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 general-ize 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. Fur-ther, we introduce a large aerial imagery dataset with more than 11 k Images and 28k annotations verified by domain 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 Github11https://github.com/UCSB-VRL/WildlifeMapper.

Abstract Each year, underwater remotely operated vehicles (ROVs) collect thousands of hours of video of unexplored ocean habitats revealing a plethora of information regarding biodiversity on Earth. However, fully utilizing this information remains a challenge as proper annotations and analysis require trained scientists’ time, which is both limited and costly. To this end, we present a Dataset for Underwater Substrate and Invertebrate Analysis (DUSIA), a benchmark suite and growing large-scale dataset to train, validate, and test methods for temporally localizing four underwater substrates as well as temporally and spatially localizing 59 underwater invertebrate species. DUSIA currently includes over ten hours of footage across 25 videos captured in 1080p at 30 fps by an ROV following pre-planned transects across the ocean floor near the Channel Islands of California. Each video includes annotations indicating the start and end times of substrates across the video in addition to counts of species of interest. Some frames are annotated with precise bounding box locations for invertebrate species of interest, as seen in Fig. 1. To our knowledge, DUSIA is the first dataset of its kind for deep sea exploration, with video from a moving camera, that includes substrate annotations and invertebrate species that are present at significant depths where sunlight does not penetrate. Additionally, we present the novel context-driven object detector (CDD) where we use explicit substrate classification to influence an object detection network to simultaneously predict a substrate and species class influenced by that substrate. We also present a method for improving training on partially annotated bounding box frames. Finally, we offer a baseline method for automating the counting of invertebrate species of interest.