The recording of plant and animal species found in the wild is what makes up animal and plant species classification data.
Both professionals and laypeople contribute to species classification data with images and information from various sources. Professionals may use high-tech images from satellites, LiDAR, and more. Laypeople, meanwhile, are able to submit images from ordinary cameras and smartphones to public, open-source databases.
Species classification data consists of images and the data about the images, especially the date, location, and species featured in the image. This information can be assigned by either humans or machine learning programs.
Animal and plant classification data has a wide range of uses, including education, entertainment, research, and conservation.
The main concerns with species classification data are image quality and the lack of images for certain less-common species. For both humans and machine learning programs, the smaller numbers of uncommon species images for training samples end up leading to greater rates of incorrect identification.
An additional concern for machine learning programs is that the sub-field of image recognition is still new and there is not without problems.
However, during the past two decades, a variety of commercial satellites have begun to collect data at a higher spatial resolution, capable of capturing ground objects measuring one square metre or less. This resolution improvement places the field of terrestrial remote sensing on the threshold of a fundamental leap forward: from focusing on aggregate landscape-scale measurements to having the potential to map the location and canopy size of every tree over large regional or global scales. This revolution in observational capabilities will undoubtedly drive fundamental changes in how we think about, monitor, model and manage global terrestrial ecosystems.
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