Seabird (Tern) Detection - Africa

Name: Seabird (Tern) Detection - Africa
Creator: Esri
License: 暂无描述

Esri Aid & Development Team2026-03-28 收录

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<div style='display:inherit; font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; max-width:100%;'>Seabirds are found near marine habitats, such as sea and wetlands, due to food availability. They mostly feed on fish and insects. The seabird population is declining at a much faster rate compared to other birds as the coastal region is sensitive to pollution, commercial fisheries, habitat degradation, mineral extraction, human disturbance, etc. Seabirds are also endangered by predatory species from both land and water. Apart from the geography of their habitat, they do not have much ability to defend their nest or protect their young ones. Breeding and laying of eggs happen in open habitats, such as bare ground and open sandy or rocky areas, on coasts and islands with little or no nest material.The Royal tern and Caspian tern are two of the 350 odd seabird species. These adult terns could be of size 45-60 cm weighing 350-750 gm. Their size puts them in the category of small objects and thus we need very high-resolution imagery to detect them. Recent innovations in drones and AI have enabled us to capture high-resolution imagery over a large geographic area and detect objects of different shapes and sizes. Drones also decrease the disturbance to bird population. Drones are easier to deploy and can perform frequent surveys even after disasters like hurricanes, oil spills, etc. This deep learning model helps automate the task of detecting seabirds (Royal and Caspian terns) from high-resolution aerial imagery. This can help in mapping effective site protection areas for seabirds. Using the model Follow the <a style='font-family:inherit; font-size:16px; text-indent:0cm;' target='_blank' href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/using-seabird-tern-detection-africa.htm' rel='nofollow ugc noopener noreferrer'>guide</a> to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check <a style='font-family:inherit; text-decoration-line:none;' target='_blank' href='https://github.com/esri/deep-learning-frameworks' rel='nofollow ugc noopener noreferrer'>Deep Learning Libraries Installer for ArcGIS</a>. Fine-tuning the model This model can be fine-tuned using the Train Deep Learning Model tool. Follow the <a target='_blank' href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/finetuning-the-seabird-tern-detection-africa.htm' rel='nofollow ugc noopener noreferrer'>guide</a> to fine-tune this model. Input High resolution RGB imagery (1.0 cm resolution).  Output Feature class containing detected seabirds. Applicable geographies The model is expected to work well with aerial imagery of West African coast or similar geographies. Model architecture This model uses the <a style='font-family:inherit; font-size:16px; text-decoration-line:none;' target='_blank' href='https://developers.arcgis.com/python/guide/how-maskrcnn-works/' rel='nofollow ugc noopener noreferrer'>Mask R-CNN</a> model architecture implemented in ArcGIS API for Python. Accuracy metrics This model has an average precision score of 0.76 for seabird. Training data The model has been trained on the Aerial Seabirds West Africa. Limitations<div style='text-align:start;'><ul><li> This model works well only with very high-resolution aerial imagery.</li><li> It is trained on imagery of colonies of Royal and Caspian tern species in a coastal region.</li></ul></div> Sample results Here are a few results from the model.<div style='font-family:inherit;'><img style='font-size:15px; height:auto; max-width:100%;' src='https://www.arcgis.com/sharing/rest/content/items/bcd5725802064ec192d5c618796948bd/data' /><img style='height:auto; max-width:100%;' src='https://www.arcgis.com/sharing/rest/content/items/abc0189d07c24e8c9fbe3ceb5aed2241/data' alt='detected_seabirds' /><img style='font-size:15px; height:auto; max-width:100%;' src='https://www.arcgis.com/sharing/rest/content/items/fa6a51798f3a43d58e42d9d9355a4f7c/data' /> <img style='font-size:15px; height:auto; max-width:100%;' src='https://www.arcgis.com/sharing/rest/content/items/16f723542b2e4b63977842591c834700/data' /> Citations <a style='font-family:inherit; font-size:16px; text-decoration-line:none;' target='_blank' href='https://zslpublications.onlinelibrary.wiley.com/doi/10.1002/rse2.200' rel='nofollow ugc noopener noreferrer'>Kellenberger B, Veen T, Folmer E, Tuia D. 21,000 birds in 4.5 h: efficient large‐scale seabird detection with machine learning. Remote Sensing in Ecology and Conservation. 2021.</a></div></div>

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