- Data Categories
Agriculture Data 
B2B Data 
Biometric Data 
Company Data 
Conservation Data 
Demographic Data 
Economic Data 
Environmental Data 
Geospatial Data 
Healthcare Data 
Individual Data 
Industry-Specific Data 
Internet of Things (IoT) Data 
Legal and IP Data 
People and Employment Data 
Political Data 
Psychology Data 
Real Estate Data 
Retail & Commerce Data 
Social Data 
Space Exploration Data 
Sports & Entertainment Data 
Web Data
- Data Categories
- Agriculture Data
- B2B Data
- Biometric Data
- Company Data
- Conservation Data
- Demographic Data
- Economic Data
- Environmental Data
- Geospatial Data
- Healthcare Data
- Individual Data
- Industry-Specific Data
- Internet of Things (IoT) Data
- Legal and IP Data
- People and Employment Data
- Political Data
- Psychology Data
- Real Estate Data
- Retail & Commerce Data
- Social Data
- Space Exploration Data
- Sports & Entertainment Data
- Web Data
What Is Animal and Plant Species Classification Data?
Species classification data identifies and records the plant and animal species found in the wild.
Where Does Animal and Plant Species Classification Data Come From?
Both professionals and laypeople contribute to species classification data with images from various sources. Professionals may use high-tech images from satellites, LiDAR, and more. Lay people, meanwhile, submit images from ordinary cameras and smartphones to public, open-source databases.
What Types of Columns/Attributes Should I Expect When Working with This Data?
Species classification data consists of images and the data about them, especially date, location, and species. Either humans or machine learning programs can assign this information to the images.
What Is This Data Used For?
Animal and plant classification data has a wide range of uses, including education, entertainment, research, and conservation.
How Should I Test the Quality of Species Classification Data?
The main concerns with species classification data is 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 leads 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 not without problems.
Interesting Case Studies and Blogs to Look Into
iNaturalist
GBIF: Global Biodiversity Information Facility
Tangible Examples of Impact
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.
Relevant datasets
Atmospheric and Environmental Science Earth
AER – Earth provides research & studies on everything interconnected with our planet
HealRWorld Sustain Planet Earth Committed
Sustain Planet Earth Committed™ is based on the SDG of the United Nation for small and mid-enterprises.
Geocento EarthImages
EarthImages is a web-based platform for searching, comparing and accessing imagery
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