Automated classification of natural habitats using ground-level imagery

• URL: http://arxiv.org/abs/2508.19314v1
• Date: Tue, 26 Aug 2025 10:57:53 GMT
• Title: Automated classification of natural habitats using ground-level imagery
• Authors: Mahdis Tourian, Sareh Rowlands, Remy Vandaele, Max Fancourt, Rebecca Mein, Hywel T. P. Williams,
• Abstract summary: We present a methodology for classification of habitats based solely on ground-level imagery (photographs)<n>This study develops a classification system that applies deep learning to ground-level habitat photographs, categorising each image into one of 18 classes defined by the 'Living England' framework.<n>Using five-fold cross-validation, the model demonstrated strong overall performance across 18 habitat classes, with accuracy and F1-scores varying between classes.
• Score: 1.9646275424931439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate classification of terrestrial habitats is critical for biodiversity conservation, ecological monitoring, and land-use planning. Several habitat classification schemes are in use, typically based on analysis of satellite imagery with validation by field ecologists. Here we present a methodology for classification of habitats based solely on ground-level imagery (photographs), offering improved validation and the ability to classify habitats at scale (for example using citizen-science imagery). In collaboration with Natural England, a public sector organisation responsible for nature conservation in England, this study develops a classification system that applies deep learning to ground-level habitat photographs, categorising each image into one of 18 classes defined by the 'Living England' framework. Images were pre-processed using resizing, normalisation, and augmentation; re-sampling was used to balance classes in the training data and enhance model robustness. We developed and fine-tuned a DeepLabV3-ResNet101 classifier to assign a habitat class label to each photograph. Using five-fold cross-validation, the model demonstrated strong overall performance across 18 habitat classes, with accuracy and F1-scores varying between classes. Across all folds, the model achieved a mean F1-score of 0.61, with visually distinct habitats such as Bare Soil, Silt and Peat (BSSP) and Bare Sand (BS) reaching values above 0.90, and mixed or ambiguous classes scoring lower. These findings demonstrate the potential of this approach for ecological monitoring. Ground-level imagery is readily obtained, and accurate computational methods for habitat classification based on such data have many potential applications. To support use by practitioners, we also provide a simple web application that classifies uploaded images using our model.

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