interest in Syria from OpenStreetMap

Syria's public-facing amenities, shops, tourist attractions, and landmarks tagged via OpenStreetMap keys. The dataset is compiled by the Humanitarian OpenStreetMap Team and was last updated in June 2026. Coverage reflects volunteer mapper activity, with urban areas likely better represented than remote regions.

GeospatialJSONFacilities InfrastructureGeodataOpenstreetmapPoints Of InterestUrban AnalysisSyriaInfrastructurePoints Of Interest Poi+1

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