Drone Aerial Video Datasets for AI, Mapping, and Surveillance | Statswork

Image Annotation

Get Your ML Training data to build better image, video, text and speech recognition with meaningful information that will be used to train and improve machine learning models.
We can use, bounding boxes, polygons, key points, and semantic segmentation to annotate image objects, features, and specific areas of interest accurately. Our image annotation services provide your models with quality and precise visual data.

High-Quality Custom Image

Your ML algorithms need a lot of datasets to effectively recognize videos. However, the video set that you require has to be the right volume and best match the requirements that are needed to carry out the training. Statswork offers a variety ofhigh-qualityvideo datasetsto match any specifications. Whether it’s gesture or motion detection or facial tracking data for advanced sentiment analyses, Statswork offers video datasets for any demographics in the form of individualized video sets to achieve outstanding results.This includes tracking human interactions, collecting license plates, motion sequences while doing different types of housework, gestures, sports activities, scenes, objects, animals, to watching an audience for signs of happiness.
Types of Image annotations
Draw rectangular boxes around objects to see both their location, and how big they are in an image—key for object detection.
Uses cuboids to capture depth, orientation, and spatial volume of objects— this is mostly used for autonomous vehicle perception systems.
Labels each pixel in an image with a class to see what represents the scene and advance comprehension at the pixel level
Like semantic segmentation but as a class of different object instances.
Encloses irregularly shaped objects with remarkable accuracy by outlining with multiple vertices—helps for detailed contours of objects.
Semantic and instance segmentation combined so every pixel can be labelled, and every object can have a unique identifier, including the pixelation of overlapping instances in the same image.
Labelling and segmenting text within images for OCR tasks—used in document scanning and license plate detection techniques.
Tagging whole images with designed categories (eg “cat,” “vehicle,” “tumor”) to train classification models.
To develop techniques for predicting motion and tracking about an axis of relative translation, annotating and linking in the same image, in multiple frames, the same object.
To add descriptive tags or attributes to images (i.e. weather, color, feelings) to create richer datasets for multi-label learning.
Marking important points in objects (like facial landmarks, body joints, or mechanical components) for objective pose estimation and motion analysis.
specialised annotation of medical imaging like X-Rays, MRIs, or CT scans from health domain experts relevant to the development of healthcare-focused AI.
Annotating aerial or satellite images including ND
Used to markup boundaries, lanes, roads, pipelines, and other linear features frequently implemented
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