Elephant Detection

Name: Elephant Detection
Creator: Esri
License: 暂无描述

Esri Aid & Development Team2026-03-28 收录

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<div style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; max-width:100%; display:inherit;'>Elephants are the largest terrestrial living species. They are herbivorous animals and require 100 kilograms to 200 kilograms of food and about 230 liters of water each day. Their home range can expand up to 11,000 square kilometers. Their ability to find food and water sources is gained from traditional knowledge learned over generations. This knowledge, which is important for survival, is lost if elder elephants of the herd perish.Elephants are endangered due to many reasons. They can be killed by poachers for their tusks, or captured and tamed for social status and for the circus. Changes in environment such as global warming, rain patterns, deforestation, and mining can lead to degradation of their habitat, forcing these animals to move to different areas in search of food and water. This can cause conflicts with humans as elephants move into human settlements and farmlands. They can also run into electrical fences and traps. To avoid life-threatening incidents, and for their conservation, monitoring the elephants and their movements is of high importance. It is easier to monitor the elephants using aerial imagery, as it does not require human intervention or disturbance in elephants' natural habitat. Elephant Detection using aerial imagery is more efficient when performed over vast areas. This deep learning model helps automate the task of detecting elephants from high-resolution aerial imagery. Using the model Follow the <a href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/using-elephant-detection.htm' target='_blank' 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 href='https://github.com/esri/deep-learning-frameworks' style='text-decoration-line:none; font-family:inherit;' target='_blank' 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 href='https://doc.arcgis.com/en/pretrained-models/latest/imagery/finetuning-the-elephant-detection.htm' target='_blank' rel='nofollow ugc noopener noreferrer'>guide</a> to fine-tune this model. Input High resolution RGB imagery (3-13 centimeters spatial resolution). Output Feature class containing detected elephants. Applicable geographies The model is expected to work well with aerial imagery of southern African forests (South Africa, Botswana, and Namibia) or similar geographies. Model architecture This model uses the <a href='https://developers.arcgis.com/python/guide/faster-rcnn-object-detector/' style='font-family:inherit; font-size:16px; text-decoration-line:none;' target='_blank' rel='nofollow ugc noopener noreferrer'>FasterRCNN</a> model architecture implemented in ArcGIS API for Python. Accuracy metrics This model has an average precision score of 0.857 for elephant. Training data The model has been trained on the The Aerial Elephant Dataset. Limitations <ul><li>This model works well only with high-resolution aerial imagery.</li><li>This model is trained on imagery of African Bush Elephants. However, it detects all kinds of elephants and is species agnostic</li></ul></div><div style='font-family:"Avenir Next W01", "Avenir Next W00", "Avenir Next", Avenir, "Helvetica Neue", sans-serif; max-width:100%; display:inherit;'><div style='font-family:inherit;'>Sample results Here are a few result from the model. <img alt='detected_elephants' src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/427b5cd7804a4b4e8cccc2a604720ee0/data' style='max-width:100%; height:auto;' /><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/001b2c329e054871bc00c94b38873ec9/data' style='max-width:100%; height:auto; font-size:15px;' /><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/79b81650d07f440a94639abd5704c2b4/data' style='max-width:100%; height:auto; font-size:15px;' /><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/7671bad8b3094625b3e9295769549f09/data' style='max-width:100%; height:auto; font-size:15px;' /><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/b7d3a9dd20154811ab661084640c8791/data' style='max-width:100%; height:auto; font-size:15px;' /><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/22ea69e52d2141988d256ba767e90ff3/data' style='max-width:100%; height:auto; font-size:15px;' /> </div><div style='font-size:16px; font-family:inherit;'><img src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/ff1f09c567054b76ba95a6236ee2bb48/data' style='max-width:100%; height:auto;' /></div><div style='font-size:16px; font-family:inherit;'><img alt='detected_elephants' src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/9acaf55b8b7c4b57b07dd3c0406eff83/data' style='max-width:100%; height:auto; font-size:15px;' /><img alt='detected_elephant' src='https://geosaurus.maps.arcgis.com/sharing/rest/content/items/6f4f288ada294c31a42721c7e34edcb7/data' style='max-width:100%; height:auto; font-size:15px;' /> </div><div style='font-family:inherit;'><img style='max-width:100%; height:auto;' /> Citations <div style='font-family:inherit; max-width:100%; display:inherit;'><a href='https://doi.org/10.5281/zenodo.3234780' style='font-size:16px; text-decoration-line:none; font-family:inherit;' target='_blank' rel='nofollow ugc noopener noreferrer'>Naudé, Johannes J., & Joubert, Deon. (2019). The Aerial Elephant Dataset [Dataset]. Zenodo.</a></div><div style='font-size:16px;'> </div></div></div>

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