EarthSenseAI Center uses deep‑learning pipelines to automate the analysis of visual and acoustic data from underwater vehicles, camera traps, and hydrophones. Its models rapidly process video, classify species, and identify individual dolphins, delivering standardized annotations that integrate with existing research workflows, helping conservation researchers and agencies obtain actionable insights faster and at lower cost.
Funding
Funding not disclosed
Founders
Product
Problem
Conservation programs generate massive amounts of visual and acoustic data from underwater vehicles, camera traps, and hydrophones, but manual analysis is time‑consuming and requires specialized expertise. This bottleneck limits timely insight into habitat use, species distribution, and ecosystem health.
Solution
EarthSenseAI applies deep learning to automate the analysis of environmental sensor data. For marine monitoring, the platform accelerates video processing from autonomous underwater vehicles and uses acoustic models to identify individual dolphins from hydrophone recordings. In terrestrial settings, it deploys computer‑vision models that rapidly classify species and detect animals in camera‑trap images and video. The resulting annotations are delivered in standardized formats that integrate with existing research workflows, enabling scientists and conservation practitioners to obtain actionable insights faster and at lower cost.
Target Audience
Primary users are marine and terrestrial conservation researchers, NGOs, and government agencies that rely on video, image, and acoustic monitoring to assess wildlife populations and habitat conditions.
Features
- Convolutional neural network pipelines for high‑throughput processing of underwater video streams
- Acoustic deep‑learning models that perform individual-level dolphin identification from hydrophone data
- Camera‑trap image and video classification models trained on diverse terrestrial species
- Automated data labeling and export to common biodiversity data standards (e.g., CSV, JSON, Darwin Core)
- Scalable cloud‑based inference infrastructure that can handle large datasets without local hardware
- Continuous model improvement through active learning loops with user‑provided annotations