{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/wildlifemapper-aerial-image-analysis-for","title":"WildlifeMapper: Aerial Image Analysis for Multi-Species Detection and Identification","arxiv_id":null,"date":"2024-01-01","proceeding":"CVPR 2024 1","authors":["Satish Kumar","BoWen Zhang","Chandrakanth Gudavalli","Connor Levenson","Lacey Hughey","Jared A. Stabach","Irene Amoke","Gordon Ojwang","Joseph Mukeka","Stephen Mwiu","Joseph Ogutu","Howard Frederick","B.S. Manjunath"],"abstract":"    We introduce WildlifeMapper (WM) a flexible model designed to detect locate and identify multiple species in aerial imagery. It addresses the limitations of traditional labor-intensive wildlife population assessments that are central to advancing environmental conservation efforts worldwide. While a number of methods exist to automate this process they are often limited in their ability to generalize to different species or landscapes due to the dominance of homogeneous backgrounds and/or poorly captured local image structures. WM introduces two novel modules that help to capture the local structure and context of objects of interest to accurately localize and identify them achieving a state-of-the-art (SOTA) detection rate of 0.56 mAP. Further we introduce a large aerial imagery dataset with more than 11k Images and 28k annotations verified by trained experts. WM also achieves SOTA performance on 3 other publicly available aerial survey datasets collected across 4 different countries improving mAP by 42%. Source code and trained models are available at Github    ","url_abs":"http://openaccess.thecvf.com//content/CVPR2024/html/Kumar_WildlifeMapper_Aerial_Image_Analysis_for_Multi-Species_Detection_and_Identification_CVPR_2024_paper.html","url_pdf":"http://openaccess.thecvf.com//content/CVPR2024/papers/Kumar_WildlifeMapper_Aerial_Image_Analysis_for_Multi-Species_Detection_and_Identification_CVPR_2024_paper.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"wildlifemapper-aerial-image-analysis-for","repo_url":"https://github.com/ucsb-vrl/wildlifemapper","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}