Benjamin Kellenberger

Hello, world! I am a Lecturer and permanent researcher in the People and Nature Lab at University College London (UCL), working at the intersection of Earth observation, machine learning, and ecology to answer questions about the distribution of species and underlying processes.
I previously worked in the Jetz lab at Yale University, as well as the ECEO lab at EPFL, Switzerland, where I researched methods to automatically identify animals from above—using aerial imagery.

In a Nutshell

Ecologists have been contributing for decades to our understanding of where individuals of species thrive and where they don't. It turns out that most of these findings about biological nature are things we acknowledge that we don't know—perhaps more so than in any other science field (reference needed). Nonetheless, the process understanding amassed by ecologists is huge and severely underused in current methodologies.

Species distribution map of the Bighorn Sheep (Ovis canadensis).

In my research, I explore ways to integrate such latent knowledge into modelling approaches in new ways, stepping beyond hard and limiting assumptions of existing heuristics.

Digital Wildlife Conservation: What and Why

I am interested in developing and providing solutions for nature and wildlife conservation, particularly using machine learning, computer vision in conjunction with aerial or camera trap imagery. One of my major research directives to this end is the automated and interactive localisation of mammals (elephants, rhino, etc.) in aerial images. Click for more infos!

You can hover over the image to the right to see one basic example product of what machine learning models can deliver.

Wait, what's so hard about this?

Predicting bounding boxes (i.e., object detection) has long been studied in computer vision, sure. However, aerial imagery poses a completely different set of problems. If you believe this to be simple, I would like to encourage you to get a stopwatch, open up this aerial image and measure the time it takes you to find the mammals. Then, think about how hard it is—even for a computer—to do that in hundreds of thousands of such images with a high recall and low error rate...

Why even bother?

Locating animals in aerial imagery serves many purposes, such as:

Censuses: wildlife parks, game reserves, and authorities need to periodically obtain animal counts to monitor the health of their livestock.
Poaching prevention: even to this day, thousands of elephants, rhino, and more are poached every year in Africa. If this continues, much of our great fauna will become extinct. For example, the Black Rhino is about to die out in a few years from now...
Knowing where the animals are and how many there are helps identifying grazing hotspots and sending out anti-poaching patrols. Plus, on a more civilised note, ecologists have great interest in these data to establish links between various ecosystem parameters, and to study the interactions between animals and flora.
Doing all of this using aerial images enables covering larger areas from a better point of view (less occlusions). Doing it automatically means that humans need less time to annotate, the localisation quality will improve, and the costs go down.

How does this work?

As is widespread these days, much of my research uses computer vision and machine learning (including, but not limited to, deep learning) to identify and localise animals. See the publications page if you want to know more.
However, simply developing models and publishing them in papers will not automatically change things for the better. Instead, my work is fuelled by two critical stances:

Machines work best if they assist and collaborate with humans. I oppose full automation of jobs, unless the task is too dangerous or requires immense precision. Computers are good at large-scale, monotonic tasks, so let's offload this portion of the work to them, and leave the final reasoning to humans.
I strongly believe that scientific research needs to be applied and given to the right audience, academic or not, in order to be successful. I wish to see a direct impact of my work, beyond citations.

You may want to check out my latest software projects and publications.

Other things I do

My research interests are highly interdisciplinary and span more general vision, remote sensing and GIS topics. In detail, I also work on domain adaptation, weakly- and self-supervised learning, meta-learning, land cover and land use mapping, and more.
Besides that, I have been frequently involved in teaching (BSc and MSc courses in GIS and machine learning for spatial data), and I administered and maintained the computing infrastructure of my previous research groups, including some GPU servers back in the Netherlands.

News

December 17, 2024

I am co-advertising five PhD projects for 2025!
Projects are distributed across two doctoral training programmes, TREES and AI-INTERVENE, and have a wonderful team of co-supervisors from UCL, Queen Mary University London, and EPFL Switzerland!
Enquire and apply now!

PhD 1: https://tinyurl.com/uav-wildlife
PhD 2: https://tinyurl.com/forest-GNN
PhD 3: https://tinyurl.com/genome-sweep
PhD 4: https://tinyurl.com/interaction-cv
PhD 5: https://tinyurl.com/interaction-map

November 28, 2024

As part of the Map of Life Rapid Assessments team, I am thrilled to announce that we have made second place in the XPRIZE Rainforest Competition this year!
This competition was five years in the making, with the aim of advancing technology for biodiversity and ecosystem monitoring in challenging areas like tropical rainforests. Each of the six finalist teams from around the globe was given 24 hours to acquire data of a predefined patch of Brazilian rainforest. The location was kept secret until a few days before the finals in June; teams were prohibited from setting foot into the area and had to gather data with technology. A subsequent 48 hour window was given to analyse any data and identify as many species as possible.
Our team employed a vast array of technologies, from imaging and acoustic drones to air and water eDNA collectors, and employed a human-in-the-loop scheme with taxon experts to make sense of this data.
As head of the data science team, I was responsible for data pre-processing, prediction (with state-of-the-art deep learning methods), and organisation in the cloud. Our solution worked fast and flawlessly, detecting fine-grained species in aerial images and acoustic recordings, estimated forest functional traits in ultra high-resolution remote sensing data, and provided innovations in all aspects, from 3D data matching to open set species recognition.
More about our efforts at Yale.

August 1, 2024

It is my pleasure to announce that I have officially started my new, permanent position as Lecturer and Researcher in the People and Nature Lab at University College London (UCL)! I will be deepening my research on data science, Machine and Deep Learning, and Earth observation, to answer questions about our natural environment and protection thereof.
Please stay tuned if you are interested in working in these spheres and using technology for good, as I may be hiring soon!
UCL Profile Page

▼ more news

July 15, 2024

Known under Map of Life Rapid Assessments, we are one of the six finalists in the XPRIZE Rainforest competition. Our team just completed the finals, consisting of 24 hours of unmanned data acquisition in a square kilometre patch of rainforest in Brazil, followed by 48 hours of data analysis, with the aim to identify as many non-bacterial species and characterise the ecosystem as completely as possible. Thanks to the tremendous breadth of knowledge in our team, ranging from taxon experts and eDNA researchers to data scientists and software engineers, we were able to set up a technology-empowered and human-inclusive system that worked fabulously, A to Z, and gave us a wealth of species and insights! Winners of the competition will be announced later this year.

September 1, 2022

I will be moving labs! Starting October 1st, I will be joining the Department of Ecology and Evolutionary Biology, the Jetz Lab, at Yale University as a postdoctoral researcher! My focus will be on large-scale habitat suitability mapping with remote sensing and machine learning; so will be contributions to the Map of Life project.

February 9, 2022

New position paper in Nature Communications: Perspectives in machine learning for wildlife conservation
We connect the dots between ecological research on wildlife conservation and automated heuristics from machine learning and computer vision. We not only highlight the potential of machine vision to assist, accelerate, enhance, and upscale ecological research, along with success stories and lessons learnt, but sketch an agenda to foster joint efforts between the involved fields towards a holistic understanding of wildlife behaviour, conservation, and needs.

I am thrilled to have been joint first author on this landmark publication and, more importantly, honoured to have been able to work together with some of the greatest scientists and people imaginable.

Tweets

January 28, 2022

Development of AIDE v3.0 is on its way! Lots and lots of new features are planned. Here's just a sneak peek of some of them:

Photoshop-grade semantic segmentation tools (seriously): tweet
Support for multi- and hyperspectral remote sensing images and formats (ENVI, ERDAS Imagine, etc.): tweet

Development will take more time, but you may want to stay tuned for an exciting release!

April 27, 2021

New paper is out: 21 000 birds in 4.5 h: efficient large-scale seabird detection with machine learning! In this work we tackled the problem of detecting and counting high-density bird colonies off West Africa. Using deep learning-based models and prior knowledge, we were able to accurately detect 21 000 birds in drone imagery, using only 200 annotations per species and a total of 4.5 hours, from unannotated orthomosaic to prediction.

October 28, 2020

Our new paper about AIDE, the machine learning-assisted web annotation interface, is out! It has been published in the British Ecological Society's journal Methods in Ecology and Evolution, and is openly accessible here:
Link to paper
A bit of social media presence about the work: Twitter—YouTube (teaser video)—YouTube (extended video)

September 30, 2020

I have moved labs! From October 1 on, I will officially be an employee of the newly inaugurated Environmental Computational Science and Earth Observation Laboratory (ECEO) at EPFL in Sion, Switzerland! My duties will include all I did in Wageningen (including wildlife detection) and much more. I invite you to visit the web page (still work in progress) of our brand new lab (still in growth).

About me

I am originally from Zurich, Switzerland and also did my BSc and MSc there (University of Zurich). I completed my PhD at Wageningen University, Netherlands, with distinction "cum laude" (PhD thesis). A recording of my PhD defence from April 6, 2020, can be found here for your amusement. I have also worked in the U.S. at Microsoft in summer 2019.

Here's a brief summary of my educational activities:

October 2022 - July 2024	Postdoctoral position at BGC, Yale University, United States.
October 2020 - September 2022	Postdoctoral position at ECEO, École Polytechnique Fédérale de Lausanne, campus Sion, Switzerland.
April 7, 2020 - September 2020	Postdoctoral position at GRS, WUR
May - August 2019	Research Intern at Microsoft in the context of the AI for Earth initiative. Development of the AIDE platform.
Aug 2017 - April 6, 2020	PhD candidate (cont'd) at the Laboratory of Geo-Information Science and Remote Sensing (GRS), Wageningen University (WUR), the Netherlands.
Feb 2016 - Jul 2017	PhD candidate at the Remote Sensing Laboratories, University of Zurich.
2015 - 2016	Intern at the Institute of Cartography and Geoinformation, ETH Zurich. Development of the GeoVITe geodata download portal.
2014 - 2015	Intern at the Federal Office of Topography swisstopo. Development of the Swiss Map Mobile data backbone.
2009 - 2014	BSc and MSc in Geography (Remote Sensing and GIS) and Computer Science at University of Zurich, Switzerland.

Feel free to contact me for a full CV.

People I collaborate with

Just a few of the amazing people I have or had the honour to work with (in no particular order):