Deep learning model classifies natural habitats from ground-level photographs with strong accuracy
By
Mahdis Tourian ,
Summary
This paper presents a methodology for classifying terrestrial habitats using ground-level photographs and deep learning, rather than traditional satellite imagery. Developed in collaboration with Natural England, the study uses a DeepLabV3-ResNet101 architecture to classify images into 16 habitat classes following the 'Living England' framework. The model achieved a mean F1-score of 0.63 across all classes, with high performance (above 0.87) for visually distinct habitats like Bare Sand and Coniferous Woodland, and lower performance for visually mixed classes. The approach supports scalable, robust habitat classification and includes a web application for practitioners to upload and classify images.
Source
bskyDeep learning model classifies natural habitats from ground-level photographs with strong accuracyplos.ioKey quotes
· 5 pulledAccurate classification of terrestrial habitats is critical for biodiversity conservation, ecological monitoring, and land use planning.
This approach supports robust, scalable habitat classification based on balanced and well-prepared training data.
Across all folds, the model achieved a mean F1-score of 0.63, with some habitat classes such as Bare Sand (BS) and Coniferous Woodland (CW) reaching values above 0.87.
Ground-level imagery is easily obtained and accurate computational methods for habitat classification based on such data have many potential applications.
To support use by practitioners, a simple web application is also provided that allows classification of uploaded images using the trained model.
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