Where are the Whales: A Human-in-the-loop Detection Method for Identifying Whales in High-resolution Satellite Imagery
- URL: http://arxiv.org/abs/2510.14709v1
- Date: Thu, 16 Oct 2025 14:10:51 GMT
- Title: Where are the Whales: A Human-in-the-loop Detection Method for Identifying Whales in High-resolution Satellite Imagery
- Authors: Caleb Robinson, Kimberly T. Goetz, Christin B. Khan, Meredith Sackett, Kathleen Leonard, Rahul Dodhia, Juan M. Lavista Ferres,
- Abstract summary: We present a semi-automated approach for surfacing possible whale detections in satellite imagery.<n>We use a statistical anomaly detection method that flags spatial outliers, i.e. "interesting points"<n>We achieve recalls of 90.3% to 96.4%, while reducing the area requiring expert inspection by up to 99.8%.
- Score: 6.166882357769285
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Effective monitoring of whale populations is critical for conservation, but traditional survey methods are expensive and difficult to scale. While prior work has shown that whales can be identified in very high-resolution (VHR) satellite imagery, large-scale automated detection remains challenging due to a lack of annotated imagery, variability in image quality and environmental conditions, and the cost of building robust machine learning pipelines over massive remote sensing archives. We present a semi-automated approach for surfacing possible whale detections in VHR imagery using a statistical anomaly detection method that flags spatial outliers, i.e. "interesting points". We pair this detector with a web-based labeling interface designed to enable experts to quickly annotate the interesting points. We evaluate our system on three benchmark scenes with known whale annotations and achieve recalls of 90.3% to 96.4%, while reducing the area requiring expert inspection by up to 99.8% -- from over 1,000 sq km to less than 2 sq km in some cases. Our method does not rely on labeled training data and offers a scalable first step toward future machine-assisted marine mammal monitoring from space. We have open sourced this pipeline at https://github.com/microsoft/whales.
Related papers
- Beluga Whale Detection from Satellite Imagery with Point Labels [8.461883879383517]
This study introduces an automated pipeline for detecting beluga whales and harp seals in VHR satellite imagery.<n>The pipeline leverages point annotations and the Segment Anything Model (SAM) to generate precise bounding box annotations.<n>YOLOv8 trained on SAM-labeled boxes achieved an overall $textF_text1$-score of 72.2% for whales overall and 70.3% for harp seals.
arXiv Detail & Related papers (2025-05-17T16:13:10Z) - Bootstrapping Rare Object Detection in High-Resolution Satellite Imagery [2.242884292006914]
This paper addresses the problem of bootstrapping such a rare object detection task.
We propose novel offline and online cluster-based approaches for sampling patches.
We apply our methods for identifying bomas, or small enclosures for herd animals, in the Serengeti Mara region of Kenya and Tanzania.
arXiv Detail & Related papers (2024-03-05T07:44:13Z) - Whale Detection Enhancement through Synthetic Satellite Images [13.842008598751445]
We show that we can achieve a 15% performance boost on whale detection compared to using the real data alone for training.
We open source the code of the simulation platform SeaDroneSim2 and the dataset generated through it.
arXiv Detail & Related papers (2023-08-15T13:35:29Z) - An Empirical Analysis of Range for 3D Object Detection [70.54345282696138]
We present an empirical analysis of far-field 3D detection using the long-range detection dataset Argoverse 2.0.
Near-field LiDAR measurements are dense and optimally encoded by small voxels, while far-field measurements are sparse and are better encoded with large voxels.
We propose simple techniques to efficiently ensemble models for long-range detection that improve efficiency by 33% and boost accuracy by 3.2% CDS.
arXiv Detail & Related papers (2023-08-08T05:29:26Z) - Far3Det: Towards Far-Field 3D Detection [67.38417186733487]
We focus on the task of far-field 3D detection (Far3Det) of objects beyond a certain distance from an observer.
Far3Det is particularly important for autonomous vehicles (AVs) operating at highway speeds.
We develop a method to find well-annotated scenes from the nuScenes dataset and derive a well-annotated far-field validation set.
We propose a Far3Det evaluation protocol and explore various 3D detection methods for Far3Det.
arXiv Detail & Related papers (2022-11-25T02:07:57Z) - TempNet: Temporal Attention Towards the Detection of Animal Behaviour in
Videos [63.85815474157357]
We propose an efficient computer vision- and deep learning-based method for the detection of biological behaviours in videos.
TempNet uses an encoder bridge and residual blocks to maintain model performance with a two-staged, spatial, then temporal, encoder.
We demonstrate its application to the detection of sablefish (Anoplopoma fimbria) startle events.
arXiv Detail & Related papers (2022-11-17T23:55:12Z) - Deep object detection for waterbird monitoring using aerial imagery [56.1262568293658]
In this work, we present a deep learning pipeline that can be used to precisely detect, count, and monitor waterbirds using aerial imagery collected by a commercial drone.
By utilizing convolutional neural network-based object detectors, we show that we can detect 16 classes of waterbird species that are commonly found in colonial nesting islands along the Texas coast.
arXiv Detail & Related papers (2022-10-10T17:37:56Z) - Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data
for Aerial Animal Surveillance [3.8015092217142223]
We show that a holistic attention network based super-resolution approach and a custom-built altitude data exploitation network can increase the detection efficacy in real-world settings.
We evaluate the system on two public, large aerial-capture animal datasets, SAVMAP and AED.
arXiv Detail & Related papers (2021-11-12T17:30:55Z) - FOVEA: Foveated Image Magnification for Autonomous Navigation [53.69803081925454]
We propose an attentional approach that elastically magnifies certain regions while maintaining a small input canvas.
Our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
On the autonomous driving datasets Argoverse-HD and BDD100K, we show our proposed method boosts the detection AP over standard Faster R-CNN, with and without finetuning.
arXiv Detail & Related papers (2021-08-27T03:07:55Z) - Detecting Cattle and Elk in the Wild from Space [6.810164473908359]
Localizing and counting large ungulates in satellite imagery is an important task for supporting ecological studies.
We propose a baseline method, CowNet, that simultaneously estimates the number of animals in an image (counts) and predicts their location at a pixel level (localizes)
We specifically test the temporal generalization of the resulting models over a large landscape in Point Reyes Seashore, CA.
arXiv Detail & Related papers (2021-06-29T14:35:23Z) - Road Curb Detection and Localization with Monocular Forward-view Vehicle
Camera [74.45649274085447]
We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens.
Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
arXiv Detail & Related papers (2020-02-28T00:24:18Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.