Abstract: An approach is provided for filtering imagery to train a feature detection model. The approach involves, for example, receiving a plurality of images. The plurality of images is classified as depicting a feature of interest. The approach also involves providing data for presenting a bulk arrangement of at least one subset of the plurality of images, wherein the bulk arrangement is based on a characteristic of the plurality of images (e.g., detection confidence, position, size, visual characteristic, etc. of the detected feature). The approach further involves initiating a filtering of the plurality of images based on the bulk arrangement and providing the filtered plurality of images as training data to train the feature detection model.

Abstract: An approach is provided for presenting an annotation task. The approach involves, for example, selecting a designated number of one or more points in an image. The approach also involves providing data for presenting a user interface indicating the one or more points in the image comprising the designated number. The user interface provides at least one user interface element for annotating one or more objects in the image corresponding to the designated number of the one or more points during an annotation session. The approach further involves initiating an end of the annotation session based on determining that the one or more objects in the image have been annotated. The approach further involves storing the annotated one or more objects.

Abstract: An approach is provided for filtering imagery to train a feature detection model. The approach involves, for example, receiving a plurality of images. The plurality of images is classified as depicting a feature of interest. The approach also involves providing data for presenting a bulk arrangement of at least one subset of the plurality of images, wherein the bulk arrangement is based on a characteristic of the plurality of images (e.g., detection confidence, position, size, visual characteristic, etc. of the detected feature). The approach further involves initiating a filtering of the plurality of images based on the bulk arrangement and providing the filtered plurality of images as training data to train the feature detection model.