[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119129-en":3,"doc-seo-119129-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119129,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","Combining UAV-imagery and machine learning for wildlife conservation","Semi-arid savannas face degradation due to shifts in the balance between rainfall, fires, and grazing pressure from wildlife and cattle. Effective land management requires keeping animal numbers aligned with grass availability, but routine wildlife census methods are costly and labor-intensive. This work evaluates automated animal detection from UAV-acquired very high resolution imagery, using a Namibian dataset from the Kuzikus Wildlife Reserve and ground truth produced via crowd-sourcing. Machine learning models based on BoVW, exemplar SVMs, and active learning achieve promising recall of roughly 60–80% when low precision (about 5–20%) is tolerated, while analyzing acquisition factors such as image resolution and time of day.","ECOLE POLYTECHNIQUE FÉDÉRALE DE LAUSANNE  \nMASTER THESIS  \nCombining UAV-imagery and machine learning for  \nwildlife conservation  \nAuthor:  \nNicolas Rey  \nSupervisors:  \nDr. Stéphane Joost Prof. Dr. Devis Tuia  \nLaboratory of Geographic Information Systems (LASIG)  \nJune 2016  \n2  \nSemi-arid savannas are endangered by changes in the fragile equilibrium between rainfalls, fires and grazing pressure exerted by wildlife or cattle. To avoid bush encroachment and the decline of perennial grass , land managers must pay attention to keep the amount of cattle and wildlife in balance with the grass availability. In large farms and conservation parks, to estimate the animal populations is therefore an important management aspect.  \nTraditional methods of animal census – such as transect counts from a helicopter, or mark / recapture – are too expensive and laborious to be conducted on a regular basis. In this context unmanned aerial vehicles (UAVs) appear as an interesting tool for animals detection. They can be easily deployed, for lower cost and an increased safety. The drawback is that it is difficult to visually interpret the large number of very high resolution (VHR) images that they acquire. The recent advances in machine learning techniques could allow to automate the detection of animals in these aerial images.  \nThis project aims to implementing such algorithms in order to investigate the feasibility and potential benefits of combining machine learning and UAVs for animals detection. This study uses an image dataset acquired in the Kuzikus Wildlife Reserve in Namibia and aground truth acquired through crowd-sourcing. The machine learning techniques involved include Bags of visual Words, exemplar SVMs and active learning. The promising results show that recall rates in the range of 60 to 80% are possible, if a low precision (5 to 20%) is accepted. The study also discusses parameters related to the data acquisition, such as the image resolution and the time of the day when the images are acquired.  \nRésumé  \nLes savanes semi-arides sont menacées par des changements dans le fragile équilibre entre les pluies, les feux de brousse et la pression pastorale exercée par le bétail et les herbivores sauvages. Afin d’éviter l’avancement des broussailles ligneuses et le déclin des herbes pérennes, les éleveurs et gardiens de parcs doivent être attentifs à maintenir un nombre d’animaux en adéquation avec le fourrage disponible. Ainsi, estimer les populations d’herbivores des grandes fermes et parcs naturels est une étape importante dans la gestiondes savanes semi-arides.  \nLes méthodes traditionnelles pour le comptage des animaux – telles que les comptages par transectes ou par marquage et recapture – sont trop chères et trop laborieuses pour être utilisées de façon régulière. Dans ce contexte, les véhicules aériens sans pilotes (UAV) semblent être un outil intéressant pour la détection et le comptage des animaux. Ils sont faciles à déployer, moins onéreux et assurent une meilleure sécurité . L’inconvénient est qu’il est difficile d’interpréter manuellement le grand nombre d’images à très haute résolution (VHR) produites par les UAVs. Les avancées récentes en apprentissage machine pourraient permettre d’automatiser la reconnaissance d’animaux dans les images aériennes.  \nCe projet a pour but d’implémenter un tel système afin d’étudier la faisabilité et les bénéfices de l’utilisation conjointe d’imagerie par UAVs et d’apprentissage machine pour ladétection d’animaux. Elle se base sur des images aériennes acquises dans la Kuzikus Wildlife Reserve et sur une réalité-terrain obtenue par crowd-sourcing. Les méthodes d’apprentissage machine employées dans cette étude sont notamment les suivantes : bag of visual words (BoVW), exemplar SVMs, apprentissage actif. Les résultats encourageants montrent que les méthodes implémentées permettent d’obtenir un taux de rappel entre 60 et 80%, pour autant qu’une précision relativement faible soit acceptée (de","cbCaioyF54Fywerj","https://ap.wps.com/l/cbCaioyF54Fywerj","pdf",2077979,1,59,"English","en",105,"# 1. Introduction\n## 1.1 Carrying capacity\n## 1.2 Bush encroachment","[{\"question\":\"Why are traditional animal census methods difficult to use regularly in this context?\",\"answer\":\"They are too expensive and laborious to conduct on a regular basis, making frequent population monitoring challenging.\"},{\"question\":\"What data sources support the study’s wildlife detection experiments?\",\"answer\":\"The study uses a UAV image dataset acquired in the Kuzikus Wildlife Reserve in Namibia and ground truth collected through crowd-sourcing.\"},{\"question\":\"Which machine learning approaches are applied to detect animals in UAV images?\",\"answer\":\"The methods include Bags of visual Words (BoVW), exemplar SVMs, and active learning.\"}]","Combining UAV-imagery and machine learning for wildlife conservation | PDF",1785722550,149,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"combining-uav-imagery-and-machine-learning-for-wildlife-conservation","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/combining-uav-imagery-and-machine-learning-for-wildlife-conservation/119129/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why are traditional animal census methods difficult to use regularly in this context?","Question",{"text":76,"@type":77},"They are too expensive and laborious to conduct on a regular basis, making frequent population monitoring challenging.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What data sources support the study’s wildlife detection experiments?",{"text":81,"@type":77},"The study uses a UAV image dataset acquired in the Kuzikus Wildlife Reserve in Namibia and ground truth collected through crowd-sourcing.",{"name":83,"@type":74,"acceptedAnswer":84},"Which machine learning approaches are applied to detect animals in UAV images?",{"text":85,"@type":77},"The methods include Bags of visual Words (BoVW), exemplar SVMs, and active learning.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]