Ecology
Monitoring wildlife across vast landscapes is essential for conservation, yet traditional field surveys are slow, costly, and hard to scale. Our ecology work pairs aerial and drone imagery with deep-learning detectors — developed and deployed inside BisQue — to automatically find, count, and identify animals and their habitat features from the air, turning raw survey footage into population data in hours rather than weeks.
This research is carried out with the Smithsonian Institution's National Zoo & Conservation Biology Institute and partner wildlife institutes. Every model below is available to run in the browser through the BisQue Analysis Modules.
WildlifeMapper
Detecting, locating, and identifying wildlife from aerial surveys.
WildlifeMapper is a deep learning module for detecting, locating, and identifying multiple animal species in large-scale aerial survey imagery. It replaces the slow, labor-intensive manual counting that underpins wildlife population assessments, giving conservation teams a faster and more consistent way to census animals across vast landscapes.
Unlike earlier detectors that struggle when animals are small and backgrounds are highly uniform, WildlifeMapper introduces a High-Frequency Feature Generator that sharpens the local image structures needed to separate wildlife from terrain. It was trained and validated on a curated dataset of more than 11,000 aerial images and reaches state-of-the-art accuracy across several public aerial survey datasets, improving mean average precision by 42% over prior methods.
- Multi-species detection, localization, and identification from aerial imagery
- High-Frequency Feature Generator for robust detection against homogeneous backgrounds
- Curated and verified dataset of 11k+ aerial images
- State-of-the-art performance across datasets from four countries
- Runs inside BisQue for browser-based analysis
- Built with conservation partners in Kenya and Tanzania
Prairie Dog Detection
Spotting small, rare wildlife — prairie dogs and their burrows — in drone imagery, for grassland conservation.
Prairie dogs are a keystone species: the burrow systems they build and maintain support an entire grassland community, including the endangered black-footed ferret that preys on them. Monitoring prairie dog populations traditionally means manually counting thousands of burrows and hundreds of animals in the field — slow, costly, and error-prone. This module pairs high-resolution drone surveys with AI to automate that count.
The detector is built on RareSpot, a framework combining multi-scale consistency learning with context-aware augmentation to find objects that are tiny (~30 px), sparse, and visually indistinct from the terrain. It improves detection accuracy by more than 35% over baseline models and generalizes to other wildlife datasets. Against expert human annotations it reaches roughly 90% accuracy on burrows; prairie dogs themselves — smaller and more easily hidden — remain harder, at around 50%. A survey that once took days or weeks of fieldwork can now be processed in hours. This work is a collaboration between the UCSB Vision Research Lab and the Smithsonian Institution.
The module runs as a three-stage pipeline that turns a raw drone survey into geolocated detections:
Step 1 — Detection
RareSpot locates individual prairie dogs and burrows across the drone frames, using multi-scale feature alignment to recover objects only ~30 pixels wide against homogeneous grassland.
Step 2 — Mosaicing
Overlapping frames are aligned and stitched into a single georeferenced orthomosaic — one continuous, high-resolution view covering roughly 200 acres.
Step 3 — Mapping
Detections are projected onto the orthomosaic and to geographic coordinates, yielding burrow density, colony extent, and relative abundance that can be tracked over time to guide management.
